Mentha longifolia assisted nanostructures: An approach to obliterate microbial biofilms.

Microbes maneuver strategies to become incessant and biofilms perfectly play a role in scaling up virulence to cause long-lasting infections. The present study was designed to assess the use of an eco-friendly formulation of functionalized silver nanoparticles generated from Mentha longifolia leaf extract (MℓE) for the treatment of biofilm-producing microbes. Nanoparticles synthesized using MℓE as a reducing agent were optimized at different strengths of AgNO3 (1 mM, 2 mM, 3 mM, and 4 mM). Synthesis of M. longifolia silver nanoparticles (MℓAgNPs) was observed spectrophotometrically (450 nm) showing that MℓAgNPs (4 mM) had the highest absorbance. Various techniques e.g., Fourier transforms Infrared spectroscopy (FTIR), Dynamic light scattering (DLS), zeta potential (ZP), X-ray Diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscope (TEM) were used to characterize MℓAgNPs. In the present study, the Kirby-Bauer method revealed 4mM was the most detrimental conc. of MℓAgNPs with MIC and MBC values of 0.62 µg/mL and 1.25 µg/mL, 0.03 µg/mL and 0.078 µg/mL, and 0.07 µg/mL and 0.15 µg/mL against previously isolated and identified clinical strains of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, respectively. Moreover, the MℓAgNP antibiofilm activity was examined via tissue culture plate (TCP) assay that revealed biofilm inhibition of up to 87.09%, 85.6%, 83.11%, and 75.09% against E. coli, P. aeruginosa, K. pneumonia, and S. aureus, respectively. Herbal synthesized silver nanoparticles (MℓAgNPs) tend to have excellent antibacterial and antibiofilm properties and are promising for other biomedical applications involving the extrication of irksome biofilms. For our best knowledge, it is the first study on the use of the green-synthesized silver nanoparticle MℓAgNP as an antibiofilm agent, suggesting that this material has antibiotic, therapeutic, and industrial applications.

Machine learning assisted designing of conjugated organic chromophores, light absorption, and emission behavior prediction.

Organic materials have several important characteristics that make them suitable for use in optoelectronics and optical signal processing applications. For absorption and emission maxima, the stabilities and photoactivities of conjugated organic chromophores can be tailored by selecting a suitable parent structure and incorporating substituents that predictably change the optical characteristics. However, a high-throughput design of efficient conjugated organic chromophores without using trial-and-error experimental approaches is required. In this study, machine learning (ML) is used to design and test the conjugated organic chromophores and predict light absorption and emission behavior. Many machine learning models are tried to select the best models for the prediction of absorption and emission maxima. Extreme gradient boosting regressor has appeared as the best model for the prediction of absorption maxima. Random forest regressor stands out as the best model for the prediction of emission maxima. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) is used to generate 10,000 organic chromophores. Chemical similarity analysis is performed to obtain a deeper understanding of the characteristics and actions of compounds. Furthermore, clustering and heatmap approaches are utilized.

Polyoxometalate-derived electrocatalysts enabling progress in hydrogen evolution reactions.

Platinum-based catalysts exhibit outstanding electrocatalytic performance in the hydrogen evolution reaction (HER). However, platinum-based catalysts face significant challenges due to their rarity and high cost. This paper endeavors to shed light on a promising alternative: polyoxometalate (POM)-based catalysts, which possess significant potential for the synthesis of non-noble metal-based catalysts for the HER. Utilizing POMs as raw materials to assemble POM-derived materials, including POM-derived crystalline materials, metal sulfides, phosphides, carbides, nitrides, and so on, has emerged as an effective approach for the synthesis of hydrogen evolution electrocatalysts. This approach offers advantages in both stability and electrocatalytic performance. This comprehensive review navigates through latest progress in the assembly strategy and HER performance of POM-based crystal materials, alongside discussion on transition metal compounds derived from POMs, such as carbides, phosphides, and sulfides. Besides, future developments in POM-derived electrocatalyst regulation of the electrochemical HER are prospected.

Impact of hydroxypropylation coupled with extrusion treatment on the morphological, structural, and rheological properties of sorghum and corn starch.

The purpose of the present study to prepare hydroxypropyl derivatives (HP) instant (partially cold-water swell-able) starches via extrusion technique (Ex) from sorghum and corn. The native and hydroxypropylated (at propylene level 5 % and 12 %) starch extrudates were evaluated for functional, structural, thermal and rheological properties. The development of extrudates provides ease to industries as they are easily soluble in aqueous mediums and does not require any prior heating. The degree of substitution (DS) for all extrudates varied between 0.0083 and 0.1530 which is under limit and save to consume. The X-ray diffraction (XRD) analysis revealed that all extrudates exhibited V-type pattern while the intensity of peaks increased due to hydroxypropylation. The starch extrudates showed gel-like behavior since storage modulus (G') was greater than loss modulus (G″). Non-Newtonian shear thinning behavior was observed for all extrudates. At lower temperature, HP-Ex at higher substitution level (12 %) demonstrated higher complex viscosity than native and low-substituted extrudates. Creep recovery of HP-Ex dispersion was more pronounced than native extrudates suggesting elastic nature of sorghum and corn starch extrudates. The differential scanning calorimetry (DSC) results revealed that HP-Ex were observed to gelatinize at lower temperatures and needed lower enthalpy of gelatinization (ΔHgel) because of their weakened structure after modification.

Burden and Patterns of Multidrug-Resistant Tuberculosis in Pediatric and Adolescent Patients at a Tertiary Care Hospital in Pakistan.

Background Multidrug-resistant tuberculosis (MDR-TB) presents a significant global health challenge, particularly in developing countries. This study focuses on the burden and pattern of pediatric and adolescent MDR-TB in a tertiary care hospital setting. Aims/objectives The main objective is to evaluate MDR-TB's prevalence and resistance patterns among pediatric and adolescent patients, highlighting critical demographic factors and resistance trends. Materials and methods The study utilized a prospective analytical design in two tertiary care facilities, focusing on children aged four months to 18 years diagnosed with extra-pulmonary tuberculosis. Data on demographic profiles, clinical outcomes, and drug resistance patterns were collected and analyzed using the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 27.0, Armonk, NY). Results Out of 99 enrolled participants, 63 (63.64%) met the inclusion criteria. The mean age was 70.22±48.90 months. A significant proportion, 60 (95.2%) of the cases, originated from Punjab. Notably, 10 (15.9%) of the cultures demonstrated MDR, with specific resistance observed to isoniazid (INH) in 10 (15.9%) cases, rifampicin (RIF) in 11 (17.5%) cases, and pyrazinamide (PZA) in seven (11.1%) cases. The study also recorded a high prevalence of tuberculous meningitis, affecting 52 (82.5%) participants, and malnutrition, affecting 49 (77.8%). Conclusions MDR-TB in 10 (15.9%) of the study children and adolescents presenting in Pakistan's specialized health centres is a notable burden. This points to a need for better diagnostic methods and treatment plans for pediatric patients. Implementing advanced diagnostics and personalized therapies is crucial for managing MDR-TB in susceptible demographics. Our findings emphasize the importance of updating treatment protocols to tackle the impacts of MDR-TB and its evolving resistance.

Surviving chlorinated waters: bleaching sensitivity and persistence of free-living amoebae.

Recent advancements in membrane technologies and disinfection methods have enhanced drinking water quality significantly. However, microorganisms, including free-living amoebae (FLA), persist and pose potential threats to humans. FLA are linked to severe neuro-ophthalmic infections and serve as hosts of pathogenic bacteria. This study examined FLA presence in chlorinated and ultrafiltration drinking water and evaluated chlorine's disinfectant. Of 115 water samples, 21 tested positive for Acanthamoeba sp., Allovahlkampfia sp., and Vermamoeba vermiformis, originating from chlorinated sources. FLA trophozoites withstand temperatures up to 37 °C, while the cysts tolerate heat shocks of 60-70 °C. Trophozoites are susceptible to 5 mg L-1 chlorine, but cysts remain viable at concentrations up to 10 mg L-1. FLAs' survival in chlorinated waters is attributed to high cyst tolerance and lower residual chlorine concentrations. These findings highlight the need for ultrafiltration or enhanced chlorination protocols to ensure safer drinking water.

Exploring half-metallic ferromagnetism and thermoelectric properties of Tl2WX6 (X = Cl and Br) double perovskites.

Half-metallic semiconductors typically exhibit 100% spin polarization at the Fermi level which makes them desired materials for spintronic applications. In this study, we reported a half-metallic ferromagnetic nature in vacancy-ordered double perovskites Tl2WX6 (X = Cl and Br). The magnetic, electronic, and thermoelectric properties of the material are studied by the use of density functional theory (DFT). For the calculations of exchange-correlation potential, PBE-sol is employed while more accurate electronic band structure and density of states (DOS) are calculated by the mBJ potential. Both materials exhibited structural stability in the cubic structure with Fm3̄m space-group. The mechanical stability is confirmed by their computed elastic constants while their thermodynamic stability is attested by negative formation energy. The spin-based volume optimization suggested the ferromagnetic nature of the materials which is further confirmed by the negative value of the exchange energy Δ x(pd). Moreover, computed magnetic moment value for Tl2WCl6 and Tl2WBr6 is 2 µB and the majority of this comes from W. The spin-polarized band structure and DOS confirmed that both materials are half-metallic and at the Fermi level they exhibit 100% spin polarization. Furthermore, in the spin-down state, materials behave as semiconductors with wide bandgaps. Lastly, the thermoelectric properties are evaluated by the BoltzTrap code. The thermoelectric parameters which include the Seebeck coefficient, electrical conductivity, thermal conductivity, power factor, and figure of merit (ZT) are investigated in the range of temperatures from 200 to 800 K. The half-metallic ferromagnetic and thermoelectric characteristics make these materials desired for spintronics and thermoelectric applications.

Competitiveness of citrus export and its determinants: a two-way fixed effect panel data model approach.

Pakistan has a conducive condition for the development of a wide range of scrumptious fruits. As a result, the country grows a diverse assortment of tropical and subtropical fruits; the most prized and top-ranked fruit among all fruits grown in Pakistan is citrus. Citrus is the principal fruit that contributes significantly to Pakistan's export earnings and national income. In this study, the cross-border determinants influencing Pakistan's citrus exports to its topmost 22 trading partners are examined using a gravity model technique. This is the first large study from Pakistan by using gravity model to check the impact of various cross-border factors on citrus fruit export. The analysis is based on a panel dataset covering the years 2003 to 2021. To estimate the results, the study used fixed effect regression with time and country fixed effects. The results signify that per capita income, population, and some regional dummies are positively associated with citrus exports from Pakistan. Citrus price, distance, exchange rate, and other regional dummies are observed to have an adverse effect on citrus exports. Trade agreements between Pakistan and trade partners such as free trade agreements, preferential trade agreements, and SAFTA, have been observed as important determinants of citrus exports. Citrus exporters in Pakistan can also benefit from understanding the factors that influence export markets. By addressing the challenges identified in this study, Pakistan can enhance its citrus exports and boost its agricultural sector.

Numerical simulation to optimize power conversion efficiency of an FTO/GO/Cs2AgBiBr6/Cu2O solar cell.

Efficient conversion of solar power to electrical power through the development of smart, reliable, and environmentally friendly materials is a key focus for the next-generation renewable energy sector. The involvement of degradable and toxic elements present in hybrid perovskites presents serious concerns regarding the commercial viability of these materials for the solar cell industry. In this study, a solar cell with a stable, nondegradable, and lead-free halide-based double perovskite Cs2AgBiBr6 as the absorber layer, Cu2O as a hole transport layer, and GO as the electron transport layer has been simulated using SCAPS 1D. The thickness of the absorber, electron transport, and hole transport layers are tuned to optimize the performance of the designed solar cell. Notably, perovskite solar cells functioned most efficiently with an electron affinity value of 4.0 eV for Cu2O. In addition, the effect of variation of series resistance and temperature on generation and recombination rates, current density, and quantum efficiency has been elaborated in detail. The findings of this study provide valuable insight and encouragement toward the realization of a non-toxic, inorganic perovskite solar device and will be a significant step forward in addressing environmental concerns associated with perovskite solar cell technology.

MRET: Modified Recursive Elimination Technique for ranking author assessment parameters.

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP's capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields' of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

Investigation of half metallic properties of Tl2Mo(Cl/Br)6 double perovskites for spintronic devices.

The manipulation of electronic device characteristics through electron spin represents a burgeoning frontier in technological advancement. Investigation of magnetic and transport attributes of the Tl2Mo(Cl/Br)6 double perovskite was performed using Wien2k and BoltzTraP code. When the energy states between ferromagnetic and antiferromagnetic conditions are compared, it is evident that the ferromagnetic state exhibits lower energy levels. Overcoming stability challenges within the ferromagnetic state is achieved through the manipulation of negative ΔHf within the cubic state. The analysis of the half metallicity character involves an analysis of band structure (BS) and DOS, elucidating its mechanism through PDOS using double exchange model p-d hybridization. The verification of 100% spin polarization is confirmed through factors such as spin polarization and the integer value of the total magnetic moment. Furthermore, the thermoelectric response, as indicated by the ratios of thermal-electrical conductivity and ZT, underscores the promising applications of these compounds in thermoelectric device applications.

Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Nigella sativa for the Treatment of Breast Cancer.

Breast cancer is a prevalent and potentially life-threatening disease that affects women worldwide. Natural products have gained attention as potential anticancer agents due to their fewer side effects, low toxicity, and cost effectiveness compared to traditional chemotherapy drugs. In the current study, the network pharmacology approach was used following a molecular docking study to evaluate the therapeutic potential of N. sativa-derived phytochemicals against breast cancer. Specifically, the study aimed to identify potential anticancer agents targeting key proteins implicated in breast cancer progression. Five proteins (i.e., EGFR, MAPK3, ESR1, MAPK1, and PTGS2) associated with breast cancer were selected as receptor proteins. Fourteen phytochemicals from N. sativa were prioritized based on drug-likeness (DL) and oral bioavailability (OB) parameters (with criteria set at DL > 0.18 and OB > 30%, respectively). Subsequent analysis of gene targets identified 283 overlapping genes primarily related to breast cancer pathogenesis. Ten hub genes were identified through topological analysis based on their significance in the KEGG pathway and GO annotations. Molecular docking revealed strong binding affinities between folic acid, betulinic acid, stigmasterol, and selected receptor proteins. These phytochemicals also demonstrated druggability potential. In vitro experiments in the MDA-MB-231 breast cancer cell line revealed that betulinic acid and stigmasterol significantly reduced cell viability after 24 h of treatment, confirming their anticancer activity. Furthermore, in vivo evaluation using a DMBA-induced rat model showed that betulinic acid and stigmasterol contributed to the significant recovery of cancer markers. This study aimed to explore the mechanisms underlying the anticancer potential of N. sativa phytochemicals against breast cancer, with the ultimate goal of identifying novel therapeutic candidates for future drug development. Overall, these results highlight betulinic acid and stigmasterol as promising candidates to develop novel anticancer agents against breast cancer. The comprehensive approach of this study, which integrates network pharmacology and molecular docking study and its experimental validation, strengthens the evidence supporting the therapeutic benefits of N. sativa-derived phytochemicals in breast cancer treatment, making them promising candidates for the development of novel anticancer agents against breast cancer.

Microalgal and activated sludge processing for biodegradation of textile dyes.

The textile industry contributes substantially to water pollution. To investigate bioremediation of dye-containing wastewater, the decolorization and biotransformation of three textile azo dyes, Red HE8B, Reactive Green 27, and Acid Blue 29, were considered using an integrated remediation approach involving the microalga Chlamydomonas mexicana and activated sludge (ACS). At a 5 mg L-1 dye concentration, using C. mexicana and ACS alone, decolorization percentages of 39%-64% and 52%-54%, respectively, were obtained. In comparison, decolorization percentages of 75%-79% were obtained using a consortium of C. mexicana and ACS. The same trend was observed for the decolorization of dyes at higher concentrations, but the potential for decolorization was low. The toxic azo dyes adversely affect the growth of microalgae and at high concentration 50 mg L-1 the growth rate inhibited to 50-60% as compared to the control. The natural textile wastewater was also treated with the same pattern and got promising results of decolorization (90%). Moreover, the removal of BOD (82%), COD (72%), TN (64%), and TP (63%) was observed with the consortium. The HPLC and GC-MS confirm dye biotransformation, revealing the emergence of new peaks and the generation of multiple metabolites with more superficial structures, such as N-hydroxy-aniline, naphthalene-1-ol, and sodium hydroxy naphthalene. This analysis demonstrates the potential of the C. mexicana and ACS consortium for efficient, eco-friendly bioremediation of textile azo dyes.

Role of BP-ANN in simulating greenhouse gas emissions from global aquatic ecosystems via carbon component-environmental factor coupling.

Inland waters (IW), estuarine areas (EA), and offshore areas (OA) function as aquatic systems in which the transport of carbon components results in the release of greenhouse gases (GHGs). Interconnected subsystems exhibit a greater greenhouse effect than individual systems. Despite this, there is a lack of research on how carbon loading and its components impact GHG emissions in various aquatic systems. In this study, we analyzed 430 aquatic sites to explore trade-off mechanisms among dissolved organic carbon (DOC), particulate organic carbon, dissolved inorganic carbon (DIC), and GHGs. The results revealed that IW emerged as the most significant GHG source, possessing a comprehensive global warming potential (GWP) of 0.78 ± 0.08 (10-2 Pg CO2-ep ha-1 year-1) for combined carbon dioxide, methane, and nitrous oxide. This surpassed the cumulative potentials of EA and OA (0.35 ± 0.05 (10-2 Pg CO2-ep ha-1 year-1)). Additionally, structural equation modeling indicated that GHG emissions resulted from a combination of carbon component loading and environmental factors. DOC exhibited a positive correlation with GWPs when influenced by biodegradable DOC. Total alkalinity and pH influenced DIC, leading to elevated pCO2 in aquatic systems, thereby enhancing GWPs. Predictive modeling using backpropagation artificial neural networks (BP-ANN) for GWPs, incorporating carbon components and environmental factors, demonstrated a good fit (R2 = 0.6078, RMSEaverage = 0.069, p > 0.05) between observed and predicted values. Enhancing the estimation of aquatic region feedback to GHG changes was achieved by incorporating corresponding water quality parameters. In summary, this study underscores the pivotal role of carbon components and environmental factors in aquatic regions for GHG emissions. The application of BP-ANN to estimate greenhouse effects from aquatic regions is highlighted, providing theoretical and experimental support for future advancements in monitoring and developing policies concerning the influence of water quality on GHG emissions.

Discovery of a novel and highly selective JAK3 inhibitor as a potent hair growth promoter.

JAK-STAT signalling pathway inhibitors have emerged as promising therapeutic agents for the treatment of hair loss. Among different JAK isoforms, JAK3 has become an ideal target for drug discovery because it only regulates a narrow spectrum of γc cytokines. Here, we report the discovery of MJ04, a novel and highly selective 3-pyrimidinylazaindole based JAK3 inhibitor, as a potential hair growth promoter with an IC50 of 2.03 nM. During in vivo efficacy assays, topical application of MJ04 on DHT-challenged AGA and athymic nude mice resulted in early onset of hair regrowth. Furthermore, MJ04 significantly promoted the growth of human hair follicles under ex-vivo conditions. MJ04 exhibited a reasonably good pharmacokinetic profile and demonstrated a favourable safety profile under in vivo and in vitro conditions. Taken together, we report MJ04 as a highly potent and selective JAK3 inhibitor that exhibits overall properties suitable for topical drug development and advancement to human clinical trials.

Responses of soil seed bank and its above-ground vegetation to various reclamation patterns.

Coastal land reclamation has become a primary strategy for alleviating conflicts between human development and land resource utilization. However, anthropogenic activities associated with land reclamation inevitably result in significant changes to coastal wetland ecosystems. Previous studies have mainly focused on the ecological consequences of land reclamation on above-ground vegetation, while overlooking the distinctions between different reclamation patterns and the critical role of soil seed bank in maintaining ecosystem stability. In this study, the responses of soil seed bank and vegetation to various reclamation patterns, as well as the factors influencing changes in seed bank characteristics, were analyzed in a natural coastal wetland (NCW), a reclaimed wetland with sea embankments constructed on native wetland (SEW), and another reclaimed wetland formed through land reclamation from the sea (LRW). These findings suggest that seed banks and their vegetation adopt different adaptation strategies under various reclamation patterns. In the NCW, the proportion of non-halophytes (1.39%), diversity, and density of the seed bank were at their lowest levels, whereas the species compositions derived from the seed bank and vegetation were very similar (similarity coefficient = 0.67). Conversely, the seed bank in the SEW demonstrated the highest species diversity, which differed significantly from the species composition of its above-ground vegetation (similarity coefficient = 0.21). However, the highest proportion of non-halophytes (36.60%), vegetation diversity, and seed bank density occurred in LRW. Furthermore, differences in seed bank characteristics under different reclamation patterns may be related to changes in soil salinity and plant reproductive strategies after reclamation. Adjusting reclamation patterns and restoring soil properties could potentially optimize the types of local plant species and their distribution in reclaimed areas.

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

Efficient performance of InP and InP/ZnS quantum dots for photocatalytic degradation of toxic aquatic pollutants.

In recent years, the growing concern over the presence of toxic aquatic pollutants has prompted intensive research into effective and environmentally friendly remediation methods. Photocatalysis using semiconductor quantum dots (QDs) has developed as a promising technology for pollutant degradation. Among various QD materials, indium phosphide (InP) and its hybrid with zinc sulfide (ZnS) have gained considerable attention due to their unique optical and photocatalytic properties. Herein, InP and InP/ZnS QDs were employed for the removal of dyes (crystal violet, and congo red), polyaromatic hydrocarbons (pyrene, naphthalene, and phenanthrene), and pesticides (deltamethrin) in the presence of visible light. The degradation efficiencies of crystal violet (CV) and congo red (CR) were 74.54% and 88.12% with InP, and 84.53% and 91.78% with InP/ZnS, respectively, within 50 min of reaction. The InP/ZnS showed efficient performance for the removal of polyaromatic hydrocarbons (PAHs). For example, the removal percentage for naphthalene, phenanthrene, and pyrene was 99.8%, 99.6%, and 88.97% after the photocatalytic reaction. However, the removal percentage of InP/ZnS for pesticide deltamethrin was 90.2% after 90 min light irradiation. Additionally, advanced characterization techniques including UV-visible spectrophotometer (UV-Vis), photoluminescence (PL), X-ray diffractometer (XRD), energy-dispersive spectrometer (EDS) elemental mapping, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA) were used to analyze the crystal structure, morphology, and purity of the fabricated materials in detail. The particle size results obtained from TEM are in the range of 2.28-4.60 nm. Both materials (InP and InP/ZnS) exhibited a spherical morphology, displaying distinct lattice fringes. XRD results of InP depicted lattice planes (111), (220), and (311) in good agreement with cubic geometry. Furthermore, the addition of dopants was discovered to enhance the thermal stability of the fabricated material. In addition, QDs exhibited efficacy in the breakdown of PAHs. The analysis of their fragmentation suggests that the primary mechanism for PAHs degradation is the phthalic acid pathway.

Investigation of gadolinium doped manganese nano spinel ferrites via magnetic hypothermia therapy effect towards MCF-7 breast cancer.

Magnetic spinel ferrite nanoparticles (MSF-NPs) are potential candidates for biomedical applications, especially in cancer diagnosis and therapy due to their excellent physiochemical and magnetic properties. In the current study, MSF-NPs were fabricated by sol-gel auto combustion method. The crystal structure and surface morphology were confirmed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The magnetic properties were studied by VSM (vibrating sample magnetometer). As increasing Gd3+ concentration, the saturation magnetization values decreased from (17.8-2.3) emu/g, while the coercivity decreased from (499-133) Oe at room temperature. Finally, the fabricated MSF-NPs were tested against anticancer activity by MTT assay. The IC50 = 21.27 µg/mL value was observed, showing the strong antiproliferative activity of these nanoparticles. These results suggested that the obtained MSF-NPs would be useful for remote-controlled hyperthermia therapy for cancer treatment and MRI application due to their excellent magnetic properties. These distinct properties make MSF-NPs most suitable for cancer treatment and bright Contrast Agents (T1-MRI).

Bio-net dataset: AI-based diagnostic solutions using peripheral blood smear images.

Peripheral blood smear examination is one of the basic steps in the evaluation of different blood cells. It is a confirmatory step after an automated complete blood count analysis. Manual microscopy is time-consuming and requires professional laboratory expertise. Therefore, the turn-around time for peripheral smear in a health care center is approximately 3-4 hours. To avoid the traditional method of manual counting under the microscope a computerized automation of peripheral blood smear examination has been adopted, which is a challenging task in medical diagnostics. In recent times, deep learning techniques have overcome the challenges associated with human microscopic evaluation of peripheral smears and this has led to reduced cost and precise diagnosis. However, their application can be significantly improved by the availability of annotated datasets. This study presents a large customized annotated blood cell dataset (named the Bio-Net dataset from healthy individuals) and blood cell detection and counting in the peripheral blood smear images. A mini-version of the dataset for specialized WBC-based image processing tasks is also equipped to classify the healthy and mature WBCs in their respective classes. An object detection algorithm called You Only Look Once (YOLO) with a refashion disposition has been trained on the novel dataset to automatically detect and classify blood cells into RBCs, WBCs, and platelets and compare the results with other publicly available datasets to highlight the versatility. In short the introduction of the Bio-Net dataset and AI-powered detection and counting offers a significant potential for advancement in biomedical research for analyzing and understanding biological data.