Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Formulation of Microemulsion-Based Gels for Enhanced Topical Administration of Nonsteroidal Anti-Inflammatory Drugs.

Nonsteroidal anti-inflammatory drugs are commonly administered orally to manage pain and inflammation, but they can have negative gastrointestinal side effects. Topical delivery is an alternative, and microemulsions (µEs) have been shown to be effective in facilitating, but they suffer from a liquid nature and low long-term retention on the skin. Hence, microemulsified gels (µEGs) have been developed, and in this study, we explored certain µEGs with diclofenac sodium (DF-Na) and naproxen sodium (NP-Na) with the hypothesis to ensure a slower and more sustained delivery of NSAIDs through the skin. The µEGs comprised castor oil (∼8%), water (∼12%), Tween-20 (∼72%), Span-20 (∼8%), poloxamer 407, and DF-Na or NP-Na. Optical microscopy was used to study the microstructures in the µEs and µEGs, and phase transitions from water-in-oil (w/o) to oil-in-water (o/w) with continuous networks were observed. Based on studies with dynamic light scattering and analyses of electron micrographs, it was observed that the µEs and µEGs loaded with DF-Na and NP-Na comprised monomodal nanodroplets. The average sizes of the droplets were (∼35 nm) and (∼60 nm) for the µEGs, without and with drugs. Fluorescence spectroscopy was used to ensure that the drugs were more likely to be present in the hydrophobic microenvironment of the formulations. Moreover, ex vivo permeation studies were conducted at pH values of 5.5 and 7.4 across rabbit skin. The release rates of DF-Na (>99 ± 1.5%, P < 0.07) and NP-Na (>89 ± 1.1%, P < 0.01) were slower for the µEGs within 8-10 h than for the µEs at the low pH, which is of relevance to the optimal pH of the skin. It was observed that µEGs with high viscosities are effective and may have potential for use in topical drug delivery applications.

The Acute Impact of Propofol on Blood-Brain Barrier Integrity in Mice.

PURPOSE: We investigated whether short term infusion of propofol, a highly lipophilic agonist at GABAA receptors, which is in widespread clinical use as anesthetic and sedative, affects passive blood-brain barrier (BBB) permeability in vivo. METHODS: Mice were anesthetized with an intraperitoneal injection of ketamine/xylazine followed by a continuous IV infusion of propofol in lipid emulsion through a tail vein catheter. Control groups received ketamine/xylazine anesthesia and an infusion of Intralipid, or ketamine/xylazine anesthesia only. [13C12]sucrose as a permeability marker was injected as IV bolus 15 min after start of the infusions. Brain uptake clearance, Kin, of sucrose was calculated from the brain concentrations at 30 min and the area under the plasma-concentration time curve. We also measured the plasma and brain concentration of propofol at the terminal time point. RESULTS: The Kin value for propofol-infused mice was significantly higher, by a factor of 1.55 and 1.87, compared to the Intralipid infusion and the ketamine/xylazine groups, respectively, while the control groups were not significantly different. No difference was seen in the expression levels of tight junction proteins in brain across all groups. The propofol plasma concentration at the end of infusion (10.7 µM) matched the clinically relevant range of blood concentrations reported in humans, while concentration in brain was 2.5-fold higher than plasma. CONCLUSIONS: Propofol at clinical plasma concentrations acutely increases BBB permeability, extending our previous results with volatile anesthetics to a lipophilic injectable agent. This prompts further exploration, potentially refining clinical practices and ensuring safety, especially during extended propofol infusion schemes.

Divergent Fate and Roles of Dissolved Organic Matter from Spatially Varied Grassland Soils in China During Long-Term Biogeochemical Processes.

Terrestrial dissolved organic matter (DOM) is critical to global carbon and nutrient cycling, climate change, and human health. However, how the spatial and compositional differences of soil DOM affect its dynamics and fate in water during the carbon cycle is largely unclear. Herein, the biodegradation of DOM from 14 spatially distributed grassland soils in China with diverse organic composition was investigated by 165 days of incubation experiments. The results showed that although the high humified fraction (high-HS) regions were featured by high humic-like fractions of 4-25 kDa molecular weight, especially the abundant condensed aromatics and tannins, they unexpectedly displayed greater DOM degradation during 45-165 days. In contrast, the unique proteinaceous and 25-100 kDa fractions enriched in the low humified fraction (low-HS) regions were drastically depleted and improved the decay of bulk DOM but only during 0-45 days. Together, DOM from the high-HS regions would cause lower CO2 outgassing to the atmosphere but higher organic loads for drinking water production in the short term than that from the low-HS regions. However, this would be reversed for the two regions during the long-term transformation processes. These findings highlight the importance of spatial and temporal variability of DOM biogeochemistry to mitigate the negative impacts of grassland soil DOM on climate, waters, and humans.

Polygonum bistorta Linn. as a green source for synthesis of biocompatible selenium nanoparticles with potent antimicrobial and antioxidant properties.

Here, we report for the first time, green-synthesized selenium nanoparticles (SeNPs) using pharmacologically potent herb of Polygonum bistorta Linn. for multiple biomedical applications. In the study, a facile and an eco-friendly approach is utilized for synthesis of SeNPs using an aqueous roots extract of P. bistorta Linn. followed by extensive characterization via Fourier transform infrared spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Energy Dispersive X-Ray (EDX) analysis. The XRD and FTIR data determine the phase composition and successful capping of plant extract onto the surface of NPs while SEM and TEM micrographic examination reveals the elliptical and spherical morphology of the particles with a mean size of 69 ± 23 nm. After comprehensive characterization, the NPs are investigated for antifungal, antibacterial, antileishmanial, antioxidant, and biocompatibility properties. The study reveals that Polygonum bistorta Linn. synthesized SeNPs exhibit significant antibacterial and antifungal activities with Staphylococcus aureus and Fusarium oxysporum inducing the highest zone of inhibition of 14 ± 1.0 mm and 20 ± 1.2 mm, respectively at the concentration of 40 mg/mL. The NPs are also found to have antiparasitic potential against promastigote and amastigote forms of Leishmania tropica. Furthermore, the NPs are discovered to have excellent potential in neutralizing harmful free radicals thus exhibiting considerable antioxidant potential. Most importantly, Polygonum bistorta Linn. synthesized SeNPs showed substantial compatibility against blood cells in vitro studies, which signifies the nontoxic nature of the NPs. The study thus concludes that medicinally important Polygonum bistorta Linn. roots can be utilized as an eco-friendly, sustainable, and green source for the synthesis of pharmacologically potent selenium nanoparticles.

Quality, Usability, and Trust Challenges to Effective Data Use in the Deployment and Use of the Bangladesh Nutrition Information System Dashboard: Qualitative Study.

BACKGROUND: Evidence-based decision-making is essential to improve public health benefits and resources, especially in low- and middle-income countries (LMICs), but the mechanisms of its implementation remain less straightforward. The availability of high-quality, reliable, and sufficient data in LMICs can be challenging due to issues such as a lack of human resource capacity and weak digital infrastructure, among others. Health information systems (HISs) have been critical for aggregating and integrating health-related data from different sources to support evidence-based decision-making. Nutrition information systems (NISs), which are nutrition-focused HISs, collect and report on nutrition-related indicators to improve issues related to malnutrition and food security-and can assist in improving populations' nutritional statuses and the integration of nutrition programming into routine health services. Data visualization tools (DVTs) such as dashboards have been recommended to support evidence-based decision-making, leveraging data from HISs or NISs. The use of such DVTs to support decision-making has largely been unexplored within LMIC contexts. In Bangladesh, the Mukto dashboard was developed to display and visualize nutrition-related performance indicators at the national and subnational levels. However, despite this effort, the current use of nutrition data to guide priorities and decisions remains relatively nascent and underused. OBJECTIVE: The goal of this study is to better understand how Bangladesh's NIS, including the Mukto dashboard, has been used and areas for improvement to facilitate its use for evidence-based decision-making toward ameliorating nutrition-related service delivery and the health status of communities in Bangladesh. METHODS: Primary data collection was conducted through qualitative semistructured interviews with key policy-level stakeholders (n=24). Key informants were identified through purposive sampling and were asked questions about the experiences and challenges with the NIS and related nutrition dashboards. RESULTS: Main themes such as trust, data usability, personal power, and data use for decision-making emerged from the data. Trust in both data collection and quality was lacking among many stakeholders. Poor data usability stemmed from unstandardized indicators, irregular data collection, and differences between rural and urban data. Insufficient personal power and staff training coupled with infrastructural challenges can negatively affect data at the input stage. While stakeholders understood and expressed the importance of evidence-based decision-making, ultimately, they noted that the data were not being used to their maximum potential. CONCLUSIONS: Leveraging DVTs can improve the use of data for evidence-based decision-making, but decision makers must trust that the data are believable, credible, timely, and responsive. The results support the significance of a tailored data ecosystem, which has not reached its full potential in Bangladesh. Recommendations to reach this potential include ensuring a clear intended user base and accountable stakeholders are present. Systems should also have the capacity to ensure data credibility and support ongoing personal power requirements.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

Isolation and characterization of novel cadmium-resistant Escherichia fergusonii ZSF-15 from industrial effluent for flocculant production and antioxidant enzyme activity.

Cadmium (Cd) is a highly toxic metal that frequently contaminates our environment. In this study, the bioflocculant-producing, cadmium-resistant Escherichia fergusonii ZSF-15 was characterized from Paharang drain, Bawa Chak, Faisalabad, Pakistan. The Cd-resistant E. fergusonii was used to determine the bioflocculant production using yeast-peptone-glycerol medium (pH 6.5) supplemented with 50 mg L-1 of Cd. The culture was incubated for 3 days at 37 °C in a rotary shaker at 120 rpm. The fermentation broth was centrifuged at 4000 g for 10 min after the incubation period. The maximum flocculating activity by isolate ZSF-15 was found to be 71.4% after 48 h of incubation. According to the Fourier transform infrared spectroscopy analysis, the bioflocculant produced by strain ZSF-15 was comprised of typical polysaccharide and protein, i.e. hydroxyl, carboxyl, and amino groups. The strain ZSF-15 exhibited bioflocculant activity at range of pH (6-8) and temperature (35-50℃). Maximum flocculation activity (i.e. 71%) was observed at 47℃, whereas 63% flocculation production was observed at pH 8. In the present study, antioxidant enzyme profile of ZSF-15 was also evaluated under cadmium stress. A significant increase in antioxidant enzymes including superoxide dismutase (118%) and ascorbate peroxidase (28%) was observed, whereas contents of catalase (86%), glutathione transferase (13%), and peroxidase (8%) were decreased as compared to control.

Effects of Volatile Anesthetics versus Ketamine on Blood-Brain Barrier Permeability via Lipid-Mediated Alterations of Endothelial Cell Membranes.

The purpose of this study was to investigate the effects of the volatile anesthetic agents isoflurane and sevoflurane, at clinically relevant concentrations, on the fluidity of lipid membranes and permeability of the blood-brain barrier (BBB). We analyzed the in vitro effects of isoflurane or ketamine using erythrocyte ghosts (sodium fluorescein permeability), monolayers of brain microvascular endothelial cells ([13C]sucrose and fluorescein permeability), or liposomes (fluorescence anisotropy). Additionally, we determined the effects of 30-minute exposure of mice to isoflurane on the brain tight junction proteins. Finally, we investigated in vivo brain uptake of [13C]mannitol and [13C]sucrose after intravenous administration in mice under anesthesia with isoflurane, sevoflurane, or ketamine/xylazine in addition to the awake condition. Isoflurane at 1-mM and 5-mM concentrations increased fluorescein efflux from the erythrocyte ghosts in a concentration-dependent manner. Similarly, in endothelial cell monolayers exposed to 3% (v/v) isoflurane, permeability coefficients rose by about 25% for fluorescein and 40% for [13C]sucrose, whereas transendothelial resistance and cell viability remained unaffected. Although isoflurane caused a significant decrease in liposomes anisotropy values, ketamine/xylazine did not show any effects. Brain uptake clearance (apparent Kin) of the passive permeability markers in vivo in mice approximately doubled under isoflurane or sevoflurane anesthesia compared with either ketamine/xylazine anesthesia or the awake condition. In vivo exposure of mice to isoflurane did not change any of the brain tight junction proteins. Our data support membrane permeabilization rather than loosening of intercellular tight junctions as an underlying mechanism for increased permeability of the endothelial cell monolayers and the BBB in vivo. SIGNIFICANCE STATEMENT: The blood-brain barrier controls the entry of endogenous substances and xenobiotics from the circulation into the central nervous system. Volatile anesthetic agents like isoflurane alter the lipid structure of cell membranes, transiently facilitating the brain uptake of otherwise poorly permeable, hydrophilic small molecules. Clinical implications may arise when potentially neurotoxic drugs gain enhanced access to the central nervous system under inhalational anesthetics.

A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time-frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN.

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

Nanotechnologies for environmental remediation and their ecotoxicological impacts.

Environmental nanoremediation is an emerging technology that aims to rapidly and efficiently remove contaminants from the polluted sites using engineered nanomaterials (ENMs). Inorganic nanoparticles which are generally metallic, silica-based, carbon-based, or polymeric in nature serve to remediate through chemical reactions, filtration, or adsorption. Their greater surface area per unit mass and high reactivity enable them to treat groundwater, wastewater, oilfields, and toxic industrial contaminants. Despite the growing interest in nanotechnological solutions for bioremediation, the environmental and human hazard associated with their use is raising concerns globally. Nanoremediation techniques when compared to conventional remediation solutions show increased effectivity in terms of cost and time; however, the main challenge is the ability of ENMs to remove contaminants from different environmental mediums by safeguarding the ecosystem. ENMs improving the accretion of the pollutant and increasing their bioavailability should be rectified along with the vigilant management of their transfer to the upper levels of the food chain which subsequently causes biomagnification. The ecosystem-centered approach will help monitor the ecotoxicological impacts of nanoremediation considering the safety, sustainability, and proper disposal of ENMs. The environment and human health risk assessment of each novel engineered nanomaterial along with the regulation of life cycle assessment (LCA) tools of ENMs for nanoremediation can help investigate the possible environmental hazard. This review focuses on the currently available nanotechnological methods used for environmental remediation and their potential toxicological impacts on the ecosystem.

The effect of Dapagliflozin on renal functions in hospitalized patients with Acute Heart Failure.

Objective: To evaluate the influence of dapagliflozin on renal functions and diuretics use in patients with acute heart failure (AHF). Methods: This comparative analytical study was conducted at Armed Forces Institute of Cardiology, Rawalpindi from July 2022 to November 2022. Patients were distributed equally in two groups i.e. Dapagliflozin and Conventional Groups, where patients received dapagliflozin added to conventional therapy for AHF and, only conventional therapy for AHF respectively. Estimated glomerular filtration rate (eGFR), serum creatinine were measured and compared on admission, after 48 hours and on discharge. Weight loss during hospitalization, daily dose of furosemide and length of hospital stay was also recorded. Quantitative parameters were analyzed using t-test or Mann Whitney U test accordingly. Results: There were no significant baseline differences in renal functions. A modest decline in eGFR was observed in both groups after 48 hours. However, the variation in values of eGFR remained similar among both groups after 48 hours (p-value 0.365) and on discharge (p-value 0.768). Whereas, patients subjected to dapagliflozin treatment exhibited a more profound diuretic response expressed as greater weight loss (p-value < 0.001), achieved at comparatively lower doses of loop diuretics. Moreover, they also had a shorter duration of hospital stay (six vs eight days, p-value <0.001). Conclusion: Institution of dapagliflozin did not cause any significant deterioration of renal functions, whereas; it was associated with improved diuretic response as depicted by more pronounced weight loss at comparatively lower doses of loop diuretics.

Influence of Temperature on Biofilm Formation Mechanisms Using a Gravity-Driven Membrane (GDM) System: Insights from Microbial Community Structures and Metabolomics.

A biofilm has a significant effect on water treatment processes. Currently, there is a lack of knowledge about the effect of temperature on the biofilm structure in water treatment processes. In this study, a gravity-driven membrane ultrafiltration system was operated with river feedwater at two temperatures ("low", 4 °C; "high", 25 °C) to explore the biofilm structure and transformation mechanism. The results showed that the difference in dissolved oxygen concentration might be one of the main factors regulating the structural components of the biofilm. A denser biofilm formation and reduced flux were observed at the lower temperature. The linoleic acid metabolism was significantly inhibited at low temperature, resulting in enhanced pyrimidine metabolism by Na+ accumulation. In addition, the biofilm at low temperature had a higher proportion of the metabolites of lipids and lipid-like molecules (11.25%), organic acids and derivatives (10.83%), nucleosides, nucleotides, and analogues (7.083%), and organoheterocyclic compounds (6.66%). These small molecules secrete more polysaccharides having C═O and O═C-O functional groups, which intensified the resistance of the biofilm. Furthermore, the upregulation pathway of pyrimidine metabolism also increased the risk of urea accumulation at low temperature. Limnohabitans, Deinococcus, Diaphorobacter, Flavobacterium, and Pseudomonas were identified as the principal microorganisms involved in this metabolic transformation.

Combined efficacy of Cinnamomum zeylanicum and doxorubicin against leukemia through regulation of TRAIL and NF-kappa B pathways in rat model.

BACKGROUND: Recent discoveries in cancer therapeutics have proven combination therapies more effective than individual drugs. This study describes the efficacy of the combination of Cinnamomum zeylanicum and doxorubicin against benzene-induced leukemia. METHODS AND RESULTS: Brine shrimp assay was used to assess the cytotoxicity of C. zeylanicum, doxorubicin and their combination. After AML induction in Sprague Dawley rats, the same drugs were given to rat groups. Changes in organ weight, haematological profile, and hepatic enzymes were determined. Real-time PCR was used to elucidate the effect on the expression of STMN1, GAPDH, P53 and various TRAIL and NF-kappaB components. C. zeylanicum reduced the cytotoxicity of doxorubicin. The combination treatment showed better anti-leukemic results than any of the individual drugs as evident from STMN1 expression (p < 0.001). It was particularly effective in reducing total white blood cell counts and recovering lymphocytes, monocytes and eosinophils along with hepatic enzymes ALT and AST (p < 0.001). All doses recovered relative organ weights and improved blood parameters. The combination therapy was particularly effective in inducing apoptosis, inhibition of proliferation marker GAPDH (p < 0.001) and NF-kappaB pathway components Rel-A (p < 0.001) and Rel-B (p < 0.01). Expressions of TRAIL components c-FLIP (p < 0.001), TRAIL ligand (p < 0.001) and caspase 8 (p < 0.01) were also altered. CONCLUSION: Cinnamomum zeylanicum in combination with doxorubicin helps to counter benzene-induced cellular and hepatic toxicity and improves haematological profile. The anti-leukemic effects are potentially due to inhibition of GAPDH and NF-kappa B pathway, and through regulation of TRAIL pathway. Our data suggests the use of C. zeylanicum with doxorubicin to improve anti-leukemic therapeutic regimes.

Dosimetric properties of thermoluminescent NaCl pellets from Khewra Salt Mines, Pakistan.

Thermoluminescence (TL) and extended dosimetric characteristics of naturally occurring sodium chloride (NaCl) salt were studied. Pellets were prepared from mined crystalline salt obtained from Khewra Salt Mines, Pakistan and irradiated from 1 mGy to 10,000 mGy using cobalt-60 gamma source. The TL response showed two dominant peaks around 125°C and 230°C, respectively, at low doses, with an additional peak in between at doses beyond 300 mGy. A linear and supra-linear TL response was observed between 1 mGy-100 mGy and 100 mGy-10 Gy dose ranges, respectively. During the first 24 h post-irradiation, the TL intensity dropped by 20%. A maximum angular dependence of up to 50% was observed between 0° to 360°. For photon energies between 33 keV and 1.25 MeV significant energy dependence was observed for photons < 100 keV only. Sample sensitivity increased with dose a qualitatively similar behaviour to TLD-200. Effective atomic number (Zeff ) of the sample (14.6) was comparable to TLD-200 (16.3). No significant dose rate effects (deviation for a cobalt-60 source within 3.5%) on the TL sensitivity of the sample were found. The lowest detectable dose limit (LDDL) for salt sample was found to be 0.8 mGy whereas the sample reproducibility test showed a maximum of ±11% deviation from the first value.

Ecofriendly phytosynthesized zirconium oxide nanoparticles as antibiofilm and quorum quenching agents against Acinetobacter baumannii.

The worldwide increase of multi-drug resistance has directed the researchers to focus on ecofriendly ways of nanoparticles synthesis with effective antivirulence properties. Here, we report the antibiofilm and quorum quenching (QQ) potential of zirconium oxide nanoparticles (ZrO2 NPs) synthesized from aqueous ginger extract against multi-drug resistant (MDR) Acinetobacter baumannii. The results indicated that ZrO2 NPs were of tetragonal shape with average diameter of 16 nm. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values for A. baumannii were 15.6 and 62.5 µg/ml, respectively, as revealed by broth microdilution assay. Exposure of bacterial cells to ZrO2 NPs resulted in reactive oxygen species (ROS) generation which in turn led to cellular membrane disruption as observed by an increase in leakage of cellular contents, such as proteins, sugars, and DNA. The antibiofilm activity was evaluated by microtiter plate assay and the results revealed that the percentage inhibition of biofilm was found to be 14.3-80.6%. ZrO2 NPs also obstructed the chemical composition of biofilms matrix by reducing the proteins and carbohydrate contents. Molecular docking studies of ZrO2 NPs with four proteins (2NAZ, 4HKG, 5D6H, and 5HM6) involved in biofilm formation of A. baumannii revealed the interaction of zirconium with target proteins. These findings suggested the in vitro efficacy of phytosynthesized ZrO2 NPs as antibiofilm and QQ agents that can be exploited in the development of alternative therapeutic options against MDR A. baumannii.

Fabrication of moxifloxacin HCl-loaded biodegradable chitosan nanoparticles for potential antibacterial and accelerated cutaneous wound healing efficacy.

AIM: This work aims to formulate topical hybrid gel containing chitosan-coated moxifloxacin (MXF) HCl nanoparticles (NPs) with enhanced antibacterial and healing activity. METHODS: MXF HCl NPs prepared by the ionic gelation method were loaded onto a hybrid chitosan carbomer gel. Size analysis of the prepared NPs was performed using SEM and Zeta-sizer. Further characterisation was done using Fourier transforms infra-red spectroscopy (FTIR), X-ray diffraction (XRD), and Thermogravimetric analysis (TGA). Prepared gel was evaluated for its in vitro drug release, biocompatibility, antibacterial activity, and stability studies under storage conditions. In-vivo wound healing was measured by observing percentage reduction in wound. RESULTS: NPs have 359 ± 79 nm mean particle size, 31.01 mV zeta potential with 0.008 polydispersity index (PD1), 63.5% drug entrapment and 83 ± 3.5% drug release at pH 5.5. Hybrid chitosan carbomer gel showed good biocompatibility, antibacterial, in-vivo wound healing properties and stable properties. CONCLUSIONS: NP-loaded hybrid gel can be an effective treatment for acute and challenged topical wounds.

Polyphenolic Composition, Antioxidant, Antiproliferative and Antidiabetic Activities of Coronopus didymus Leaf Extracts.

Coronopus didymus (Brassicaceae) commonly known as lesser swine cress has been reported to be used for its pharmacological activities. This study aimed to evaluate the medicinal potential of C. didymus extracts against cancer, diabetes, infectious bacteria and oxidative stress and the identification of bioactive compounds present in these extracts. The effects of using different solvents for the extraction of C. didymus on the contents of major polyphenols and biological activities were investigated. Plant sample was shade dried, ground to a fine powder, and then soaked in pure acetone, ethanol and methanol. The highest contents of major polyphenols were found in methanol-based extract, i.e., chlorogenic acid, HB acid, kaempferol, ferulic acid, quercetin and benzoic acid with 305.02, 12.42, 11.5, 23.33, 975.7 and 428 mg/g of dry weight, respectively, followed by ethanol- and acetone-based extracts. The methanol-based extract also resulted in the highest antioxidant activities (56.76%), whereas the highest antiproliferative (76.36) and alpha glucosidase inhabitation (96.65) were demonstrated in ethanol-based extracts. No antibacterial property of C. didymus was observed against all the tested strains of bacteria. Further studies should be focused on the identification of specific bioactive compounds responsible for pharmacological activities.