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Peer-led Team Learning (PLTL) is a model of instruction and learning that has been used to address low success rates in various science, technology, engineering, and mathematics (STEM) courses and other fields in higher education. (1) While research conveying the efficacy of this model of instruction remains mixed, this project sought to add to the body of knowledge communicating the benefits of PLTL for STEM achievement. This study examined the experiences of STEM community college transfer students’ engagement in PLTL within their major course of study when a suite of supports was provided. (2) Specifically, study participants were enrolled in degree programs at a four-year rural, regional university in the engineering, computer science, and mathematics (ECSM) fields and were provided social, financial, and academic assistance. Qualitative analysis of the data revealed that with a PLTL component, community college transfer students were more likely to participate, engage, and perform better in coursework given the additional supports. (3) Study participants indicated the community of learners created through PLTL was a primary motivator for continued engagement and progress towards ECSM degree attainment. (4) Implications for practice extend to engagement and retention efforts of STEM students from the community college population

High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less spatial coverage, lower signal-to-noise ratio (SNR), and longer scan time due to physical, physiological and hardware limitations. In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. The public IXI dataset (only structural images) was used. Data were artificially downsampled to obtain lower-resolution spatial MRIs (downsampling factor varying from 8 to 64). When assessing performance using the SSIM metric in the test set, all models performed well. In particular, regardless of the downsampling factor, the UNet consistently obtained the top results. On the other hand, the SPSR model consistently performed worse. In conclusion, UNet and UNet-MSS achieved overall top performances while RRDB performed relatively poorly compared to the other models.

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a persistent organic pollutant and a potent aryl hydrocarbon receptor (AHR) ligand, causes delayed intestinal motility and affects the survival of enteric neurons. In this study, we investigated the specific signaling pathways and molecular targets involved in TCDD-induced enteric neurotoxicity. Immortalized fetal enteric neuronal (IM-FEN) cells treated with 10 nM TCDD exhibited cytotoxicity and caspase 3/7 activation, indicating apoptosis. Increased cleaved caspase-3 expression with TCDD treatment, as assessed by immunostaining in enteric neuronal cells isolated from WT mice but not in neural crest cell-specific Ahr deletion mutant mice (Wnt1Cre+/−/Ahrb(fl/fl)), emphasized the pivotal role of AHR in this process. Importantly, the apoptosis in IM-FEN cells treated with TCDD was mediated through a ceramide-dependent pathway, independent of endoplasmic reticulum stress, as evidenced by increased ceramide synthesis and the reversal of cytotoxic effects with myriocin, a potent inhibitor of ceramide biosynthesis. We identified Sptlc2 and Smpd2 as potential gene targets of AHR in ceramide regulation by a chromatin immunoprecipitation (ChIP) assay in IM-FEN cells. Additionally, TCDD downregulated phosphorylated Akt and phosphorylated Ser9-GSK-3β levels, implicating the PI3 kinase/AKT pathway in TCDD-induced neurotoxicity. Overall, this study provides important insights into the mechanisms underlying TCDD-induced enteric neurotoxicity and identifies potential targets for the development of therapeutic interventions.

Thiosemicarbazones and their metal complexes have been studied for their biological activities against bacteria, cancer cells and protozoa. Short-term in vitro treatment with one gold (III) complex (C3) and its salicyl-thiosemicarbazone ligand (C4) selectively inhibited proliferation of T. gondii. Transmission Electron Microscopy (TEM) detected transient structural alterations in the parasitophorous vacuole membrane and the tachyzoite cytoplasm, but the mitochondrial membrane potential appeared unaffected by these compounds. Proteins potentially interacting with C3 and C4 were identified using differential affinity chromatography coupled with mass spectrometry (DAC-MS). Moreover, long-term in vitro treatment was performed to investigate parasitostatic or parasiticidal activity of the compounds. DAC-MS identified 50 ribosomal proteins binding both compounds, and continuous drug treatments for up to 6 days caused the loss of efficacy. Parasite tolerance to both compounds was, however, rapidly lost in their absence and regained shortly after re-exposure. Proteome analyses of six T. gondii ME49 clones adapted to C3 and C4 compared to the non-adapted wildtype revealed overexpression of ribosomal proteins, of two transmembrane proteins involved in exocytosis and of an alpha/beta hydrolase fold domain-containing protein. Results suggest that C3 and C4 may interfere with protein biosynthesis and that adaptation may be associated with the upregulated expression of tachyzoite transmembrane proteins and transporters, suggesting that the in vitro drug tolerance in T. gondii might be due to reversible, non-drug specific stress-responses mediated by phenotypic plasticity.

The prevalence of anaemia in India remains high in children, especially those in rural areas, and in women of childbearing age, and its impairment of neurological development can have serious lifelong effects. It is concerning that the most recent official data (2019–21) indicate an increased prevalence compared with 2015–16. There is also considerable variability in childhood anaemia between Indian states with socioeconomic factors, such as wealth and education contributing to the risk of anaemia among adolescent women and their children. Dietary iron deficiency is often regarded as the main contributor to anaemia but increasing evidence accumulated from the authors’ ongoing literature database coupled with recent literature research suggests that it has a multifactorial aetiology, some of which is not related to nutrition. This narrative review focused on these multifactorial issues, notably the contribution of vitamin B12/folate deficiency, which also has a high prevalence in India. It was also noted that the dietary intake of bioavailable iron remains an important contributor for reducing anaemia, and the role of millets as an improved iron source compared to traditional staple cereals is briefly discussed. The overall conclusion is that anaemia has a multifactorial aetiology requiring multifactorial assessment that must include assessment of vitamin B12 status.

Photosynthesis is a process where solar energy is utilized to convert atmospheric CO2 into carbohydrates, which forms the basis for plant productivity. The increasing demand for food has created a global urge to enhance yield. Earlier, the plant breeding program was targeting the yield and yield-associated traits to enhance the crop yield. However, the yield cannot be further improved without improving the leaf photosynthetic rate. Hence, in this review, various strategies to enhance leaf photosynthesis were presented. The most promising strategies were the optimization of Rubisco carboxylation efficiency, the introduction of a CO2 concentrating mechanism in C3 plants, and the manipulation of photorespiratory bypasses in C3 plants, which are discussed in detail. Improving Rubisco’s carboxylation efficiency is possible by engineering targets such as Rubisco subunits, chaperones, and Rubisco activase enzyme activity. Carbon-concentrating mechanisms can be introduced in C3 plants by the adoption of pyrenoid and carboxysomes, which can increase the CO2 concentration around the Rubisco enzyme. Photorespiration is the process by which the fixed carbon is lost through an oxidative process. Different approaches to reduce carbon and nitrogen loss were discussed. Overall, the potential approaches to improve the photosynthetic process and the way forward were discussed in detail.

A community-level nutritional intervention was implemented among tribal children (3 to 6 years of age) in Telangana, India. The one-year intervention involved six nutrient-rich formulations of millet–pulse–groundnut-based products suited to local taste preferences. Anthropometric measurements of height, weight, and mid-upper-arm circumference (MUAC) along with haemoglobin (Hb) levels were monitored at baseline and endline. The treatment group showed considerable gains in height (3.2 cm), weight (1.68 kg), and MUAC (0.33 cm) over the control group. The paired t-test indicated significant differences (p < 0.01) between the pre- and post-intervention anthropometric measurements. Positive shifts were observed in terms of wasting (WHZ; −1.2 ± 1.3 to −0.9 ± 1), stunting (HAZ; −1.8 ± 1.6 to −0.3 ± 1.3), and underweight (WAZ; −1.9 ± 1.2 to −0.7 ± 1) in the treatment group. The Hb levels in the treatment group also improved significantly from 9.70 ± 0.14 g/dL (moderately anaemic) to 11.08 ± 0.13 g/dL (non-anaemic). Post-intervention focus group discussions (FGDs) involving mothers and teachers confirmed these positive impacts. Thus, a nutritional intervention formulated using climate-resilient millets, pulses, and groundnuts promotes dietary diversity and improves the nutrition and health statuses of children.

An inflammation-resolving polysialic acid-decorated PLGA nanoparticle (PolySia-NP) has been developed to treat geographic atrophy/age-related macular degeneration and other conditions caused by macrophage and complement over-activation. While PolySia-NPs have demonstrated pre-clinical efficacy, this study evaluated its systemic and intraocular safety. PolySia-NPs were evaluated in vitro for mutagenic activity using Salmonella strains and E. coli, with and without metabolic activation; cytotoxicity was evaluated based on its interference with normal mitosis. PolySia-NPs were administered intravenously in CD-1 mice and Sprague Dawley rats and assessed for survival and toxicity. Intravitreal (IVT) administration in Dutch Belted rabbits and non-human primates was assessed for ocular or systemic toxicity. In vitro results indicate that PolySia-NPs did not induce mutagenicity or cytotoxicity. Intravenous administration did not show clastogenic activity, effects on survival, or toxicity. A single intravitreal (IVT) injection and two elevated repeat IVT doses of PolySia-NPs separated by 7 days in rabbits showed no signs of systemic or ocular toxicity. A single IVT inoculation of PolySia-NPs in non-human primates demonstrated no adverse clinical or ophthalmological effects. The demonstration of systemic and ocular safety of PolySia-NPs supports its advancement into human clinical trials as a promising therapeutic approach for systemic and retinal degenerative diseases caused by chronic immune activation.

The alternative pathway of the complement system is implicated in the etiology of age-related macular degeneration (AMD). Complement depletion with pegcetacoplan and avacincaptad pegol are FDA-approved treatments for geographic atrophy in AMD that, while effective, have clinically observed risks of choroidal neovascular (CNV) conversion, optic neuritis, and retinal vasculitis, leaving room for other equally efficacious but safer therapeutics, including Poly Sialic acid (PSA) nanoparticle (PolySia-NP)-actuated complement factor H (CFH) alternative pathway inhibition. Our previous paper demonstrated that PolySia-NP inhibits pro-inflammatory polarization and cytokine release. Here, we extend these findings by investigating the therapeutic potential of PolySia-NP to attenuate the alternative complement pathway. First, we show that PolySia-NP binds CFH and enhances affinity to C3b. Next, we demonstrate that PolySia-NP treatment of human serum suppresses alternative pathway hemolytic activity and C3b deposition. Further, we show that treating human macrophages with PolySia-NP is non-toxic and reduces markers of complement activity. Finally, we describe PolySia-NP-treatment-induced decreases in neovascularization and inflammatory response in a laser-induced CNV mouse model of neovascular AMD. In conclusion, PolySia-NP suppresses alternative pathway complement activity in human serum, human macrophage, and mouse CNV without increasing neovascularization.

The monitoring of machining has been the focus of widespread research in recent years because of its substantial contribution to manufacturing process automation. To maximise productivity, the spindle speed and feed have been increased, which has increased noise and temperature generation in the workpiece and tool. Such loud noise levels are uncomfortable for the operators, create stress, and have negative health effects, which reduces productivity. Controlling the temperature and noise produced during milling is crucial. Among its several series, the aluminium alloy 7075 (AA7075) is one of the strong alloys utilised in aircraft structural components. Hence, the present work examines the effect of spindle speed, feed, and depth of cut on noise and temperature generation during the end-milling of an AA7075 plate with 0.25-inch thickness. The sound and temperature were measured for both up- and down-milling processes and compared. In up-milling, the temperature and noise varied from 32.9 to 50.6 °C and 87.5 to 95 dB and the same were varied from 30.6 to 36.2 °C and 89.6 to 93.9 dB for down-milling. Finally, it was recommended that down-milling is the best process with low noise and temperature generation.

With over 9 million additional tons of plastic waste entering the oceans each year, 165 million tons of plastic waste have already defiled our oceans. Considering that just 9% of plastic is recycled, the rest of the waste contaminates the environment or is dumped in landfills, where it can take up to 500 years to degrade while releasing hazardous chemicals into the soil. The bioplastics prepared in this study were obtained from tapioca and sweet potato using plasticizers like glycerol in various combinations. The biodegradability and mechanical and physical properties were analyzed. The tensile strengths of the prepared bioplastic films were 4.25 and 2.35 MPa with elongation at break (EAB) of 39.5% and 25.4% for tapioca and sweet potato bioplastics, respectively. The biodegradability of the films was examined, and thus, the obtained bioplastics in the study are shown to be a good alternative to the existing plastics.

This work investigates the stick–slip phenomenon during sliding motion between solid lubricant-impregnated epoxy polymer-coated steel bars and AISI 52,100 steel balls. An acoustic sensor detected the stick–slip phenomenon during the tribo-pair interaction. The wear characteristics of the workpiece coated with different epoxy coatings were observed and scrutinized. The RMS values of the acoustic sensor were correlated with the frictional coefficient to develop a standard based on the acoustic sensor, leading to the detection of the stick–slip phenomenon. As per the findings, the acoustic waveform remained relatively similar to the friction coefficient observed during the study and can be used effectively in detecting the stick–slip phenomenon between steel and polymer interaction. This work will be highly beneficial in industrial and automotive applications with a significant interaction of polymer and steel surfaces.

Objective: This study investigated the relationship between chronic obstructive pulmonary disease (COPD) and periodontitis, focusing on how periodontal health impacts COPD airflow limitation, exacerbations, and hospitalization. Background: Periodontitis, a multifactorial inflammatory disease, is characterized by destruction of tooth-supporting structures, while COPD is a global pulmonary disorder with high mortality. Methods: A total of 199 COPD patients aged over 40 years underwent lung function tests (spirometry), 6 min walk test, and St George’s Respiratory Questionnaire-COPD (SGRQ-C) to assess lung health. Periodontal indices such as probing depth (PD), clinical attachment loss (CAL), and plaque index (PI) were assessed. Results: We found a significant negative correlation between periodontal disease severity and lung function (lower FEV1, FVC, and FEV1/FVC ratio) after adjusting for smoking. Likewise, periodontal parameters (PPD, PI, and CAL) exhibited negative correlations with lung function. These periodontal indices were independently associated with airflow limitation severity, exacerbations frequency, and prior-year hospitalization. Linear regression indicated that each unit increase in PPD, PI, and CAL corresponded to estimated increases in GOLD airflow limitation grading (0.288, 0.718, and 0.193, respectively) and number of exacerbations (0.115, 0.041, and 0.109, respectively). In logistic regression, PPD, PI, and CAL adjusted odds ratios (ORs) were estimated to increase by 1.29 (95%CI: 1.03–1.62), 3.04 (95%CI: 1.28–7.2), and 1.26 (95%CI: 1.06–1.49), respectively, for hospitalization in previous year. Conclusion: Periodontitis is associated with COPD airflow limitation, exacerbation, and hospitalization, with PI being the most clinically relevant periodontal factor. Dentists and physicians should monitor and increase awareness among COPD patients to maintain oral hygiene for prevention of periodontal diseases and mitigate its effect on COPD progression.

Age-related macular degeneration (AMD), a leading cause of visual loss and dysfunction worldwide, is a disease initiated by genetic polymorphisms that impair the negative regulation of complement. Proteomic investigation points to altered glycosylation and loss of Siglec-mediated glyco-immune checkpoint parainflammatory and inflammatory homeostasis as the main determinant for the vision impairing complications of macular degeneration. The effect of altered glycosylation on microglial maintained retinal para-inflammatory homeostasis and eventual recruitment and polarization of peripheral blood monocyte-derived macrophages (PBMDMs) into the retina can explain the phenotypic variability seen in this clinically heterogenous disease. Restoring glyco-immune checkpoint control with a sialic acid mimetic agonist targeting microglial/macrophage Siglecs to regain retinal para-inflammatory and inflammatory homeostasis is a promising therapeutic that could halt the progression of and improve visual function in all stages of macular degeneration.

Heart rate estimation from face videos is an emerging technology that offers numerous potential applications in healthcare and human–computer interaction. However, most of the existing approaches often overlook the importance of long-range spatiotemporal dependencies, which is essential for robust measurement of heart rate prediction. Additionally, they involve extensive pre-processing steps to enhance the prediction accuracy, resulting in high computational complexity. In this paper, we propose an innovative solution called LGTransPPG. This end-to-end transformer-based framework eliminates the need for pre-processing steps while achieving improved efficiency and accuracy. LGTransPPG incorporates local and global aggregation techniques to capture fine-grained facial features and contextual information. By leveraging the power of transformers, our framework can effectively model long-range dependencies and temporal dynamics, enhancing the heart rate prediction process. The proposed approach is evaluated on three publicly available datasets, demonstrating its robustness and generalizability. Furthermore, we achieved a high Pearson correlation coefficient (PCC) value of 0.88, indicating its superior efficiency and accuracy between the predicted and actual heart rate values.

Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.

This paper presents a study on a memristive two-neuron-based Hopfield neural network with fractional-order derivatives. The equilibrium points of the system are identified, and their stability is analyzed. Bifurcation diagrams are obtained by varying the magnetic induction strength and the fractional-order derivative, revealing significant changes in the system dynamics. It is observed that lower fractional orders result in an extended bistability region. Also, chaos is only observed for larger magnetic strengths and fractional orders. Additionally, the application of the fractional-order model for image encryption is explored. The results demonstrate that the encryption based on the fractional model is efficient with high key sensitivity. It leads to an encrypted image with high entropy, neglectable correlation coefficient, and uniform distribution. Furthermore, the encryption system shows resistance to differential attacks, cropping attacks, and noise pollution. The Peak Signal-to-Noise Ratio (PSNR) calculations indicate that using a fractional derivative yields a higher PSNR compared to an integer derivative.

Upgrading abradable or wearable coatings in the high-temperature zone of aero engines is advised to increase the efficiency and high-density power in gas turbine engines for military or commercial fixed-wing and rotary-wing aircraft. The development of these coated materials is also motivated by minimization of the number of failures in the blade, as well as increasing their resistance to wear and erosion. It is suggested that abradable coatings or seals be used to accomplish this goal. The space between the rotor and the shroud is minimized thanks to an abradable seal at the blade’s tip. Coatings that can withstand abrasion are often multiphase materials sprayed through thermal spray methods, and which consist of a metal matAzmeerix, oxide particles, and void space. The maintenance of an ideal blend of qualities, such as erosion resistance and hardness, during production determines a seal’s effectiveness. The objective of this research is to develop microstructure-based modelling methodology which will mimic the coating wear process and subsequently help in designing the abradable coating composition. Microstructure modelling, meshing, and wear analysis using many tools such as Fusion360, Hyper Mesh, and LS-Dyna, have been employed to develop an abradable coating model and perform wear analysis using a simulated rub rig test. The relation between percentage composition and morphology variations of metal, oxide, and voids to the output parameters such as hardness, abradability, and other mechanical properties is explored using simulated finite analysis models of real micrographic images of abradable coatings.

Melatonin (MT), a naturally occurring compound, is found in various species worldwide. In 1958, it was first identified in the pineal gland of dairy cows. MT is an “old friend” but a “new compound” for plant biology. It brings experts and research minds from the broad field of plant sciences due to its considerable influence on plant systems. The MT production process in plants and animals is distinct, where it has been expressed explicitly in chloroplasts and mitochondria in plants. Tryptophan acts as the precursor for the formation of phyto-melatonin, along with intermediates including tryptamine, serotonin, N-acetyl serotonin, and 5-methoxy tryptamine. It plays a vital role in growth phases such as the seed germination and seedling growth of crop plants. MT significantly impacts the gas exchange, thereby improving physio-chemical functions in plant systems. During stress, the excessive generation and accumulation of reactive oxygen species (ROS) causes protein oxidation, lipid peroxidation, nucleic acid damage, and enzyme inhibition. Because it directly acts as an antioxidant compound, it awakens the plant antioxidant defense system during stress and reduces the production of ROS, which results in decreasing cellular oxidative damage. MT can enhance plant growth and development in response to various abiotic stresses such as drought, salinity, high temperature, flooding, and heavy metals by regulating the antioxidant mechanism of plants. However, these reactions differ significantly from crop to crop and are based on the level and kind of stress. The role of MT in the physiological functions of plants towards plant growth and development, tolerance towards various abiotic stresses, and approaches for enhancing the endogenous MT in plant systems are broadly reviewed and it is suggested that MT is a steering compound in directing major physiological functions of plants under the changing climate in future.

Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in recent years. Recent developments in deep learning and transfer learning approaches have attracted huge attention in addressing this challenging area. In our work, we have proposed a fine-tuned deep transfer learning approach using DenseNet201 for feature extraction and a deep Liquid State Machine model as the classifier in order to develop a model for recognising and understanding indoor scenes. We have included fuzzy colour stacking techniques, colour-based segmentation, and an adaptive World Cup optimisation algorithm to improve the performance of our deep model. Our proposed model would dedicatedly assist the visually impaired and blind to navigate in the indoor environment and completely integrate into their day-to-day activities. Our proposed work was implemented on the NYU depth dataset and attained an accuracy of 96% for classifying the indoor scenes.

Studying simple chaotic systems with fractional-order derivatives improves modeling accuracy, increases complexity, and enhances control capabilities and robustness against noise. This paper investigates the dynamics of the simple Sprott-B chaotic system using fractional-order derivatives. This study involves a comprehensive dynamical analysis conducted through bifurcation diagrams, revealing the presence of coexisting attractors. Additionally, the synchronization behavior of the system is examined for various derivative orders. Finally, the integer-order and fractional-order electronic circuits are implemented to validate the theoretical findings. This research contributes to a deeper understanding of the Sprott-B system and its fractional-order dynamics, with potential applications in diverse fields such as chaos-based secure communications and nonlinear control systems.

Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.

T-cell PTLDs are lymphoid proliferations that develop in recipients of SOT or allogeneic HSCT. They carry an extremely poor prognosis with a reported median survival of only 6 months. The infrequency with which they are encountered makes treatment a challenge due to the lack of prospective trials to guide management. The significantly higher risk of morbidity and mortality in T-cell PTLD, compared to B-cell PTLD, underscores the challenge of treating these patients and the need for new therapeutic options. Brentuximab vedotin, an ADC targeting CD30, is FDA-approved in combination with CHP as front-line treatment for patients with CD30 expressing PTCL. Herein we report a case of CD30-positive T-cell PTLD that was successfully treated with BV-CHP, suggesting the added value of the addition of BV to chemotherapy, contributing to our patient’s long and ongoing progression-free survival. To our knowledge, this is the first documented case of successful treatment using BV-CHP for a CD30-positive, EBV-negative, late T-cell PTLD.

According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.

Dye-sensitized solar cells have been under development for the last three decades but are yet to see the market. This has been attributed to stability issues of the electrolyte in the cell. Electrolytes can be liquid, quasi-solid, or solid. Liquid electrolytes were the first to be developed and, therefore, have been subject to radical revisions in both composition and applicability. They have shown the best power conversion efficiencies but have poor thermal stability. Although quasi-solid and solid-state electrolytes were developed to overcome these stability issues, they too have their limits. The aim of this paper is to explore the development of liquid electrolytes, outlining the current state of the technology and considering their potential in the photovoltaic market.

Mung bean, a legume, is sensitive to abiotic stresses at different growth stages, and its yield potential is affected by drought and high-temperature stress at the sensitive stage. Melatonin is a multifunctional hormone that plays a vital role in plant stress defense mechanisms. This study aimed to evaluate the efficiency of melatonin under individual and combined drought and high-temperature stress in mung bean. An experiment was laid out with five treatments, including an exogenous application of 100 µM melatonin as a seed treatment, foliar spray, and a combination of both seed treatment and foliar spray, as well as absolute control (ambient condition) and control (stress without melatonin treatment). Stresses were imposed during the mung bean’s reproductive stage (31–40 DAS) for ten days. Results revealed that drought and high-temperature stress significantly decreased chlorophyll index, Fv/Fm ratio, photosynthetic rate, stomatal conductance, and transpiration rate through increased reactive oxygen species (ROS) production. Foliar application of melatonin at 100 µM concentration enhanced the activity of antioxidant enzymes such as superoxide dismutase, catalase, and ascorbate peroxidase and the concentration of metabolites involved in osmoregulation and ion homeostasis; thereby, it improves physiological and yield-related traits in mung bean under individual and combined stress at the reproductive stage.

Ecosystems worldwide face a serious and life-threatening water crisis due to water contamination. Nanotechnology offers a promising solution to this issue by providing methods for removing pollutants from aquatic sources. In this study, we utilized a green and simple approach to biosynthesize NiSe NPs using Hibiscus rosa-sinensis extract as the bio-source. The plant extract acts as a reducing, stabilizing, and capping agent in the synthesis process. A simple hydrothermal method was employed to blend the NiSe NPs photocatalysts. UV-Visible DRS spectroscopy was utilized to confirm the reduction in and stabilization of Ni2+ and Se2− ions. The resulting NiSe NPs have a bandgap of 1.74 eV, which facilitates electron and hole production on their surfaces. To characterize the functional groups on the NiSe NPs and their surface interactions with bio-compounds, FTIR spectroscopy was utilized. XRD analysis revealed the crystallite size of the NiSe NPs to be 24 nm, while FE-SEM and TEM imaging showed their spherical shape and material distribution. EDX spectroscopy confirmed the integrity of the NiSe NPs’ material. XPS analysis provided information on the chemical composition, nickel and selenium valency, and their interface. The efficacy of the NiSe NPs as a blended photocatalyst in photodegrading Methylene Blue (MB) dye was tested under visible light, resulting in 92% degradation. Furthermore, the NiSe NPs exhibited bactericidal activity against Escherichia coli and Staphylococcus aureus bacteria due to their advanced oxidation and reduction in charge particles, which increased the degradation efficiency and suppressed cell proliferation. Based on the obtained findings, the NiSe NPs show promise as a powerful agent for water remediation and microbial resistance.

Groundwater is the most abundant freshwater resource. Agriculture, industrialization, and domestic water supplies rely on it. The depletion of groundwater leads to drought. Topographic elevation, aquifer properties, and geomorphology influence groundwater quality. As the groundwater level data (GWL) are time series in nature, it is challenging to determine appropriate metrics and to evaluate groundwater levels accurately with less information loss. An effort has been made to forecast groundwater levels in India by developing a deep ensemble learning approach using a double-edge bi-directed long-short-term-memory (DEBi-LSTM) model approximated with a randomized low-ranked approximation algorithm (RLRA) and the variance inflation factor (VIF) to reduce information loss and to preserve data consistency. With minimal computation time, the model outperformed existing state-of-the-art models with 96.1% accuracy. To ensure sustainable groundwater development, the proposed work is discussed in terms of its managerial implications. By applying the model, we can identify safe, critical, and semi-critical groundwater levels in Indian states so that strategic plans can be developed.

The discrete system serves an important role in mimicking collective dynamics found in continuous dynamical systems, which are relevant to many realistic natural and artificial systems. To investigate the dynamical transition of a discrete system, we employ three-dimensional sinusoidal discrete maps with an additional self feedback factor. Specifically, we focus on dynamical transitions with respect to the bifurcation parameter, sine function amplitude, and intensity of self feedback factors. We demonstrate the presence of symmetry in relation to parametric variation using two parameter diagrams. The study is then expanded to the network of sine maps in the presence of self-feedback factor. We discover that negative feedback exhibits the transition from cluster state to synchronization while raising the coupling strength for small-world network interactions. Furthermore, increasing feedback from negative to positive causes the transition from synchronization to desynchronization via chimera state for various complex network connectivities.

In this research, the porous polymer structures (IPN) were made from natural isoprene rubber (NR) and poly(methyl methacrylate) (PMMA). The effects of molecular weight and crosslink density of polyisoprene on the morphology and miscibility with PMMA were determined. Sequential semi-IPNs were prepared. Viscoelastic, thermal and mechanical properties of semi-IPN were studied. The results showed that the key factor influencing the miscibility in semi-IPN was the crosslinking density of the natural rubber. The degree of compatibility was increased by doubling the crosslinking level. The degree of miscibility at two different compositions was compared by simulations of the electron spin resonance spectra. Compatibility of semi-IPNs was found to be more efficient when the PMMA content was less than 40 wt.%. A nanometer-sized morphology was obtained for a NR/PMMA ratio of 50/50. Highly crosslinked elastic semi-IPN followed the storage modulus of PMMA after the glass transition as a result of certain degree of phase mixing and interlocked structure. It was shown that the morphology of the porous polymer network could be easily controlled by the proper choice of concentration and composition of crosslinking agent. A dual phase morphology resulted from the higher concentration and the lower crosslinking level. This was used for developing porous structures from the elastic semi-IPN. The mechanical performance was correlated with morphology, and the thermal stability was comparable with respect to pure NR. Investigated materials might be interesting for use as potential carriers of bioactive molecules aimed for innovative applications such as in food packaging.

Currently, we lack crucial knowledge on how the physicochemical properties of particles affect cellular health, resulting in an important gap in our understanding of the human toxicity of microplastics (MPs). Our aim was to evaluate the impact of the size and the shape of MPs on uptake and the intracellular effects in a human epithelial colorectal adenocarcinoma (Caco-2) cell line. Spherical (200 nm and 2 µm) and fibre-/fragment-shaped (8.9 ± 10.1 µm by 1.14 ± 0.97 µm) polystyrene microplastics (PS-MPs) were used to study their uptake and the potential to induce redox and mitochondrial stress responses after 24 h of exposure. We demonstrated the cellular uptake of both spherical and fibre-/fragment-shaped MPs in a size-dependent manner. In response to 2 µm spheres, we observed differential expressions of redox-related genes, including HMOX1, CAT, and GPX1. All PS-MPs decreased the intracellular H2O2 levels, which can be attributed to mitochondrial stress responses, such as increased mitochondrial DNA content, footprint, and morphology. Altogether, we demonstrated uptakes and changes in redox and mitochondrial parameters for all PS-MPs, with the 200 nm spheres showing the most profound effects. This suggests that the induction of defensive responses in Caco-2 cells mainly correlates with the number of particles taken up.

Biobased and biodegradable polyhydroxyalkanoates (PHAs) are currently gaining momentum. Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) polymer has a useful processing window for extrusion and injection molding of packaging, agricultural and fishery applications with required flexibility. Processing PHBHHx into fibers using electrospinning or centrifugal fiber spinning (CFS) can further broaden the application area, although CFS remains rather unexplored. In this study, PHBHHx fibers are centrifugally spun from 4–12 wt.% polymer/chloroform solutions. Beads and beads-on-a-string (BOAS) fibrous structures with an average diameter (ϕav) between 0.5 and 1.6 µm form at 4–8 wt.% polymer concentrations, while more continuous fibers (ϕav = 3.6–4.6 µm) with few beads form at 10–12 wt.% polymer concentrations. This change is correlated with increased solution viscosity and enhanced mechanical properties of the fiber mats (strength, stiffness and elongation values range between 1.2–9.4 MPa, 11–93 MPa, and 102–188%, respectively), though the crystallinity degree of the fibers remains constant (33.0–34.3%). In addition, PHBHHx fibers are shown to anneal at 160 °C in a hot press into 10–20 µm compact top-layers on PHBHHx film substrates. We conclude that CFS is a promising novel processing technique for the production of PHBHHx fibers with tunable morphology and properties. Subsequent thermal post-processing as a barrier or active substrate top-layer offers new application potential.

Nanostructured materials have gained much attention in recent engineering and material- science research due to their unique structural makeup, which stands them out from their bulk counterparts. Their novel properties of tiny-size structural elements (molecules or crystallites, clusters) of nanoscale dimensions (1 to 100 nm) make them a perfect material for energy applications. The recent keen interest in nanostructured materials research by academia and industrial experts arises from the unique variable characteristics of increased electrical and thermal conductivity. This occurs as nanostructured materials undergo a transient process from infinite-extended solid to a particle of ascertainable numbers of atoms. The commercial and energy sectors are very interested in developing and expanding simple synthetic pathways for nanostructured-electrocatalysts materials to aid in optimizing the number of active regions. Over the decades, various techniques have been put forward to design and synthesize nanostructured-electrocatalysts materials for electrochemical generation of energy and storage applications. As a result, the design of fuel cells, supercapacitors, and energy-storage devices has advanced significantly. This review provides a comprehensive outlook of various synthesis techniques and highlight the challenges of nanostructured- electrocatalysts materials application in fuel cells. Several synthesis methods are discussed and summarized for enhanced nanomaterial preparation and high product attainment with the sol-gel synthesis method being emphasized. The design methodology for an effective nanostructured electrocatalysts with high efficiency for fuel cells was also discussed.

Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient’s health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reducse the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.

A numerical simulation of convective heat transfer coefficient (hconv) was studied with Cu-Water and TiO2-Water nanofluids flowing through a circular tube subjected to uniform wall heat flux boundary conditions under laminar and turbulent regimes. Four different concentrations of nanofluids (ɸ = 0.5, 1, 1.5 and 2%) were used for the analysis and the Reynolds number (Re) was varied from laminar (500 to 2000) to turbulent flow regime (5000 to 20,000). The dependence of hconv on Re and ɸ was investigated using a single-phase Newtonian approach. In comparison to base fluid, average hconv enhancements of 10.4% and 7.3% were noted, respectively, for the maximum concentration (ɸ = 2%) and Re = 2000 for Cu-Water and TiO2—water nanofluids in the laminar regime. For the same ɸ under the turbulent regime (Re = 20,000), the enhancements were noted to be 14.6% and 13.2% for both the nanofluids, respectively. The random motion (Brownian motion) and heat diffusion (thermophoresis) by nanosized particles are the two major slip mechanisms that have more influence on the enhancement of hconv. In addition, the Nusselt number (Nu) of the present work was validated for water with the Shah and Dittus Boelter equation and found to have good agreement for both the regimes.

Cancer of the skin is by far the most common of all cancers. Although the incidence of melanoma is relatively low among skin cancers, it can account for a high number of skin cancer deaths. Since the start of deeper insight into the mechanisms of melanoma tumorigenesis and their strong interaction with the immune system, the development of new therapeutical strategies has been continuously rising. The high number of melanoma cell mutations provides a diverse set of antigens that the immune system can recognize and use to distinguish tumor cells from normal cells. Peptide-based synthetic anti-tumor vaccines are based on tumor antigens that elicit an immune response due to antigen-presenting cells (APCs). Although targeting APCs with peptide antigens is the most important assumption for vaccine development, peptide antigens alone are poorly immunogenic. The immunogenicity of peptide antigens can be improved not only by synthetic modifications but also by the assistance of adjuvants and/or delivery systems. The current review summarizes the different chemical approaches for the development of effective peptide-based vaccines for the immunotherapeutic treatment of advanced melanoma.

The Marburg and Ebola filoviruses cause a severe, often fatal, disease in humans and nonhuman primates but have only subclinical effects in bats, including Egyptian rousettes, which are a natural reservoir of Marburg virus. A fundamental question is why these viruses are highly pathogenic in humans but fail to cause disease in bats. To address this question, we infected one cohort of Egyptian rousette bats with Marburg virus and another cohort with Ebola virus and harvested multiple tissues for mRNA expression analysis. While virus transcripts were found primarily in the liver, principal component analysis (PCA) revealed coordinated changes across multiple tissues. Gene signatures in kidney and liver pointed at induction of vasodilation, reduction in coagulation, and changes in the regulation of iron metabolism. Signatures of immune response detected in spleen and liver indicated a robust anti-inflammatory state signified by macrophages in the M2 state and an active T cell response. The evolutionary divergence between bats and humans of many responsive genes might provide a framework for understanding the differing outcomes upon infection by filoviruses. In this study, we outline multiple interconnected pathways that respond to infection by MARV and EBOV, providing insights into the complexity of the mechanisms that enable bats to resist the disease caused by filoviral infections. The results have the potential to aid in the development of new strategies to effectively mitigate and treat the disease caused by these viruses in humans.

Sentiment analysis (SA), and emotion detection and recognition from text (EDRT) are recent areas of study that are closely related to each other. Sentiment analysis strives to identify and detect neutral, positive, or negative feelings from text. On the other hand, emotion analysis seeks to identify and distinguish types of feelings such as happiness, surprise, grief, disgust, fear, and anger through the expression of texts. We suggest a four-level strategy in this paper for recommending the best book to users. The levels include semantic network grouping of comparable sentences, sentiment analysis, reviewer clustering, and recommendation system. The semantic network groups comparable sentences at the first level utilizing pre-processed data from reviewer and book datasets using the parts of speech (POS) tagger. In order to extract keywords from the pre-processed data, feature extraction uses the bag of words (BOW) and term frequency-inverse document frequency (TF-IDF) approaches. SA is performed at the second level in two phases: training and testing, employing deep learning methodologies such as convolutional neural networks (CNN)-long short-term memory (LSTM). The results of this level are sent into the third level (clustering), which uses the clustering method to group the reviewers by age, location, and gender. In the last level, the model assessment is carried out with accuracy, precision, recall, sensitivity, specificity, G-mean, and F1-measure. The book suggestion system is designed to provide the highest level of accuracy within a minimum number of epochs when compared to the state-of-the methods, SVM, CNN, ANN, LSTM, and Bi-directional (BI)-LSTM.

In the last few decades, photocatalysis has been found to be a practical, environmentally friendly approach for degrading various pollutants into non-toxic products (e.g., H2O and CO2) and generating fuels from water using solar light. Mainly, traditional photocatalysts (such as metal oxides, sulfides, and nitrides) have shown a promising role in various photocatalysis reactions. However, it faces many bottlenecks, such as a wider band gap, low light absorption nature, photo-corrosion issues, and quick recombination rates. Due to these, a big question arises of whether these traditional photocatalysts can meet increasing energy demand and degrade emerging pollutants in the future. Currently, researchers view heteroanionic materials as a feasible alternative to conventional photocatalysts for future energy generation and water purification techniques due to their superior light absorption capacity, narrower band gap, and improved photo-corrosion resistance. Therefore, this article summarizes the recent developments in heteroanionic materials, their classifications based on anionic presence, their synthesis techniques, and their role in photocatalysis. In the end, we present a few recommendations for improving the photocatalytic performance of future heteroanionic materials.

Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing precise findings before the critical stage. The computer-aided diagnostic process includes preprocessing, segmentation, feature extraction, and classification. This study discusses the advantages and disadvantages of various computer-aided algorithms. It also discusses the current approaches, problems, and various types of datasets for skin images. Information about possible future works is also highlighted in this paper. The inferences derived from this survey will be useful for researchers carrying out research in skin cancer image analysis.

Protonic ceramic fuel cells (PCFCs) are one of the promising and emerging technologies for future energy generation. PCFCs are operated at intermediate temperatures (450–750 °C) and exhibit many advantages over traditional high-temperature oxygen-ion conducting solid oxide fuel cells (O-SOFCs) because they are simplified, have a longer life, and have faster startup times. A clear understanding/analysis of their specific working parameters/processes is required to enhance the performance of PCFCs further. Many physical processes, such as heat transfer, species transport, fluid flow, and electrochemical reactions, are involved in the operation of the PCFCs. These parameters are linked with each other along with internal velocity, temperature, and electric field. In real life, a complex non-linear relationship between these process parameters and their respective output cannot be validated only using an experimental setup. Hence, the computational fluid dynamics (CFD) method is an easier and more effective mathematical-based approach, which can easily change various geometric/process parameters of PCFCs and analyze their influence on its efficiency. This short review details the recent studies related to the application of CFD modeling in the PCFC system done by researchers to improve the electrochemical characteristics of the PCFC system. One of the crucial observations from this review is that the application of CFD modeling in PCFC design optimization is still much less than the traditional O-SOFC.

Diabetic nephropathy (DN) has become a leading cause of end-stage renal failure worldwide. The goal of the current study was to examine the protective effects of chitosan-loaded p-Coumaric acid nanoparticles (PCNPs) in nephrotoxicity induced by streptozotocin (STZ). Because of the antidiabetic, anti-inflammatory, and antioxidant properties of PCNPs, the development of DN may be considerably decreased. In this study, the rats received a single intraperitoneal injection (i.p.) of STZ (45 mg/kg) to induce DN. PCNPs were given orally 80 mg/kg b.w to the rats for a duration of four weeks. Body weight, kidney weight, blood glucose, and insulin levels were measured at the end of the experiment. Serum and urine parameters were also examined, along with the histological, immunobiological, and tumor necrosis factor (TNF) and interleukin-6 (IL-6) expression of the nephrotic rats. To comprehend the impact of PCNPs, the expression patterns of the kidney injury molecule (KIM-1) and glucose transporter-2 (GLUT-2) were evaluated. Administration of PCNPs significantly increased body weight, decreased kidney weight and also ameliorated blood glucose levels in the nephropathic rats. The administration of PCNPs also reverted the levels of urea, serum creatinine, urinary NAG, β-glucuronidase and albumin to near-normal levels. The administration of PCNPs also caused the levels of serum and urine parameters to return to near-normal levels. Additionally, the PCNP-treated rats had markedly reduced TNF-α, IL-6, and KIM-1 expressions as well as enhanced GLUT-2 mRNA expression. Our findings clearly showed that PCNP administration prevents the onset of DN in rats by lowering hyperglycemia, decreasing inflammation, and improving the expression of GLUT-2 mRNA in nephropathic rats.

Biogenic CoFe2O4 nanoparticles were prepared by co-precipitation and Hibiscus rosa sinensis plant leaf was used as a bio-reductant of the nanoparticle productions. The biosynthesized CoFe2O4 nanoparticles were characterized by XRD, FTIR, UV, VSM, and SEM via EDX analysis. The cubic phase of biosynthesized CoFe2O4 nanoparticles and their crystallite size was determined by XRD. The Co-Fe-O bonding and cation displacement was confirmed by FTIR spectroscopy. The presence of spherically-shaped biosynthesized CoFe2O4 nanoparticles and their material were confirmed by SEM and TEM via EDX. The super-paramagnetic behaviour of the biosynthesized CoFe2O4 nanoparticles and magnetic pulse was established by VSM analysis. Organic and bacterial pollutants were eradicated using the biosynthesized CoFe2O4 nanoparticles. The spinel ferrite biosynthesized CoFe2O4 nanoparticles generate radical and superoxide ions, which degrade toxic organic and bacterial pollutants in the environment.

Climate change significantly aggravates the quality of soil and water, especially in desert regions such as the United Arab Emirates (UAE) and Egypt concluding in an alarming increase in salinity in the reservoirs of the natural resources. Saline farming rises as a promising solution, utilizing low-quality water and land resources to grow salt-tolerant varieties of conventional crops and halophytes. Samphire (Salicornia spp.) is among the most popular multi-purpose halophytes that are locally consumed in several countries around the world as a vegetable. Six Salicornia bigelovii genotypes (ICBA-2, ICBA-3, ICBA-4, ICBA-8, ICBA-9, ICBA-10) were evaluated for their agronomic performance and nutritional composition in Dubai in UAE and, for the first time, at the Red Sea Governorate in Egypt in the 2019–2020 season using saline groundwater for irrigation (ECw = 26 and 6.6 dS/m, respectively). ICBA-10 performed well in both locations with high green biomass and seed yield (10.9 kgm−2 and 116.3 gm−2, respectively, in UAE; 7.7 kgm−2 and 82.9 gm−2, respectively, in Egypt). ICBA-10 was, overall, also good in ion accumulation, total amino acids and unsaturated fatty acids content in both locations for shoots and seeds. Our results indicated that a lack of a drainage system and leaching fraction, the silt loam texture and the drip irrigation system might have contributed in the gradual accumulation of salts in the soil at Mubarak Valley at the end of the experiment at a higher level than ICBA. Apart from the agronomic parameters, higher salinity levels also affected ion accumulation, the amino acids and the fatty acids content for both shoots and seeds, whereas the proximate composition was affected to a lesser extent. Our findings on the high unsaturated fatty acids content under higher salinity corroborate the nutritional value of S. bigelovii oil. Due to its euhalophyte nature, S. bigelovii is a valuable source of minerals, amino acids and antioxidants that render it the most promising salt-loving plant for food use.

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.

In the present study, silver nanoparticles (Ag-NPs) were synthesized using Azadirachta indica extract and evaluated for their in vitro antioxidant activity and cytotoxicity efficacy against MCF-7 and HeLa cells. The silver nanoparticles (Ag-NPs) were formed within 40 min and after preliminary confirmation by UV-visible spectroscopy (peak observed at 375 nm), they were characterized using a transmission electron microscope (TEM) and dynamic light scattering (DLS). The TEM images showed the spherical shape of the biosynthesized Ag-NPs with particle sizes in the range of 10 to 60 nm, and compositional analysis was carried out. The cytotoxicity and antioxidant activity of various concentrations of biosynthesized silver nanoparticles, Azadirachta indica extract, and a standard ranging from 0.2 to 1.0 mg/mL were evaluated. The 2,2-Diphenyl-1-picrylhydrazyl (DPPH) activity of the biosynthesized Ag-NPs and aqueous leaf extract increased in a dose-dependent manner, with average IC50 values of the biosynthesized Ag-NPs, aqueous leaf extract, and ascorbic acid (standard) of 0.70 ± 0.07, 1.63 ± 0.09, and 0.25 ± 0.09 mg/mL, respectively. Furthermore, higher cytotoxicity was exhibited in both the MCF-7 and HeLa cell lines in a dose-dependent manner. The average IC50 values of the biosynthesized Ag-NPs, aqueous leaf extract, and cisplatin (standard) were 0.90 ± 0.07, 1.85 ± 0.01, and 0.56 ± 0.08 mg/mL, respectively, with MCF-7 cell lines and 0.85 ± 0.01, 1.76 ± 0.08, 0.45 ± 0.10 mg/mL, respectively, with HeLa cell lines. Hence, this study resulted in an efficient green reductant for producing silver nanoparticles that possess cytotoxicity and antioxidant activity against MCF-7 and HeLa cells.

Transferosomes are one of the vesicular carriers that have received extensive research and attention recently because of their capacity to get beyond the barriers posed by the stratum corneum to penetration. The intent of the current study is to optimize and evaluate proanthocyanidin (PAC) containing transferosomal transdermal gels. PAC-containing transferosomes were prepared using the film hydration method and then loaded into a 4% methylcellulose gel. A 23 Box–Behnken design was used to optimize the PAC-loaded transferosomal gel, where the effects of phospholipid 90 G (X1), Tween 80 (X2), and sonication time (X3) were evaluated. The formulation factors, such as the drug entrapment efficiency percentage (PEE) and in vitro drug release, were characterized. A PEE of 78.29 ± 1.43% and a drug release in vitro at 6 h of 24.2 ± 1.25% were obtained. The optimized transferosomal-loaded proanthocyanidin (OTP) formulation penetrated the porcine skin at an excellent rate (0.123 ± 0.0067 mg/cm2/h). Stability tests were conducted for OTP to predict the effects of various temperature conditions on the physical appearance, drug content, and PEE for periods of 15, 30, and 45 days. Finally, this transferosomal system for transdermal PAC delivery may be a suitable alternative to the conventional treatment for osteoarthritis.

Epithelial–mesenchymal transition (EMT) is implicated in tumor metastasis and therapeutic resistance. It remains a challenge to target cancer cells that have undergone EMT. The Snail family of key EMT-inducing transcription factors directly binds to and transcriptionally represses not only epithelial genes but also a myriad of additional genomic targets that may carry out significant biological functions. Therefore, we reasoned that EMT inherently causes various concomitant phenotypes, some of which may create targetable vulnerabilities for cancer treatment. In the present study, we found that Snail transcription factors bind to the promoters of multiple genes encoding subunits of the AMP-activated protein kinase (AMPK) complex, and expression of AMPK genes was markedly downregulated by EMT. Accordingly, high AMPK expression in tumors correlated with epithelial cell markers and low AMPK expression in tumors was strongly associated with adverse prognosis. AMPK is the principal sensor of cellular energy status. In response to energy stress, AMPK is activated and critically reprograms cellular metabolism to restore energy homeostasis and maintain cell survival. We showed that activation of AMPK by energy stress was severely impaired by EMT. Consequently, EMT cancer cells became hypersensitive to a variety of energy stress conditions and primarily underwent pyroptosis, a regulated form of necrotic cell death. Collectively, the study suggests that EMT impedes the activation of AMPK signaling induced by energy stress and sensitizes cancer cells to pyroptotic cell death under energy stress conditions. Therefore, while EMT promotes malignant progression, it concurrently induces collateral vulnerabilities that may be therapeutically exploited.

This study aimed at assessing the clinical outcomes of the Single Flap Approach (SFA) with the additional use of Low-level laser therapy (LLLT). The defects were treated as per the principles of SFA, whereby 20 defects received only SFA (control group) and 20 defects received additional LLLT for bio stimulation/bio modulation (test group). Stable primary closure of the flaps was obtained with vertical internal mattress sutures. Plaque indices (FMPS), clinical attachment levels (CAL), probing pocket depth (PPD), and gingival bleeding scores (FMBS) were calculated at baseline, and at the 3rd and 6th months in both groups. An EHI score of 1 was observed at all sites except for two, where a score of 2 in the control group at week 2 was observed. In the test group, the PPD reduction at 6 months was 3.60 ± 0.95 and in the control group it was 3.75 ± 0.91 mm. CAL gain at 6 months was 2.70 ± 1.36 mm and 3.45 ± 1.2 mm in the test group and showed no statistical significance. These data suggested the positive effect of LLLT over CAL gain; thus, LLLT may be combined with SFA to potentially enhance the early wound healing and higher clinical outcomes in terms of increase in CAL and decrease in PPD.

Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6–0.8 mm and 0.5–1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be 4.90 and 20. The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.