The Diels-Alder Cross-Linked Gelatin/Dextran Nanocomposite Hydrogels with Silver Nanoparticles for Wound Healing Applications: Synthesis, Characterization, and In Vitro Evaluation.

Wound healing involves a sophisticated biological process that relies on ideal conditions to advance through various stages of repair. Modern wound dressings are designed to imitate the natural surroundings around cells and offer properties such as moisture regulation, strength, and antimicrobial defense to boost healing. A recent research project unveiled a new type of gelatin (Gel)/dextran (Dex) hydrogels, linked through Diels-Alder (D-A) reactions, loaded with silver nanoparticles (Ag-NPs) for cutting-edge wound treatment. Gel and Dex were chemically modified to form the hydrogels via the D-A reaction. The hydrogels were enriched with Ag-NPs at varying levels. Thorough analyses of the hydrogels using methods like NMR, FT-IR, and SEM were carried out to assess their structure and nanoparticle integration. Rheological tests displayed that the hydrogels had favorable mechanical attributes, particularly when Ag-NPs were included. The hydrogels demonstrated controlled swelling, responsiveness to pH changes, and were non-toxic. Testing against E. coli showcased the strong antibacterial activity of the nanocomposite hydrogels in a concentration-dependent manner. This investigation showcased the promise of these bioactive nanocomposite hydrogels in promoting speedy wound healing by maintaining a moist environment, offering an antimicrobial shield, and ensuring mechanical support at the wound site.

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: methods, applications and limitations.

BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE: This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS: Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS: Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS: The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION: Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.

Nano guardians of the heart: A comprehensive investigation into the impact of silver nanoparticles on cardiovascular physiology.

Globally, cardiovascular diseases (CVDs) constitute the leading cause of death at the moment. More effective treatments to combat CVDs are urgently required. Recent advances in nanotechnology have opened the door to new avenues for cardiovascular health treatment. Silver nanotechnology's inherent therapeutic powers and wide-ranging applications have made it the center of focus in recent years. This review aims to analyze the chemical, physical, and biological processes ofproducing AgNPs and determine their potential utility as theranostics. Despite significant advances, the precise mechanism by which AgNPs function in numerous biological systems remains a mystery. We hope that at the end of this review, you will better understand how AgNPs affect the cardiovascular system from the research done thus far. This endeavor thoroughly investigates the possible toxicological effects and risks associated with exposure to AgNPs. The findings shed light on novel applications of these versatile nanomaterials and point the way toward future research directions. Due to a shortage of relevant research, we will limit our attention to AgNPs as they pertain to CVDs. Future research can use this opportunity to investigate the many medical uses of AgNPs. Given their global prevalence, we fully endorse academics' efforts to prioritize nanotechnological techniques in pursuing risk factor targeting for cardiovascular diseases. The critical need for innovative solutions to this widespread health problem is underscored by the fact that this technique may help with the early diagnosis and treatment of CVDs.

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification.

BACKGROUND: In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges. NEW METHOD: This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise. RESULTS: The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively. COMPARISON WITH EXISTING METHODS: In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods. CONCLUSION: The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.

Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

NIR-responsive carboxymethyl-cellulose hydrogels containing thioketal-linkages for on-demand drug delivery system.

Near-infrared (NIR) light-responsive hydrogels have emerged as a highly promising strategy for effective anticancer therapy owing to the remotely controlled release of chemotherapeutic molecules with minimal invasive manner. In this study, novel NIR-responsive hydrogels were developed from reactive oxygen species (ROS)-cleavable thioketal cross-linkers which possessed terminal tetrazine groups to undergo a bio-orthogonal inverse electron demand Diels Alder click reaction with norbornene modified carboxymethyl cellulose. The hydrogels were rapidly formed under physiological conditions and generated N2 gas as a by-product, which led to the formation of porous structures within the hydrogel networks. A NIR dye, indocyanine green (ICG) and chemotherapeutic doxorubicin (DOX) were co-encapsulated in the porous network of the hydrogels. Upon NIR-irradiation, the hydrogels showed spatiotemporal release of encapsulated DOX (>96 %) owing to the cleavage of thioketal bonds by interacting with ROS generated from ICG, whereas minimal release of encapsulated DOX (<25 %) was observed in the absence of NIR-light. The in vitro cytotoxicity results revealed that the hydrogels were highly cytocompatible and did not induce any toxic effect on the HEK-293 cells. In contrast, the DOX + ICG-encapsulated hydrogels enhanced the chemotherapeutic effect and effectively inhibited the proliferation of Hela cancer cells when irradiated with NIR-light.

An adaptive ensemble deep learning framework for reliable detection of pandemic patients.

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The pandemic-induced loss of nursing staff has resulted in critical shortages. To address this, robotic solutions offer promising avenues. To solve this problem, we developed an ensemble deep learning (DL) model that uses seven different models to detect patients. Detected images are then used as input for the soft robot, which performs basic assessment tests. In this study, we introduce a deep learning-based approach for nursing soft robots, and propose a novel deep learning model named Deep Ensemble of Adaptive Architectures. Our method is twofold: firstly, an ensemble deep learning technique detects COVID-19 patients; secondly, a soft robot performs basic assessment tests on the identified patients. We evaluate the performance of various deep learning-based object detectors for patient detection, examining implementations of You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), Region-Based Convolutional Neural Network (RCNN), and Region-Based Fully Convolutional Network (R-FCN) on a proprietary dataset comprising 32,668 hospital surveillance images. Our results indicate that while YOLO and VGG facilitate rapid detection, Faster-RCNN (Inception ResNet-v2) and our proposed Ensemble-DL achieve the highest accuracy. Ensemble-DL offers accurate results in a reasonable timeframe, making it apt for patient detection on embedded platforms. Through real-world experiments, our method outperforms baseline approaches (including Faster-RCNN, R-FCN variants, CNN+LSTM, etc.) in terms of both precision and recall. Achieving an impressive accuracy of 98.32%, our deep learning-based model for nursing soft robots presents a significant advancement in the identification and assessment of COVID-19 patients, ultimately enhancing healthcare efficiency and patient care.

Facile Fabrication of NIR-Responsive Alginate/CMC Hydrogels Derived through IEDDA Click Chemistry for Photothermal-Photodynamic Anti-Tumor Therapy.

Novel chemically cross-linked hydrogels derived from carboxymethyl cellulose (CMC) and alginate (Alg) were prepared through the utilization of the norbornene (Nb)-methyl tetrazine (mTz) click reaction. The hydrogels were designed to generate reactive oxygen species (ROS) from an NIR dye, indocyanine green (ICG), for combined photothermal and photodynamic therapy (PTT/PDT). The cross-linking reaction between Nb and mTz moieties occurred via an inverse electron-demand Diels-Alder chemistry under physiological conditions avoiding the need for a catalyst. The resulting hydrogels exhibited viscoelastic properties (G' ~ 492-270 Pa) and high porosity. The hydrogels were found to be injectable with tunable mechanical characteristics. The ROS production from the ICG-encapsulated hydrogels was confirmed by DPBF assays, indicating a photodynamic effect (with NIR irradiation at 1-2 W for 5-15 min). The temperature of the ICG-loaded hydrogels also increased upon the NIR irradiation to eradicate tumor cells photothermally. In vitro cytocompatibility assessments revealed the non-toxic nature of CMC-Nb and Alg-mTz towards HEK-293 cells. Furthermore, the ICG-loaded hydrogels effectively inhibited the metabolic activity of Hela cells after NIR exposure.

Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.

Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.

Effect of exogenous application of biogenic silicon sources on growth, yield, and ionic homeostasis of maize (Zea mays L.) crops cultivated in alkaline soil.

Salinity has emerged as a major threat to food security and safety around the globe. The crop production on agricultural lands is squeezing due to aridity, climate change and low quality of irrigation water. The present study investigated the effect of biogenic silicon (Si) sources including wheat straw biochar (BC-ws), cotton stick biochar (BC-cs), rice husk feedstock (RH-fs), and sugarcane bagasse (SB), on the growth of two consecutive maize (Zea mays L.) crops in alkaline calcareous soil. The application of SB increased the photosynthetic rate, transpiration rate, stomatal conductance, and internal CO2 concentration by 104, 100, 55, and 16% in maize 1 and 140, 136, 76, and 22% in maize 2 respectively. Maximum yield (g/pot) of cob, straw, and root were remained as 39.5, 110.7, and 23.6 while 39.4, 113.2, and 23.6 in maize 1 and 2 respectively with the application of SB. The concentration of phosphorus (P) in roots, shoots, and cobs was increased by 157, 173, and 78% for maize 1 while 96, 224, and 161% for maize 2 respectively over control by applying SB. The plant cationic ratios (Mg:Na, Ca:Na, K:Na) were maximum in the SB applied treatment in maize 1 and 2. The study concluded that the application of SB on the basis of soluble Si, as a biogenic source, remained the best in alleviating the salt stress and enhancing the growth of maize in rotation. The field trials will be more interesting to recommend the farmer scale.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms.

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Road Feature Detection for Advance Driver Assistance System Using Deep Learning.

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.

Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

The effect of molecular weight and chemical structure of cross-linkers on the properties of redox-responsive hyaluronic acid hydrogels.

In this work, we investigated the effect of the size and the chemical structure of crosslinkers on the properties of hyaluronic acid-based hydrogels prepared via an inverse electron demand Diels-Alder reaction. Hydrogels having loose and dense networks were designed by cross-linkers with and without polyethylene glycol (PEG) spacers of different molecular weights (1000 and 4000 g/mol). The study showed that the properties of hydrogels such as swelling ratios (20-55 times), morphology, stability, mechanical strength (storage modulus in the range 175-858 Pa), and drug loading efficiency (87 % ~ 90 %) were greatly influenced by the addition of PEG and changing its molecular weight in the cross-linker. Particularly, the presence of PEG chains in redox- responsive crosslinkers increased the doxorubicin release (85 %, after 168 h) and the degradation rate (96 %, after 10 d) of hydrogels in the simulated reducing medium (10 mM DTT). The in vitro cytotoxicity experiments conducted for HEK-293 cells revealed that the formulated hydrogels were biocompatible, which could be a promising candidate for drug delivery applications.

Multi-classification deep neural networks for identification of fish species using camera captured images.

Regular monitoring of the number of various fish species in a variety of habitats is essential for marine conservation efforts and marine biology research. To address the shortcomings of existing manual underwater video fish sampling methods, a plethora of computer-based techniques are proposed. However, there is no perfect approach for the automated identification and categorizing of fish species. This is primarily due to the difficulties inherent in capturing underwater videos, such as ambient changes in luminance, fish camouflage, dynamic environments, watercolor, poor resolution, shape variation of moving fish, and tiny differences between certain fish species. This study has proposed a novel Fish Detection Network (FD_Net) for the detection of nine different types of fish species using a camera-captured image that is based on the improved YOLOv7 algorithm by exchanging Darknet53 for MobileNetv3 and depthwise separable convolution for 3 x 3 filter size in the augmented feature extraction network bottleneck attention module (BNAM). The mean average precision (mAP) is 14.29% higher than it was in the initial version of YOLOv7. The network that is utilized in the method for the extraction of features is an improved version of DenseNet-169, and the loss function is an Arcface Loss. Widening the receptive field and improving the capability of feature extraction are achieved by incorporating dilated convolution into the dense block, removing the max-pooling layer from the trunk, and incorporating the BNAM into the dense block of the DenseNet-169 neural network. The results of several experiments comparisons and ablation experiments demonstrate that our proposed FD_Net has a higher detection mAP than YOLOv3, YOLOv3-TL, YOLOv3-BL, YOLOv4, YOLOv5, Faster-RCNN, and the most recent YOLOv7 model, and is more accurate for target fish species detection tasks in complex environments.

Review on Propolis Applications in Food Preservation and Active Packaging.

Propolis is a natural hive product collected by honeybees from different plants and trees. The collected resins are then mixed with bee wax and secretions. Propolis has a long history of use in traditional and alternative medicine. Propolis possesses recognized antimicrobial and antioxidant properties. Both properties are characteristics of food preservatives. Moreover, most propolis components, in particular flavonoids and phenolic acids, are natural constituents of food. Several studies suggest that propolis could find use as a natural food preservative. This review is focused on the potential application of propolis in the antimicrobial and antioxidant preservation of food and its possible application as new, safe, natural, and multifunctional material in food packaging. In addition, the possible influence of propolis and its used extracts on the sensory properties of food is also discussed.

Effects of silicon nanoparticles and conventional Si amendments on growth and nutrient accumulation by maize (Zea mays L.) grown in saline-sodic soil.

Salinity is one of the major abiotic stresses in arid and semiarid climates which threatens the food security of the world. Present study had been designed to assess the efficacy of different abiogenic sources of silicon (Si) to mitigate the salinity stress on maize crop grown on salt-affected soil. Abiogenic sources of Si including silicic acid (SA), sodium silicate (Na-Si), potassium silicate (K-Si), and nanoparticles of silicon (NPs-Si) were applied in saline-sodic soil. Two consecutive maize crops with different seasons were harvested to evaluate the growth response of maize under salinity stress. Post-harvest soil analysis showed a significant decrease in soil electrical conductivity of soil paste extract (ECe) (-23.0%), sodium adsorption ratio (SAR) (-47.7%) and pH of soil saturated paste (pHs) (-9.5%) by comparing with salt-affected control. Results revealed that the maximum root dry weight was recorded in maize1 by the application of NPs-Si (149.3%) and maize2 (88.6%) over control. The maximum shoot dry weight was observed by the application of NPs-Si in maize1 (42.0%) and maize2 (7.4%) by comparing with control treatment. The physiological parameters like chlorophyll contents (52.5%), photosynthetic rate (84.6%), transpiration (100.2%), stomatal conductance (50.5%), and internal CO2 concentration (61.6%) were increased by NPs-Si in the maize1 crop when compared with the control treatment. The application of an abiogenic source (NPs-Si) of Si significantly increased the concentration of phosphorus (P) in roots (223.4%), shoots (22.3%), and cobs (130.3%) of the first maize crop. The current study concluded that the application of NPs-Si and K-Si improved the plant growth by increasing the availability of nutrients like P and potassium (K), physiological attributes, and by reducing the salts stress and cationic ratios in maize after maize crop rotation..

Dry eye disease and high disease activity score (DAS-28) in rheumatoid arthritis: An underrated combination.

OBJECTIVE: To determine the association of dryness of eyes with rheumatoid arthritis severity. METHODS: The cross-sectional, observational study was conducted at the Jinnah Medical College Hospital, Karachi, from December 2020 to May 2021, and comprised adult patients of either gender with rheumatoid arthritis who were diagnosed on the basis of clinical and serological investigations. Data was collected using a structured pre-tested questionnaire. Ocular Surface Disease Index questionnaires with Tear Film Breakup Time were used to assess the severity of dry eyes. Disease Activity Score-28 with erythrocyte sedimentation rate was used to assess the severity of rheumatoid arthritis. Association between the two was explored. Data was analysed using SPSS 22. RESULTS: Of the 61 patients, 52(85.2%) were females and 9(14.8%) were males. The overall mean age was 41.7±12.8 years, with 4(6.6%) aged <20 years, 26(42.6%) aged 21-40 years, 28(45.9%) aged 41-60 years and 3(4.9%) aged >60years. Further, 46(75.4%) subjects had sero-positive rheumatoid arthritis, 25(41%) had high severity, 30(49.2%) had severe Occular Surface Density Index score and 36(59%) had decreased Tear Film Breakup Time. Logistic Regression analysis showed there were 5.45 times higher odds of having severe disease among the people with Occular Surface Density Index score >33 (p=0.003). In patients with positive Tear Film Breakup Time, there were 6.25 higher odds of having increased disease activity score (p=0.001). CONCLUSIONS: Disease activity scores of rheumatoid arthritis were found to have strong association with dryness of eyes, high Ocular Surface Disease Index score and increased erythrocyte sedimentation rate.