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Purpose: To identify self-medications used by patients as initial treatment for various eye problems and to analyze associated complications. Study Design: Cross sectional. Place and Duration of Study: Al-Khidmat teaching hospital, (University of Lahore) from October 2021 to September 2022. Method: This study included 117 patients using traditional eye medications (TEM) and over the counter (OTC) ophthalmic drugs before presenting in ophthalmic outdoor. A semi structured questionnaire was used as a tool to collect the data. All patients aged 18 years and above were directly questioned. For patients below 18 years, responses were collected from the patients themselves when possible; otherwise, the accompanying parent provided the information. Age, gender, educational status and area of residence were recorded. The symptoms compelling the use of TEM/OTC or both, the source and type of medication, diagnosis and any complications that resulted due to self-medication were documented. Complete ocular examination was done. MS Excel was used to record and analyze data. Results: There were 48% males and 52% females. Rosewater was the most frequently used TEM by 54.7%. Steroids-antibiotic combination eyedrops/ointments were used by 31.6%. Symptoms for which self-medication was done, were redness (64.1%), watering (35.9%), itching (32.5%) and discharge (26.5%). Symptoms did not improve in 54.7%, 26.5% required ophthalmic consultation and only 18.8% had temporary relief. Twelve percent developed complications. Conclusion: Self-medication with TEM or OTC drugs should be discouraged as these can cause detrimental effects on eyes. [ABSTRACT FROM AUTHOR]

Air pollution is a severe problem in the modern world. Urbanization, industrialization, and traffic emit air pollutants such as carbon monoxide (CO), nitrous oxides (NOx), hydrocarbons (HCs), and particulate matter into the environment. Plants can absorb air pollutants through stomata. They adversely affect the various metabolic and physiological processes of plant species. This review describes the impact of air pollution on plant health, morphologically, physiologically, and genetically, and the tolerance ability of plants located along roadside areas. Many morphological effects, like chlorosis, necrosis, leaf area, stomatal clogging, plant productivity, leaf falling, and reduction in flower yield, are observed due to the influence of air pollution. Air pollutants also damage the DNA and affect the biochemicals of the plants, as well as pH, relative water content (RWC), simple sugar, ascorbic acid (AA), total chlorophyll content (TCH), proline, and polyamines. Some plants located under pollution stress can mitigate air pollution. Plants with higher APTI values are more tolerant of air pollution, while those with the lowest APTI values can be used as an indicator of the rate of air pollution. There is much morphological, biochemical, and DNA damage noted in this review. Different strategies can be used to diagnose the effects of air pollution in the future and develop green belts to mitigate air pollution in pollution-stressed areas. [ABSTRACT FROM AUTHOR]

Highway safety researchers focus on crash injury severity, utilizing deep learning--specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)--as the preferred method for modeling accident severity. Deep learning's strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent. The framework optimizes hidden layers and integrates data augmentation, Gaussian noise, and dropout regularization for improved generalization. Sensitivity and factor contribution analyses identify influential predictors. Evaluated on three diverse crash record databases--NCDB 2018-2019, UK 2015-2020, and US 2016-2021--the D-RNN model excels with an ACC score of 89.0281%, a Roc Area of 0.751, an F-estimate of 0.941, and a Kappa score of 0.0629 over the NCDB dataset. The proposed framework consistently outperforms traditional methods, existing machine learning, and deep learning techniques. [ABSTRACT FROM AUTHOR]

The objective of this study is to the assess toxicity of superparamagnetic nanoparticles within normal and diabetic human blood groups using complete blood count (CBC). For this purpose, iron oxide nanoparticles (IONPs) were synthesized by co-precipitation method. Samples were characterized by XRD, SEM, EDS, VSM and CBC. XRD confirmed the cubic structure of Fe3O4 with Miller indices (2 0 0), (2 2 0), (3 1 1), (4 0 0), (4 4 0) and average size of magnetic nanoparticles was calculated about 11.13 nm diameter. The morphology of Fe3O4 nanoparticles was investigated by scanning electron microscope (SEM). SEM images of magnetite were found partially smooth. Spectra of EDX depicted the Fe, O and Cl elements in IONP. Magnetic properties were examined by vibrating sampling magnetometer (VSM). The blood toxicity was reported by blood contents in normal and diabetic blood samples for specified intervals using CBC technique. One sample test of variance of hemoglobin, erythrocytes, leucocytes and thrombocytes showed the significant difference (p > 0.05) among 0 hrs to 72 hrs for all normal and diabetic blood samples. This study concluded that presence of iron oxides nanoparticles of size 11.13 nm in human blood induces reactive oxygen species which cause the cell death. Hemoglobin was highly affected content of blood than erythrocytes, leucocytes by toxic effects of IONPs. Reduction of platelets among all content of blood groups posed another signature of this hematology study. Toxic spectrum of IONPs must be considered before the application of MRI contrast agents and drug delivery.