Irregular Size Research Articles

FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics.

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST.

Structural, electronic and optical properties of SrMnO3 photocatalyst for dye degradation; experimental and DFT study

Perovskite oxides (PO) are versatile multi-functional materials with wide range of applications. In current study, highly-crystalline perovskite manganite (PM) SrMnO3 has been synthesized via ultrasonic method followed by calcination at 600 °C. Morphology of PM with irregular shape and size was assessed by FESEM and hexagonal geometry with a crystallite size of 13.67nm was determined by XRD studies. As-synthesized PM was further tested as photocatalyst for its dye degradation potential against malachite green (MG) and methylene blue (MB). In the presence of synthesized perovskites, degradation of both the dyes MG and MB was achieved in 12min and 4.5min with the percentage degradation of 80.26% and 89.35% respectively. The factors including pH and temperature affecting degradation of both the dyes were also studied. Additionally, calculations based on simulated DFT (density function theory) have been employed to explore the optical, electronic and structural attributes of SrMnO3 PM through generalized gradient approximation (GGA). The total and partial density of states have been evaluated by GGA confirms the Fermi level (Ef) characteristics of metallic Sr and Mn and found that ‘d’ and ‘p’ orbitals of Sr, Mn and O were responsible for the band formation. The results of the current study advocate the synthesis of PM and related compounds followed by their successful applications as catalyst for dye degradation as well as many other fields involving the use of nanomaterials.

Efficacy of drugs treatment in patients with endometrial hyperplasia with or without atypia: A systematic review and network meta-analysis.

Endometrial hyperplasia (EH) is a hyperplastic endometrial lesion with irregular gland size, increased glands, and increased glandular interstitial ratio. During follow-up, some EH progressed further to endometrial cancer. It is crucial to provide timely treatment for EH and improve the overall prognosis of EH patients. We searched the PubMed, ClinicalTrials.gov., and Embase databases for studies published from their inception to March 31, 2023. The methodological quality of each study was evaluated in accordance with the Cochrane Collaboration's tool for assessing the risk of bias. The RevMan5.3 software provided by the Cochrane Collaboration was used for direct meta-analysis statistical analysis; and the relative risk and 95% confidence interval along with the mean difference and 95% confidence interval, were used as evaluation indexes. We included 21 randomized controlled trials involving a total of 2276 women with EH, 6 studies were of high quality, and 15 were of moderate quality. The blinding of subjects and intervention providers was identified as the main source of potential bias. Six interventions were addressed in the network meta-analysis: medroxyprogesterone acetate (MPA), plus metformin, norethisterone (NET), levonorgestrel-releasing intrauterine system (LNG-IUD), megestrol acetate, and other drugs. In the direct meta-analysis, we found the probability of endometrial complete regression (CR) in the LNG-IUD group to be significantly higher than those in the NET. In the network meta-analysis, we found the probability of CR in the NET group to be significantly lower than those in the MPA and plus metformin groups, the probability of CR in the LNG-IUD group to be significantly higher than those in the NET, the probability of CR in the other drugs group to be significantly higher than those in the LNG-IUD. The NET group had the lowest incidences of endometrial complete regression, plus metformin could have a better outcome. According to the 21 randomized controlled trials included in this study, MPA is the most effective for EH endometrial outcome when applied as a single agent, while the combination of metformin can achieve a more significant effect.

Prototyping Ti2Cu intermetallic grain growth heterogeneously in Ti6Al4V matrix through laser additive manufacturing

The Ti2Cu intermetallics compound possessing an ability to release Cu2+ ions has promising antibacterial properties. This research integrates experimental and computational techniques to delve into the interfacial intermetallic (IMC) formation within the Ti6Al4V/Cu system. Heat transfer analysis is performed to numerically outline the thermal history within the microstructure during laser surface alloying (power = 1000 W and scan speed = 200 mm/s), while the phase field method is employed to describe the Ti2Cu IMC grain growth in the Ti6Al4V matrix. As the IMC and the matrix phases respectively have free energy minima of -68.52 and -61.35 kJ/mol at Taverage=1250.0 K, the growth of Ti2Cu IMC grains at the Ti6Al4V matrix located around the depth of 5 μm from the top surface is thermodynamically favorable. Laser processing can cause the nucleation of IMC precipitates of irregular sizes and curvature profiles, in which case the curvature driven effects between the adjacent grains can additionally affect their growth kinetics. Furthermore, microbiological assays underscore the superior antibacterial efficacy of the Ti6Al4V+Ti2Cu heterostructures as compared to the control (Ti6Al4V) counterpart.

A Lightweight Real-Time Recognition Algorithm for Tomato Leaf Disease Based on Improved YOLOv8

To address the real-time detection challenge of deploying deep learning-based tomato leaf disease detection algorithms on embedded devices, an improved tomato leaf disease detection algorithm based on YOLOv8n is proposed in this paper. It is able to achieve the efficient, real-time detection of tomato leaf diseases while maintaining model’s lightweight requirements. The algorithm incorporated the LMSM (lightweight multi-scale module) and ALSA (Attention Lightweight Subsampling Module) to improve the ability to extract lightweight and multi-scale semantic information for the specific characteristics of tomato leaf disease, which include irregular spot size and lush tomato leaves. The head network was redesigned utilizing partial and group convolution along with a parameter-sharing method. Scalable auxiliary bounding box and loss function optimization strategies were introduced to further enhance performance. After undergoing the pruning technique, computation decreased by 61.7%, the model size decreased by 55.6%, and the FPS increased by 44.8%, all while a high level of accuracy was maintained. A detection speed of 19.70FPS on the Jetson Nano was obtained after undergoing TensorRT quantization, showing a 64.85% improvement compared to the initial detection speed. This method met the high real-time performance and small model size requirements for embedded tomato leaf disease detection systems, indirectly reducing the energy consumption of online detection. It provided an effective solution for the online detection of tomato leaf disease.

The structural, magnetic and electrical properties of chromium doped calcium ferrite nanoparticles

Calcium ferrite nanoparticles doped with Chromium (10-50 mol %) are synthesized using the solution combustion method, employing citrus Lemon extract as a reducing agent, followed by a calcination process at 500oC. Various characterization techniques are employed on the calcined samples. The Bragg reflections resulting from Chromium doping confirm the formation of a singular orthorhombic calcium ferrite phase. Crystallite sizes determined using both Scherrer’s and W-H plot methods found to be decreases with increase in dopant concentration. The surface morphology showcases agglomerated nanoparticles with irregular shapes and sizes, accompanied by pores and voids. The energy band gap found to be increases with increase in dopant concentration from 2.82 to 2.93 eV. The hysteresis loop analysis provides magnetic parameters including saturation magnetization (Ms), remanence (Mr), and coercivity (Hc). As the dopant concentration increases, Ms and Hc found to be maximum at 30 mol% cr3+ concentration in CaFe2O4 NPs. Linear increase in frequency-dependent conductivity at lower frequencies was observed. The presence of semicircles at low frequencies signifies compliance with the Cole-Cole formula for impedance behavior. Additionally, a detailed discussion on dielectric properties is presented. Notably, the dielectric constant decreases from 4.2 to 2.74 with an increase in dopant concentration. These distinctive attributes position the samples as suitable candidates for memory devices as well as high-frequency device applications.

Green photoluminescence, supercapacitor and cytotoxic properties of nickel doped haematite nanoparticles

Fe2O3: Ni (1–9 mol.%) nanoparticles (NPs) were synthesized using the co-precipitation method and calcined at 500∘C for 12 h. The crystallite size, phase, crystallinity, and structural parameters were analyzed via powder X-ray diffraction. The Bragg reflections confirmed that the synthesized NPs crystallize in a pure hexagonal crystal structure with space group R-3c. Surface morphology analysis revealed agglomerated NPs of irregular sizes and shapes. Energy Dispersive X-ray Analysis confirmed the presence of Fe, O, and Ni elements, as well as the purity of the sample. Both the direct band gap energy and crystallite size decreased with increasing dopant concentration. Detailed studies were conducted on the photoluminescence and anticancer properties. The CIE coordinates indicated a color tuning from blue to green in the visible region. CIE coordinates and CCT values ranging between 2860 to 9547 k demonstrated that the synthesized nanophosphor material meets the requirements of display technology. Additionally, anticancer properties were investigated using HeLa cells and compared with the standard drug cisplatin for biomedical applications. Electrochemical investigations revealed super capacitance values ranging from 93.43 to 149.13 F/g at a scan rate of 10 mV/s with increasing Ni2+ concentration. Therefore, the synthesized Ni2+-doped hematite NPs show great promise in display technology, the biomedical field, and as supercapacitors in energy storage devices.

Taking measurement in every direction: Implicit scene representation for accurately estimating target dimensions under monocular endoscope

Background and objectives:In endoscopy, measurement of target size can assist medical diagnosis. However, limited operating space, low image quality, and irregular target shape pose great challenges to traditional vision-based measurement methods. Methods:In this paper, we propose a novel approach to measure irregular target size under monocular endoscope using image rendering. Firstly synthesize virtual poses on the same main optical axis as known camera poses, and use implicit neural representation module that considers brightness and target boundaries to render images corresponding to virtual poses. Then, Swin-Unet and rotating calipers are utilized to obtain maximum pixel length of the target in image pairs with the same main optical axis. Finally, the similarity triangle relationship of the endoscopic imaging model is used to measure the size of the target. Results:The evaluation is conducted using renal stone fragments of patients which are placed in the kidney model and the isolated porcine kidney. The mean error of measurement is 0.12 mm. Conclusions:The approached method can automatically measure object size within narrow body cavities in any visible direction. It improves the effectiveness and accuracy of measurement in limited endoscopic space.

Photoluminescence, cytotoxic and oxygen evolution reaction kinetics studies of silver doped haematite nanoparticles

Fe2O3: Ag(1–9 mol %) nanoparticles (NPs) have been synthesized through the co-precipitation method. Crystallite size, phase, crystallinity, and structural parameters were analyzed by using powder X-ray diffraction. The Bragg reflections confirm that the synthesized NPs crystallize in hexagonal crystal structure with space group R-3c. Surface morphology consists of agglomerated irregular size and roughly spherical-shaped NPs. Energy Dispersive Analysis of X-ray confirms the presence of Fe, O, and Ag elements and also the purity of the sample. Crystallite size decreased with the increase in dopant concentration. Direct band gap energy is found to increase with an increase in dopant concentration. Photoluminescence and anticancer properties were studied in detail. The CIE coordinates confirm red emission in the visible region. CIE and CCT co-ordinate values indicate cool light emission, hence, the synthesized nanophosphor material meets the demand of display technology. Further anticancer properties were investigated on HeLa cells and compared with the standard drug cisplatin for biomedical applications. Further, Oxygen evolution reaction kinetics was investigated for Fe2O3:Ag (1 mol %) nanoparticles. demonstrates enhanced oxygen evolution reaction performance with a low overpotential of 348 mV, as shown by linear sweep voltammetry. It also exhibits a favorable Tafel slope of 83 mV/dec, indicating efficient electron transfer kinetics, making it a promising candidate for OER applications.

Chromium-extracted aluminum catalyst for co-pyrolysis of cotton fabric waste and polypropylene plastic waste to bio-oil

Heterogeneous catalysts of chromium-extracted aluminum (CE) were prepared and used to produce bio-oil from the co-pyrolysis of cotton fabric waste (CFW) and polypropylene waste (PPW). The catalysts were synthesized via wet impregnation at 5–20 wt% catalyst loading and calcined at 600 °C for 5 h. The co-pyrolysis was conducted in a fixed-bed reactor at 500 °C for 1 h with 1:1 CWF/PPW ratio and 1:1 catalyst/feedstock ratio. The physical and chemical properties of the catalysts and bio-oil were characterized by several techniques such as Brunauer–Emmet–Teller, scanning electron microscopy, energy dispersive x-ray spectroscopy, x-ray diffraction, temperature-programmed desorption and gas chromatography-mass spectrometry. Investigations into the effects of metal loadings revealed that the 15CE catalyst was the most active, achieving a maximum bio-oil yield of 76.4 % and producing valuable chemical compounds like hydrocarbons, alcohols, phenols, and furans. The XRD of 15CE catalyst confirmed the presence of chromium oxide and assigned to the rhombohedral phase of the crystal lattice. BET indicated 15CE has the largest surface area (50.4 m2/g) among the CE catalysts which contributed to its high activity. The SEM revealed CE catalysts at different metal loadings contain irregular shapes and sizes with a rough surface texture. The NH3-TPD profiles indicated alterations in acid site distribution due to the interaction between the introduced Cr metal and EA. The 15CE catalyst also demonstrated significant reusability, maintaining performance after two regeneration cycles. These results confirm the potential of CE catalysts in enhancing bio-oil production from CFW and PPW.

Preparation and Characterization of Hydroxyapatite Based Composite Material via Cold Sintering Process

This study focuses on the synthesis of hydroxyapatite (HA) from bovine bones through calcination and subsequent combination with Polyvinyl Alcohol (PVA) using the Cold Sintering Process (CSP). The CSP, operating at lower temperatures than conventional methods, aims to preserve the bioactivity and biocompatibility of HA, crucial for biomedical applications. The research investigates the influence of HA composition, sintering temperature, and PVA content on the porosity and compressive strength of the HA-PVA composite. The Hydroxyapatite powder, obtained from calcinated bovine bones, exhibits irregular shapes and sizes with a maximum grain size of 10µm, confirmed by SEM analysis. XRD analysis validates the successful production of Hydroxyapatite, showcasing characteristic peaks. The HA/PVA composite's XRD pattern indicates the dominance of HA, with PVA present as well. The study produces 27 specimens through the CSP, varying HA composition and sintering temperature. Porosity increases with higher HA content and sintering temperature, attributed to the role of PVA in binding HA particles. Compressive strength rises with increased HA content, emphasizing HA's role in enhancing mechanical strength. The presence of porosity is mitigated by a strong bond between HA and PVA, contributing to structural integrity. This research provides insights into tailoring HA-PVA materials for biomedical applications, considering controlled porosity and mechanical strength. The CSP, with its unique advantages, emerges as a promising method for developing customized materials to meet diverse biomedical needs.

A survey on machine learning system for intraductal papillary mucinous neoplasms detection

IPMN cysts, a pre-malignant risk to the pancreas, have the potential to develop into pancreatic cancer. Accurately identifying and evaluating the risk level is crucial for planning an efficient treatment strategy. However, this task is immensely challenging due to the varied and irregular shapes, textures, and sizes of IPMN cysts, as well as those of the pancreas itself. In this study, we introduce a new computer-aided diagnostic approach for classifying IPMN risk levels based on multi-contrast MRI scans. The proposed analysis framework comprises an efficient volumetric self-adapting segmentation strategy for delineating the pancreas, followed by a newly developed deep learning-based classification scheme incorporating a radiomics-based predictive approach. To evaluate the proposed decision-fusion model, we use multi-centre datasets and multi-contrast MRI scans, aiming to achieve superior performance compared to the current state of the art in this field. The ablation studies illustrate the importance of both radiomics and deep learning modules in achieving a new state-of-the-art (SOTA) performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). These key findings carry significant implications for clinical decision-making, potentially revolutionizing the way IPMN risk levels are classified. Through a series of rigorous experiments on multi-centre datasets (involving more MRI scans from five centers), we attained unprecedented performance levels with moderate accuracy. The code will be made available upon publication.

A non-contact solid particle moisture sensor based on near-infrared spectroscopy technology

The detection of moisture content in solid particles has important applications in fields such as biological research, coal mine production, and medicine. This article develops a non-contact solid particle moisture sensor (NcSPMS). NcSPMS uses the multi-light source detection method based on the principle of diffuse reflection absorption to detect the moisture content of solid particles with different particle sizes, distributions and colors. Firstly, the stacked LED light source structure (SLED-LSS) was designed to achieve illumination of LED light source for measurement (M-LED) and the LED light source for reference (R-LED) at the same position and exit angle, eliminating the influence of irregular particle size and distribution on diffuse reflected light intensity. Secondly, the stacked LED array light source structure (SLEDA-LSS) was designed to improve the detection light intensity and enable NcSPMS to detect the moisture content of solid particles of different colors. Thirdly, modulated light technology is used for the transmission of moisture content information. Two modulation frequencies were set to modulate the measurement light source and reference light source, and different intensities of square wave currents were determined to drive the M-LED and R-LED to improve detection accuracy. Fourthly, the FFT-KF data processing method was designed to effectively extract the information of diffuse reflection measurement light intensity. Finally, the function expression of moisture content was obtained through cubic fitting, and the detection of moisture content by NcSPMS was achieved. In order to verify the effectiveness of NcSPMS, the NcSPMS control system was designed. The experimental results show that compared with SLED-LSS, SLEDA-LSS improves measurement accuracy; the different current pairs driving M-LED and R-LED affect the measurement accuracy of NcSPMS; NcSPMS can detect the moisture content of white, yellow, green, red, and black sand particles, with the detection range of 0.10–11.00 %. The relative uncertainty obtained by applying NcSPMS to the detection of nickel ore particles is 4.37 %.

Guided-attention and gated-aggregation network for medical image segmentation

Recently, transformers have been widely used in medical image segmentation to capture long-range and global dependencies using self-attention. However, they often struggle to learn the local details which limit their ability to capture irregular shapes and sizes of the tissues and indistinct boundaries between the tissues, which are critical for accurate segmentation. To alleviate this issue, we propose a network named GA2Net, which comprises an encoder, a bottleneck, and a decoder. The encoder computes multi-scale features. In the bottleneck, we propose a hierarchical-gated features aggregation (HGFA) which introduces a novel spatial gating mechanism to enrich the multi-scale features. To effectively learn the shapes and sizes of the tissues, we apply deep supervision in the bottleneck. GA2Net proposes to use adaptive aggregation (AA) within the decoder, to adjust the receptive fields for each location in the feature map, by replacing the traditional concatenation/summation operations in skip connections in U-Net like architecture. Furthermore, we propose mask-guided feature attention (MGFA) modules within the decoder which strives to learn the salient features using foreground priors to adequately grasp the intricate structural and contour information of the tissues. We also apply intermediate supervision for each stage of the decoder, which further improves the capability of the model to better locate the boundaries of the tissues. Our extensive experimental results illustrate that our GA2-Net significantly outperforms the existing state-of-the-art methods over eight medical image segmentation datasets i.e., five polyps, a skin lesion, a multiple myeloma cell segmentation, and a cardiac MRI scan datasets. We then perform an extensive ablation study to validate the capabilities of our method. Code is available at https://github.com/mustansarfiaz/ga2net.

Microstructural Optimization and Corrosion Resistance Enhancement of Austenitic 317L Stainless Steel through Tailored Heat Treatment

Austenitic 317L stainless steel was favored in many industrial applications due to its excellent corrosion resistance and mechanical properties. The study involved heat treating Austenitic 317L stainless steel samples at temperatures of 500℃, 950℃, and 1100℃ to explore the effects of heat treatment temperature on microstructure and corrosion resistance. Electrochemical analysis showed that the 317L sample treated at 1100℃ exhibited the lowest passive current density, indicating the best improvement in corrosion resistance at this temperature. Results from corrosion weight loss experiments confirmed that the least weight loss occurred under the heat treatment conditions of 950℃ and 1100℃, suggesting enhanced corrosion resistance of the material. Microstructural characterization revealed that after heat treatment at 950℃, the metallographic structure transformed from a complex, irregular size and chaotic growth pattern to uniformly grown and comparatively equal-sized metallographic structures. Furthermore, heat treatment at 1100℃ resulted in larger metallographic structures with reduced boundary width and distribution density. Consequently, enhanced corrosion resistance was observed at both temperatures. Based on these findings, a heat treatment range of 950℃ to 1150℃ appeared to be a suitable post-processing method for optimizing the microstructure of 317L while concurrently improving its corrosion resistance.

Influence of Rolling Resistance and Particle Size Distribution in the Simulation of Sand Infiltration into the Static Gravel Bed

Fine sediment infiltration and subsequent clogging in a gravel bed affect several fluvial, ecological, and biological processes, resulting in the degradation of the river ecosystem. Despite many experimental and a few numerical studies, the process is yet to be entirely understood. We employed a pure Lagrangian framework, called the Discrete Element Method (DEM), to numerically investigate the infiltration process. Special attention is given to tackling the issue of non-spherical and irregular particle shapes and particle size distributions (PSDs) in numerical simulations. Due to computational limitations, these aspects were either not considered or simplified in previous numerical studies. We implicitly included non-spherical and irregular shape effects through rolling resistance models, which do not cause excessive computational overhead. Our study shows that rolling resistance models greatly influence packing and fine sediment infiltration. However, they may also lead to unphysical particle behavior; thus, they should be carefully used in numerical simulations. Oversimplified PSDs do not mirror natural systems, and full PSDs pose computational challenges. Sufficient grain classes are needed to mimic the non-homogeneity and poly-dispersity found in natural fluvial sediments. The infiltrating characteristics of sand concerning PSD and shape effects are linked to size ratio D15,Gravel/D85,Sand, assuring physical and realistic modeling of the infiltration process.

Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.

Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field. Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances. In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features. The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness. The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.

Photocatalytic removal of some selected antibiotics in polluted water system by graphitic carbon nitride-enhanced vanadium ferrite (VFe2O4@g-C3N4)

Antibiotics such as sulfamethoxazole (SUF), ciprofloxacin (CIP) and erythromycin (ERY) are frequently detected in water systems without being efficiently removed during water treatment. This study synthesized a graphitic carbon nitride-enhanced vanadium ferrite (VFe2O4@g-C3N4) as a photocatalyst for degrading SUF, CIP and ERY in an aqueous solution. VFe2O4@g-C3N4 was characterized with X-ray diffractometry (XRD), thermogravimetry analysis (TGA), ultraviolet (UV)-visible spectrophotometry, scanning electron microscope (SEM) and transmission electron microscope (TEM). The XRD characterization of VFe2O4@g-C3N4 revealed diffraction patterns with a crystallite size of 22.45 nm and a bandgap energy of 1.94 eV. The SEM image revealed the surface to be rough with irregular particle shape and size. The TEM image showed an average particle size of 92.47 nm. VFe2O4@g-C3N4 exhibited a degradation efficiency, which showed complete removal of SUF (100 %) from solution while the efficiency towards CIP is 94 ± 0.60 % and 90 ± 0.8 % towards ERY. The best photocatalytic performance was achieved with 0.12 g L−1 of VFe2O4@g-C3N4 and pH = 7.0 as the optimal conditions for achieving complete removal of SUF, CIP and ERY at a concentration lower than 10.00 mg L−1 under visible-light irradiation. The photodegradation of SUF, CIP and ERY by VFe2O4@g-C3N4 was found to be promoted by ROS with ˙OH and SO4˙− radicals playing a significant role. VFe2O4@g-C3N4 demonstrated a regeneration capacity that is above 90 % at the 10th cycle of regeneration treatment, suggesting it to be stable and reusable with the X-ray diffraction pattern remaining unchanged and no leaching of VFe2O4@g-C3N4 into solution. The result from the study reveals VFe2O4@g-C3N4 as a promising photocatalyst for removing antibiotics from an aqueous solution.

Antibacterial mechanism of Pseudomonas aeruginosa UKMP14T rhamnolipids against multidrug resistant Acinetobacter baumannii

Rhamnolipids, a major category of glycolipid biosurfactant, have recently gained enormous attention in medical field because of their relevance as effective antibacterial agents against a wide variety of pathogenic bacteria. Our previous studies have shown that rhamnolipids from an environmental isolate of Pseudomonas aeruginosa UKMP14T possess antibacterial, anti-adhesive and anti-biofilm activity against multidrug-resistant ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter sp.) pathogens. However, the mechanism of their antibacterial action remains unclear. Thus, this study aimed to elucidate the mechanism of the antibacterial action of P. aeruginosa UKMP14T rhamnolipids by studying the changes in cells of one of the ESKAPE pathogens, Acinetobacter baumannii, which is the most difficult strain to kill. Results revealed that rhamnolipid treatment rendered A. baumannii cells more hydrophobic as evaluated through contact angle measurements. It also induced the release of cellular proteins measuring 510 μg/mL at a rhamnolipid concentration of 1000 μg/mL. In addition, rhamnolipids were found to be bactericidal in their action as they could permeate the inner membranes, leading to a leak-out of nucleotides. More than 50 % of the cells were found to be killed upon 1000 μg/mL rhamnolipid treatment as observed through fluorescence microscopy. Other cellular changes such as irregular shape and size, membrane perturbations, clumping, shrinkage and physical damage were clearly visible in SEM, FESEM and laser micrographs. Furthermore, rhamnolipid treatment inhibited the levels of acyl-homoserine lactones (AHLs) in A. baumannii, which are vital for their biofilm formation and virulence. The obtained results indicate that P. aeruginosa UKMP14T rhamnolipids target outer and inner bacterial membranes through permeation, including physical damage to the cells, leading to cell leakage. Furthermore, AHL inhibition appears to be the mechanism behind their anti-biofilm action. All these observations can be correlated to rhamnolipids’ antibacterial effect against A. baumannii.

The Synthesis of Y-zeolite-modified CaCO3-ZnO Nanocomposites as an Antibacterial Agent

The ability of inorganic antibacterial agents like metal oxides and nanoscale inorganic materials to inhibit bacterial growth rates has yet to receive much research attention. In this study, CaCO3-ZnO/Y-zeolite nanocomposites were created utilizing coprecipitation and impregnation techniques with Ca(CH3COO)2, Zn(CH3COO)2 2H2O, Y-zeolite precursors. Physical and chemical characteristics of nanocomposites have been investigated using XRD, FTIR, and SEM-EDX characterizations. The agar-well diffusion method tested the substance for antibacterial activity against gram-positive and gram-negative bacteria. Nanocomposites have a crystal size range of 35.46-36.53 nm and a crystallinity of 35-37 %, according to the results of XRD analysis. The carbonate groups are visible in FTIR data at wave numbers 1433, 875, and 712 cm-1. The Zn-O absorption band was verified at wave numbers 600-400 cm-1. The Y-zeolite absorption bands at wave numbers 1012-997 cm-1 and 745-746 cm-1 were confirmed. The particle morphology is cube-shaped with irregular sizes. The EDX result showed that the composition consists of 35.92 % calcium, 1.68 % zinc, 44.81 % oxygen, and 13.79 % carbon as elements. With the addition of 2.5 % Y-zeolite, the antibacterial activity of nanocomposites showed the best results, with an inhibition zone diameter of 7.62 mm against Escherichia coli and 6.56 mm against Staphylococcus aureus bacteria.