Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Retrospective Analysis of Postoperative Nonhepatic Outcomes Following Major Liver Resection.

Background Liver surgery is a major and challenging procedure for the surgeon, the anesthetist, and the patient. The objective of this study was to evaluate the postoperative nonhepatic complications of patients undergoing liver resection surgery with perioperative factors. Methods We retrospectively analyzed 79 patients who underwent liver resection surgeries at the Shaukat Khanum Memorial Cancer Hospital and Research Centre in Lahore, Pakistan, from July 2015 to December 2022. Results The mean age at the time of surgery was 53 years (range: 3-77 years), and the mean BMI was 26.43 (range: 15.72-38.0 kg/m2). Of the total patients, 44.3 % (n = 35) had no comorbidities, 26.6% (n=21) had one comorbidity, and 29.1% (n=23) had two or more comorbidities. Patients in whom the blood loss was more than 375 ml required postoperative oxygen inhalation with a significant relative risk of 2.6 (p=0.0392) and an odds ratio of 3.5 (p=0.0327). Similarly, patients who had a surgery time of more than five hours stayed in the hospital for more than seven days, with a statistically significant relative risk of 2.7 (p=0.0003) and odds ratio of 7.64 (p=0.0001). The duration of surgery was also linked with the possibility of requiring respiratory support, with a relative risk of 5.0 (p=0.0134) and odds ratio of 5.73 (p=0.1190). Conclusion Patients in our cohort who had a prolonged duration of surgery received an increased amount of fluids, and a large volume of blood loss was associated with prolonged stay in the ICU (>2 days), hospital admission (>7 days), ICU readmission, and increased incidence of cardiorespiratory, neurological, and renal disturbances postoperatively.

Optimization of an ultrasound-assisted extraction method to obtain gallic acid-rich extracts from mango seed kernels.

Gallic acid is a widely recognized bioactive compound that falls under the category of secondary polyphenolic metabolites and is fairly found in mango fruit waste, specifically in mango seed kernel (MSK). This study aimed to adopt a green extraction approach to extract this valuable compound via ultrasound-assisted extraction (UAE) without using organic solvents but only water to obtain hazard-free extracts, and the cost of extraction can be minimal. pH (2-8), solvent ratio (20-60 mL/g), temperature (30-60°C) and time (30-60 min) of extraction were the independent variables used for extraction optimization. Single-factor experiments to obtain working ranges for selected extraction variables were carried out. A central composite design using response surface methodology was used to determine the optimum condition to obtain the maximum yield of gallic acid from MSK. The optimized extraction conditions were 3.9 pH, 36.25 mL/g solvent ratio, and 39.4°C of extraction temperature for 21.3 min. As a result, the optimized yield was 5.76 ± 0.41 mg/g, which was comparably equal to and/or better than the other solvent extraction systems. The results showed that gallic acid could efficiently be extracted via UAE under these optimal conditions. It is safer than extraction systems involving hazardous solvents that can be feasibly used for its nutraceutical and therapeutic applications.

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Iron oxide nanoparticle (Fe3O4 NPs) synthesized from B. subtilis reduced arsenic (as) toxicity in rice (Oryza sativa L.) plant.

Arsenic (As) is one of the most important water pollutant of global concern due to its extreme hazard. In the present study, B. subtilis synthesized iron oxide nanoparticles (Fe3O4 NPs) were used for mitigation of harmful metalloid As from the aqueous solution. Initially, the arsenic removal efficiency was tested in a batch culture experiment across various concentrations (5, 10 and 15 ppm) of B. subtilis synthesized Fe3O4 NPs at different pH, time interval and agitation speed. Optimal removal efficiency of As by using B. subtilis synthesized Fe3O4 NPs was observed at pH 7, after 80 min, and with agitation at 200 rpm. Additionally, hydroponic culture experiment was designed to assess B. subtilis synthesized Fe3O4 NPs efficiency in removal of As from As-contaminated water used to irrigate rice plants. Results revealed that B. subtilis synthesized Fe3O4 NPs effectively removed As from the contiminated water and reduced its uptake by the different parts of rice plants (root, shoot and leaf). Furthermore, these B. subtilis synthesized Fe3O4 NPs also reduced the bioaccumulation and enhanced plant tolerance to As, suggesting their potential in mitigating heavy metal toxicity, especially As and promoting plant growth. Thus, this study proposes B. subtilis synthesized Fe3O4 NPs as nano-adsorbents in reducing arsenic toxicity in rice plants.

Phytochemicals, Antioxidant, and Antidiabetic Effects of Ranunculus hirtellus Aerial Parts and Roots: Methanol and Aqueous Extracts.

Ranunculus hirtellus, also known as crowfoot (buttercup), has a rich tradition of use in various biological contexts. While antibacterial studies on extracts from this plant have been conducted, the phytochemical composition, antioxidant properties, and antidiabetic effects remain unexplored. In this study, the phytochemical, antioxidant, and antidiabetic effects of its methanol and aqueous extracts were investigated. Our approach involved gas chromatography-mass spectrometry (GC/MS), alongside quantitative and qualitative methods, for phytochemical profiles. Additionally, concerning biological activities, the antioxidant effect was assessed through 2, 2-diphenyl-pieryl hydrazyl (DPPH) and 2, 2'-azino-bis (3-ethylbenzothiazoline-6-sulfonate) (ABTS) assays, while the antidiabetic effect was examined through the α-amylase inhibitory assay. The chloroform, ethyl acetate, and n-hexane extracts of R. hirtellus revealed the presence of 14 distinct compounds. In the methanol extract, sterols, quinones, glycosides, lactones, lignin, and flavonoids were identified. The aqueous extract contained sterols, alkaloids, glycosides, triterpenes, terpenoids, quinones, leucoanthocyanins, and lactones. The total flavonoid content (TFC), total phenolic content (TPC), total tannin content (TTC), and reducing sugar content (RDC) were determined in plant extracts, and a linear relationship was found between these parameters. Additionally, the TTC, TPC, and TFC values for both extracts hovered around 0.3786, 0.0476, and 0.1864 µg/mL, respectively, across all plant concentrations, while RDC ranged from 0.9336 to 1.0119 µg/mL in all four extracts. In vitro assays demonstrated dose-dependent antidiabetic activity in both methanolic and aqueous extracts by inhibiting α-amylase. Furthermore, the antioxidant activity observed in the DPPH assay was greater in the aqueous extract compared with the methanolic extract. In addition, the ethyl acetate extract exhibited the highest inhibition among chloroform and n-hexane in the ABTS assay. The results suggest that R. hirtellus can be a potential source of natural antioxidants and antidiabetic agents, and further studies are warranted to investigate the underlying mechanisms of its therapeutic effects.

AI-based tool for early detection of Alzheimer's disease.

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

Inoculation of heavy metal resistant bacteria alleviated heavy metal-induced oxidative stress biomarkers in spinach (Spinacia oleracea L.).

Most vegetable crops are severely affected by the uptake of heavy metals from the soil. Heavy metals in vegetable bodies generate reactive oxygen species (ROS) that unbalance the antioxidant defense system. This study was initiated to determine the physiological and biochemical characteristics of spinach plants grown on soil contaminated with heavy metals and responding to Bacillus cereus and Bacillus aerius were isolated from soil contaminated with heavy metals. Heavy metal contamination led to a significant reduction in seed germination, seedling biomass, protein, and total nitrogen content of spinach plants grown in contaminated soils compared to control soils. In contrast, a significant increase in the content of metallothioneins and antioxidant enzymes was observed. Plants inoculated with B. cereus and B. aerius significantly reduced the oxidative stress induced by heavy metals by improving seed germination (%), seedling growth, nitrogen, and protein content. The content of metallothioneins and the activities of antioxidant enzymes were reduced in spinach plants grown from seeds inoculated with bacterial strains. In addition, plants inoculated with, B. cereus and B. aerius showed greater stomata opening than plants grown on soil contaminated with heavy metals, whose stomata were almost closed. These results suggested that both bacterial strains enhanced plant growth by reducing oxidative stress caused by metals.

Robotic Versus Laparoscopic Liver Resection in Various Settings: An International Multicenter Propensity Score Matched Study of 10.075 Patients.

OBJECTIVE: To compare the perioperative outcomes of robotic liver surgery (RLS) and laparoscopic liver surgery (LLS) in various settings. BACKGROUND: Clear advantages of RLS over LLS have rarely been demonstrated, and the associated costs of robotic surgery are generally higher than those of laparoscopic surgery. Therefore, the exact role of the robotic approach in minimally invasive liver surgery remains to be defined. METHODS: In this international retrospective cohort study, the outcomes of patients who underwent RLS and LLS for all indications between 2009 and 2021 in 34 hepatobiliary referral centers were compared. Subgroup analyses were performed to compare both approaches across several types of procedures: (1) minor resections in the anterolateral (2, 3, 4b, 5, and 6) or (2) posterosuperior segments (1, 4a, 7, 8), and (3) major resections (≥3 contiguous segments). Propensity score matching was used to mitigate the influence of selection bias. The primary outcome was textbook outcome in liver surgery (TOLS), previously defined as the absence of intraoperative incidents ≥grade 2, postoperative bile leak ≥grade B, severe morbidity, readmission, and 90-day or in-hospital mortality with the presence of an R0 resection margin in case of malignancy. The absence of a prolonged length of stay was added to define TOLS+. RESULTS: Among the 10.075 included patients, 1.507 underwent RLS and 8.568 LLS. After propensity score matching, both groups constituted 1.505 patients. RLS was associated with higher rates of TOLS (78.3% vs 71.8%, P < 0.001) and TOLS+ (55% vs 50.4%, P = 0.026), less Pringle usage (39.1% vs 47.1%, P < 0.001), blood loss (100 vs 200 milliliters, P < 0.001), transfusions (4.9% vs 7.9%, P = 0.003), conversions (2.7% vs 8.8%, P < 0.001), overall morbidity (19.3% vs 25.7%, P < 0.001), and microscopically irradical resection margins (10.1% vs. 13.8%, P = 0.015), and shorter operative times (190 vs 210 minutes, P = 0.015). In the subgroups, RLS tended to have higher TOLS rates, compared with LLS, for minor resections in the posterosuperior segments (n = 431 per group, 75.9% vs 71.2%, P = 0.184) and major resections (n = 321 per group, 72.9% vs 67.5%, P = 0.086), although these differences did not reach statistical significance. CONCLUSIONS: While both produce excellent outcomes, RLS might facilitate slightly higher TOLS rates than LLS.

Complex adaptive learning cortical neural network systems for solving time-fractional difference equations with bursting and mixed-mode oscillation behaviours.

Complex networks have been programmed to mimic the input and output functions in multiple biophysical algorithms of cortical neurons at spiking resolution. Prior research has demonstrated that the ineffectual features of membranes can be taken into account by discrete fractional commensurate, non-commensurate and variable-order patterns, which may generate multiple kinds of memory-dependent behaviour. Firing structures involving regular resonator chattering, fast, chaotic spiking and chaotic bursts play important roles in cortical nerve cell insights and execution. Yet, it is unclear how extensively the behaviour of discrete fractional-order excited mechanisms can modify firing cell attributes. It is illustrated that the discrete fractional behaviour of the Izhikevich neuron framework can generate an assortment of resonances for cortical activity via the aforesaid scheme. We analyze the bifurcation using fragmenting periodic solutions to demonstrate the evolution of periods in the framework's behaviour. We investigate various bursting trends both conceptually and computationally with the fractional difference equation. Additionally, the consequences of an excitable and inhibited Izhikevich neuron network (INN) utilizing a regulated factor set exhibit distinctive dynamic actions depending on fractional exponents regulating over extended exchanges. Ultimately, dynamic controllers for stabilizing and synchronizing the suggested framework are shown. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

An advanced approach for the electrical responses of discrete fractional-order biophysical neural network models and their dynamical responses.

The multiple activities of neurons frequently generate several spiking-bursting variations observed within the neurological mechanism. We show that a discrete fractional-order activated nerve cell framework incorporating a Caputo-type fractional difference operator can be used to investigate the impacts of complex interactions on the surge-empowering capabilities noticed within our findings. The relevance of this expansion is based on the model's structure as well as the commensurate and incommensurate fractional-orders, which take kernel and inherited characteristics into account. We begin by providing data regarding the fluctuations in electronic operations using the fractional exponent. We investigate two-dimensional Morris-Lecar neuronal cell frameworks via spiked and saturated attributes, as well as mixed-mode oscillations and mixed-mode bursting oscillations of a decoupled fractional-order neuronal cell. The investigation proceeds by using a three-dimensional slow-fast Morris-Lecar simulation within the fractional context. The proposed method determines a method for describing multiple parallels within fractional and integer-order behaviour. We examine distinctive attribute environments where inactive status develops in detached neural networks using stability and bifurcation assessment. We demonstrate features that are in accordance with the analysis's findings. The Erdös-Rényi connection of asynchronization transformed neural networks (periodic and actionable) is subsequently assembled and paired via membranes that are under pressure. It is capable of generating multifaceted launching processes in which dormant neural networks begin to come under scrutiny. Additionally, we demonstrated that boosting connections can cause classification synchronization, allowing network devices to activate in conjunction in the future. We construct a reduced-order simulation constructed around clustering synchronisation that may represent the operations that comprise the whole system. Our findings indicate the influence of fractional-order is dependent on connections between neurons and the system's stored evidence. Moreover, the processes capture the consequences of fractional derivatives on surge regularity modification and enhance delays that happen across numerous time frames in neural processing.

Proteomic Analysis Reveals Salt-Tolerant Mechanism in Soybean Applied with Plant-Derived Smoke Solution.

Salt stress of soybean is a serious problem because it reduces plant growth and seed yield. To investigate the salt-tolerant mechanism of soybean, a plant-derived smoke (PDS) solution was used. Three-day-old soybeans were subjected to PDS solution under 100 mM NaCl for 2 days, resulting in PDS solution improving soybean root growth, even under salt stress. Under the same condition, proteins were analyzed using the proteomic technique. Differential abundance proteins were associated with transport/formaldehyde catabolic process/sucrose metabolism/glutathione metabolism/cell wall organization in the biological process and membrane/Golgi in the cellular component with or without PDS solution under salt stress. Immuno-blot analysis confirmed that osmotin, alcohol dehydrogenase, and sucrose synthase increased with salt stress and decreased with additional PDS solution; however, H+ATPase showed opposite effects. Cellulose synthase and xyloglucan endotransglucosylase/hydrolase increased with salt and decreased with additional PDS solution. Furthermore, glycoproteins decreased with salt stress and recovered with additional treatment. As mitochondrion-related events, the contents of ATP and gamma-aminobutyric acid increased with salt stress and recovered with additional treatment. These results suggest that PDS solution improves the soybean growth by alleviating salt stress. Additionally, the regulation of energy metabolism, protein glycosylation, and cell wall construction might be an important factor for the acquisition of salt tolerance in soybean.

Synthesis of Iron Oxide Nanoparticles from Madhuca indica Plant Extract and Assessment of Their Cytotoxic, Antioxidant, Anti-Inflammatory, and Anti-Diabetic Properties via Different Nanoinformatics Approaches.

Recently, nanobiotechnology has attracted a lot of attention, as it is a rapidly emerging field that is still growing and developing efficient and advanced therapeutic protocols under the umbrella of nanomedicine. It can revolutionize solutions to biomedical problems by developing effective treatment protocols and therapeutics. However, focus and research are still required to make these therapeutics more effective and safer to use. In this study, iron oxide nanoparticles were synthesized from Madhuca indica extract using green synthesis protocols. The nanoparticles were further characterized based on their absorption spectrum, size, structural morphology, and other related parameters. Biological assays were also performed to evaluate biological applications for the synthesized nanoparticles. In silico analysis was performed to assess the druglike properties of synthesized nanoparticles. The results proved an optimized synthesis of the iron oxide nanoparticles with the size of 56 nm confirmed by SEM. The FTIR analysis predicted the presence of nitro and carbonyl groups in the synthesized nanoparticles. The 81% DPPH inhibition confirmed the antioxidant activity, and the 96.20% inhibition of egg albumin protein confirmed the anti-inflamatory activity. Additionally, the 73.26% inhibition of α-amylase, which was more than that of the control used, confirmed the antidiabetic activity. The ADMET analysis confirmed the synthesized nanoparticles as potential therapeutic candidates as well. However, further evaluation for safety concerns is still required to use these FeONPs as potential therapeutic agents. This study can be proved as a significant contribution to the scientific community and a gateway to the future scientists who are willing to work on nanomedicine and nanobiotechnology. ADMET analysis confirmed the synthesized nanoparticles as potential therapeutic candidates as well. However, further evaluation for safety concerns is still required to use these FeONPs and potential therapeutic agents.

Experimental and Theoretical Biological Probing of Schiff Bases as Esterase Inhibitors: Structural, Spectral and Molecular Insights.

The present study was designed to evaluate the in vitro and in silico potential of the Schiff bases (Z)-4-ethoxy-N-((5-nitrothiophen-2-yl)methylene)benzenamine (1) and (Z)-2,4-diiodo-6-((2-methyl-3-nitrophenylimino)methyl)phenol (2). These Schiff bases were synthesized according to a reported method using ethanol as a solvent, and each reaction was monitored on a TLC until completion of the reaction. The structures of both compounds were elucidated using spectroscopic techniques such as UV-Vis, FTIR, 1H NMR and 13C NMR. Molecular structure was determined using single-crystal XRD, which revealed that compounds 1 and 2 were monoclinic and triclinic, respectively. Hirshfeld surface analysis (HS) and 2D fingerprint plots were used to determine the intermolecular interactions along the contact contribution in the crystalline molecules. The structures of both compounds were optimized through a hybrid functional method B3LYP using the 6-31G(d,p) basis set, and various structural parameters were studied. The experimental and theoretical parameters (bond angle and bond length) of the compounds were compared with each other and are in close agreement. The in vitro esterase potential of the synthesized compounds was checked using a spectrophotometric model, while in silico molecular docking studies were performed with AutoDock against two enzymes of the esterase family. The docking studies and the in vitro assessment predicted that such molecules could be used as enzyme inhibitors against the tested enzymes: acetylcholine esterase (AChE) and butyrylcholine esterase (BChE).

Pretreatment with troxerutin protects/improves neurological deficits in a mouse model of traumatic brain injury.

Traumatic brain injury (TBI) is the major and leading cause of mortality and an alarming public health challenge. TBI leads to permanent cognitive, motor, sensory and psychotic disabilities. Patients suffering from the various and long-term repercussions of TBI currently have limited therapy choices. The current research work was designed to evaluate the beneficial and neuroprotective role of Troxerutin (Trox) (a natural flavonoid) in a closed brain injury mouse model. The male BALB/c 8-weeks old mice (n꞊150) were randomly distributed in three experimental groups. Control group of mice (n꞊50), TBI group (n꞊50) and Trox pre-treated mice group (Trox + TBI, n꞊50). The mice in Trox + TBI were pre-treated with Trox (150 mg/kg, 7 days) before TBI. The weight-drop mechanism was used to induce mild-moderate injury in mice in both the groups. Our results showed that the mice pre-treated with troxerutin significantly improved neurological severity score, blood glucose level, food intake and brain edema as compared to the mice in the TBI group. Furthermore, compared to the TBI group, the mice treated with troxerutin improved cognitive behavior as evaluated by Open field test, Shallow Water Maze and Y-Maze, decreased brain-infarct volume and blood-brain barrier (BBB) permeability, significantly decreased Reactive Oxygen Species (ROS), improved neuronal morphology and survival in the brain regions such as cortex and hippocampus. In summary, our data provided evidence that pre-treatment with troxerutin improved neurological functions, decreased the BBB permeability, improved behavior, reduced ROS and increased neuronal survival in the weight-drop close head traumatic injury mouse model.

Plant-Derived Smoke Mitigates the Inhibitory Effects of the Auxin Inhibitor 2,3,5-Triiodo Benzoic Acid (TIBA) by Enhancing Root Architecture and Biochemical Parameters in Maize.

The present study was designed to investigate and compare the effects of plant-derived smoke (PDS) and auxin (IAA and IBA) on maize growth under the application of 2,3,5-triiodo benzoic acid (TIBA). For this purpose, indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA), each at a concentration of 10 ppm, along with PDS at a ratio of 1:500 (v/v) were used alone and in combination with 10 ppm of TIBA. The results indicate that the germination percentage (%) of maize seeds was enhanced under IAA, IBA and PDS treatment. However, IAA and IBA resulted in reduced germination when applied in combination with TIBA. Importantly, the germination percentage (%) was improved by PDS under TIBA treatment. The analysis of seedling height, length of leaves, and number of primary, seminal and secondary/lateral roots showed improvement under individual treatments of IAA and IBA, PDS and PDS + TIBA treatment, while these values were reduced under IAA + TIBA and IBA + TIBA application. Chlorophyll content, total soluble sugars and antioxidative enzymatic activity including POD and SOD increased in seedlings treated with PDS alone or both PDS and TIBA, while in seedlings treated with IAA and TIBA or IBA and TIBA, their levels were decreased. APX and CAT responded in the opposite way-under IAA, IBA and PDS treatment, their levels were found to be lower than the control (simple water treatment), while TIBA treatment with either IAA, IBA or PDS enhanced their levels as compared to the control. These results reveal that PDS has the potential to alleviate the inhibitory effects of TIBA. This study highlights the role of PDS in preventing TIBA from blocking the auxin entry sites.

Molecular Characterization of spa, hld, fmhA, and lukD Genes and Computational Modeling the Multidrug Resistance of Staphylococcus Species through Callindra harrisii Silver Nanoparticles.

The problem of multidrug resistance in bacterial pathogens is significant and is related to the high morbidity and death rates of living things due to increased levels of beta-lactamases. Plant-derived nanoparticles have gained a great significance in the field of science and technology to combat bacterial diseases, especially multidrug-resistant bacteria. This study examines the multidrug resistance and virulent genes of identified pathogenic Staphylococcus species obtained from Molecular Biotechnology and Bioinformatics Laboratory (MBBL), culture collection. The polymerase chain reaction-based characterization of Staphylococcus aureus and Staphylococcus argenteus having ON875315.1 and ON876003.1 accession IDs revealed the presence of the spa, LukD, fmhA, and hld genes. The green synthesis of silver nanoparticles (AgNPs) was carried out by utilizing the leaf extract of Calliandra harrisii, of which metabolites act as capping and reducing agents for the precursor of nano-synthesis, i.e., AgNO3 of 0.25 M. The synthesized AgNPs were characterized via UV-vis spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy, and energy-dispersive X-ray analysis which inferred the bead-like shape of our nanoparticles with the size of 2.21 nm with the existence of aromatic and hydroxyl functional groups at surface plasmon resonance of 477 nm. The antimicrobial activity by AgNPs showed 20 mm inhibition of Staphylococcus species as compared to the vancomycin and cefoxitin antibiotics along with crude plant extract, which showed a minimum zone of inhibition. The synthesized AgNPs were also analyzed for various biological activities like anti-inflammatory with 99.15% inhibition in protein denaturation, antioxidant with 99.8% inhibition in free radical scavenging, antidiabetic with 90.56% inhibition of alpha amylase assay, and anti-haemolytic with 89.9% inhibition in cell lysis which shows good bioavailability and biocompatibility of the nanoparticles with the biological system of the living being. The amplified genes (spa, LukD, fmhA, and hld) were also analyzed for their interaction with AgNPs computationally at the molecular level. The 3-D structure of AgNP and amplified genes was retrieved from ChemSpider (ID: 22394) and Phyre2 online server, respectively. The binding affinities of AgNP with spa, LukD, fmhA, and hld were -7.16, -6.5, -6.45, and -3.3 kJ/mol, respectively, which infers a good docking score except of hld which is -3.3 kJ/mol due to its small size. The salient features of biosynthesized AgNPs proved to be an effective approach in combating the multidrug-resistant Staphylococcus species in the future.

Insights into the Mechanisms Involved in Lead (Pb) Tolerance in Invasive Plants-The Current Status of Understanding.

Invasive plant species possess remarkable abilities to establish themselves in new environments and to displace native species. Their success can be attributed to various physiological and biochemical mechanisms, allowing them to tolerate adverse environmental conditions, including high lead (Pb) toxicity. Comprehension of the mechanisms responsible for Pb tolerance in invasive plants is still limited, but it is rapidly evolving. Researchers have identified several strategies in invasive plants to tolerate high levels of Pb. This review provides an overview of the current understanding of the ability of invasive species to tolerate or even accumulate Pb in plant tissues, including vacuoles and cell walls, as well as how rhizosphere biota (bacteria and mycorrhizal fungi) help them to enhance Pb tolerance in polluted soils. Furthermore, the article highlights the physiological and molecular mechanisms regulating plant responses to Pb stress. The potential applications of these mechanisms in developing strategies for remediating Pb-contaminated soils are also discussed. Specifically, this review article provides a comprehensive understanding of the current status of research on the mechanisms involved in Pb tolerance in invasive plants. The information presented in this article may be useful in developing effective strategies for managing Pb-contaminated soils, as well as for developing more resilient crops in the face of environmental stressors.

Computational modeling of imines based anti-oxidant and anti-esterases compounds: Synthesis, single crystal and In-vitro assessment.

Molecular modeling strategy was adopted to check the biological potential of the imine based molecules against free radical, acetylcholine esterase and butyrylcholine esterase. Three Schiff based compounds as (E)-2-(((4-bromophenyl)imino)methyl)-4-methylphenol (1), (E)-2-(((3-fluorophenyl)imino)methyl)-4-methylphenol (2) and (2E,2E)-2-(2-(2-hydroxy-5-methylbenzylidene)hydrazono)-1,2-diphenylethanone (3) were synthesized with high yield. The synthesized compounds were characterized with the help of modern techniques such as UV, FTIR and NMR while exact structure was depicted with Single Crystal X-Ray diffraction technique which disclosed that compound 1 is orthorhombic, while 2 and 3 are monoclinic. A hybrid functional (B3LYP) method with general basis set of 6-31 G(d,p) were applied to optimize synthesized Schiff bases. The contribution of in-between molecular contacts within a crystalline assembly of compounds were studied using Hirshfeld surface analysis (HS). In order to check the ability of the synthesized compounds toward free radical and enzyme inhibition, in vitro models were used to assess the radical scavenging and enzyme inhibition potential which depicted that compound 3 showed highest potential (57.43 ± 1.0%; DPPH, 75.09 ± 1.0%; AChE and 64.47 ± 1.0%; BChE). The ADMET assessments suggested the drug like properties of the synthesized compounds. It was concluded from results (in vitro and in silico) that synthesized compound have ability to cure the disorder related to free radical and enzyme inhibition. Compound 3 was shown to be the most active compared to other compounds.

Biosynthesis and Mathematical Interpretation of Zero-Valent Iron NPs Using Nigella sativa Seed Tincture for Indemnification of Carcinogenic Metals Present in Industrial Effluents.

Zero-valent iron nanoparticles (ZVI-NPs) are utilized for the indemnification of a wide range of environmental pollutants. Among the pollutants, heavy metal contamination is the major environmental concern due to their increasing prevalence and durability. In this study, heavy metal remediation capabilities are determined by the green synthesis of ZVI-NPs using aqueous seed extract of Nigella sativa which is a convenient, environmentally friendly, efficient, and cost-effective technique. The seed extract of Nigella sativa was utilized as a capping and reducing agent for the generation of ZVI-NPs. UV-visible spectrophotometry (UV-vis), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX), and Fourier transform infrared spectroscopy (FTIR) was used to investigate the ZVI-NP composition, shape, elemental constitution, and perspective functional groups, respectively. The biosynthesized ZVI-NPs displayed a peak of plasmon resonance spectra at 340 nm. The synthesized NPs were cylindrical in shape, with a size of 2 nm and (-OH) hydroxyl, (C-H) alkanes and alkynes N-C, N=C, C-O, =CH functional groups attached to the surface of ZVI-NPs. Heavy metals were successfully remediated from industrial wastewater collected from the various tanneries of Kasur. During the reaction duration of 24 h, different concentrations of ZVI-NPs (10 µg, 20 µg and 30 µg) per 100 mL were utilized for the removal of heavy metals from industrial wastewater. The 30 µg/100 mL of ZVI-NPs proved the pre-eminent concentration of NPs as it removed >90% of heavy metals. The synthesized ZVI-NPs were analyzed for compatibility with the biological system resulting in 87.7% free radical scavenging, 96.16% inhibition of protein denaturation, 60.29% and 46.13% anti-cancerism against U87-MG and HEK 293 cell lines, respectively. The physiochemical and exposure mathematical models of ZVI-NPs represented them as stable and ecofriendly NPs. It proved that biologically synthesized NPs from a seed tincture of Nigella sativa have a strong potential to indemnify heavy metals found in industrial effluent samples.