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.

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.

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.

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.

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.

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.

Mixing characteristics of a bubble mixing microfluidic chip for genomic DNA extraction based on magnetophoresis: CFD simulation and experiment.

Mixing a small amount of magnetic beads and regents with large volume samples evenly in microcavities of a microfluidic chip is always the key step for the application of microfluidic technology in the field of magnetophoresis analysis. This article proposes a microfluidic chip for DNA extraction by magnetophoresis, which relies on bubble rising to generate turbulence and microvortices of various sizes to mix magnetic beads with samples uniformly. The construction and working principle of the microfluidic chip are introduced. CFD simulations are conducted when magnetic beads and samples are irritated by the generation of gas bubbles with the variation of supply pressures. The whole mixing process in the microfluidic chip is observed through a high-speed camera and a microfluidic system when the gas bubbles are generated continuously. The influence of supply pressure on the mixing characteristics of the microfluidic chip is investigated and discussed with both simulation and experiments. Compared with magnetic mixing, bubble mixing can avoid the magnetic beads gather phenomenon caused by magnetic forces and provide a rapid and high efficient solution to realize mixing small amount of regents in large volume samples in a certain order without complex moving structures and operations in a chip. Two applications of mixing with the proposed microfluidic chip are also carried out and discussed.

Isolation and identification of low-density polyethylene degrading novel bacterial strains.

Plastics are usually made up of low-density polyethylene (LDPE) that serve as the environmental nuisance. The recalcitrant nature of plastics is a huge concern, whereas the increasing demand has made it difficult to handle the plastic waste that eventually leads to plastic pollution. In recent years, due to increasing demand and high pressure for its safe disposal, plastic biodegradation has gained a lot of attention. In the current study, four bacterial strains were isolated from the solid-waste dumpsites of Faisalabad, Pakistan, using enrichment culture technique. The isolated bacterial strains were capable of growing on media having polystyrene as the sole carbon source. Based on 16S rRNA gene sequencing and phylogenetic analysis of the isolated strains Serratia sp., Stenotrophomonas sp. and Pseudomonas sp. were identified as the potential strains for the biodegradation of LDPE. Serratia sp. resulted in 40% weight loss of the LDPE plastic pieces after 150 days of treatment. Stenotrophomonas sp. and Pseudomonas species resulted in 32 and 21% weight loss of the treated piece of plastics (LDPE), respectively. Polyethylene pieces were characterized by Fourier-transform infrared spectroscopy (FTIR) analysis before and after biodegradation. The FTIR spectra indicated that the isolated bacterial strains have a good potential to degrade LDPE. Future studies are required to investigate the bacterial genetic makeup, mechanisms of LDPE biodegradation and the factors that can enhance the biodegradable characteristics of these indigenously isolated bacterial strains.

Microemulsified Gel Formulations for Topical Delivery of Clotrimazole: Structural and In Vitro Evaluation.

Microemulsified gels (µEGs) with fascinating functions have become indispensable as topical drug delivery systems due to their structural flexibility, high stability, and facile manufacturing process. Topical administration is an attractive alternative to traditional methods because of advantages such as noninvasive administration, bypassing first-pass metabolism, and improving patient compliance. In this article, we report on the new formulations of microemulsion-based gels suitable for topical pharmaceutical applications using biocompatible and ecological ingredients. For this, two biocompatible µE formulations comprising clove oil/Brij-35/water/ethanol (formulation A) and clove oil/Brij-35/water/1-propanol (formulation B) were developed to encapsulate and improve the load of an antimycotic drug, Clotrimazole (CTZ), and further gelatinized to control the release of CTZ through skin barriers. By delimiting the pseudo-ternary phase diagram, optimum µE formulations with clove oil (∼15%) and Brij-35 (∼30%) were developed, keeping constant surfactant/co-surfactant ratio (1:1), to upheld 2.0 wt % CTZ. The as-developed formulations were further converted into smart gels by adding 2.0 wt % carboxymethyl cellulose (CMC) as a cross-linker to adhere to the controlled release of CTZ through complex skin barriers. Electron micrographs show a fine, monodispersed collection of CTZ-µE nanodroplets (∼60 nm), which did not coalesce even after gelation, forming spherical CTZ-µEG (∼90 nm). However, the maturity of CTZ nanodroplets observed by dynamic light scattering suggests the affinity of CTZ for the nonpolar microenvironment, which was further supported by the peak-to-peak correlation of Fourier transform infrared (FTIR) analysis and fluorescence measurement. In addition, HPLC analysis showed that the in vitro permeation release of CTZ-µEG from rabbit skin in the ethanolic phosphate buffer (pH = 7.4) was significantly increased by >98% within 6.0 h. This indicates the sustained release of CTZ in µEBG and the improvement in transdermal therapeutic efficacy of CTZ over its traditional topical formulations.

COVID-19 and climatic factors: A global analysis.

BACKGROUND: It is unknown if COVID-19 will exhibit seasonal pattern as other diseases e.g., seasonal influenza. Similarly, some environmental factors (e.g., temperature, humidity) have been shown to be associated with transmission of SARS-CoV and MERS-CoV, but global data on their association with COVID-19 are scarce. OBJECTIVE: To examine the association between climatic factors and COVID-19. METHODS: We used multilevel mixed-effects (two-level random-intercepts) negative binomial regression models to examine the association between 7- and 14-day-lagged temperature, humidity (relative and absolute), wind speed and UV index and COVID-19 cases, adjusting for Gross Domestic Products, Global Health Security Index, cloud cover (%), precipitation (mm), sea-level air-pressure (mb), and daytime length. The effects estimates are reported as adjusted rate ratio (aRR) and their corresponding 95% confidence interval (CI). RESULTS: Data from 206 countries/regions (until April 20, 2020) with ≥100 reported cases showed no association between COVID-19 cases and 7-day-lagged temperature, relative humidity, UV index, and wind speed, after adjusting for potential confounders, but a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed. Compared to an absolute humidity of <5 g/m3, an absolute humidity of 5-10 g/m3 was associated with a 23% (95% CI: 6-42%) higher rate of COVID-19 cases, while absolute humidity >10 g/m3 did not have a significant effect. These findings were robust in the 14-day-lagged analysis. CONCLUSION: Our results of higher COVID-19 cases (through April 20) at absolute humidity of 5-10 g/m3 may be suggestive of a 'sweet point' for viral transmission, however only controlled laboratory experiments can decisively prove it.