Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

Bionanotechnology is a branch of science that has revolutionized modern science and technology. Nanomaterials, especially noble metals, have attracted researchers due to their size and application in different branches of sciences that benefit humanity. Metal nanoparticles can be synthesized using green methods, which are good for the environment, economically viable, and facilitate synthesis. Due to their size and form, gold nanoparticles have become significant. Plant materials are of particular interest in the synthesis and manufacture of theranostic gold nanoparticles (NPs), which have been generated using various materials. On the other hand, chemically produced nanoparticles have several drawbacks in terms of cost, toxicity, and effectiveness. A plant-mediated integration of metallic nanoparticles has been developed in the field of nanotechnology to overcome the drawbacks of traditional synthesis, such as physical and synthetic strategies. Nanomaterials′ tunable features make them sophisticated tools in the biomedical platform, especially for developing new diagnostics and therapeutics for malignancy, neurodegenerative, and other chronic disorders. Therefore, this review outlines the theranostic approach, the different plant materials utilized in theranostic applications, and future directions based on current breakthroughs in these fields.

Water pollution due to textile dyes is a serious threat to every life form. Bacteria can degrade and detoxify toxic dyes present in textile effluents and wastewater. The present study aimed to evaluate the degradation potential of eleven bacterial strains for azo dye methyl red. The optimum degradation efficiency was obtained using P. aeruginosa. It was found from initial screening results that P. aeruginosa is the most potent strain with 81.49% degradation activity and hence it was subsequently used in other degradation experiments. To optimize the degradation conditions, a number of experiments were conducted where only one variable was varied at a time and where maximum degradation was observed at 20 ppm dye concentration, 1666.67 mg/L glucose concentration, 666.66 mg/L sodium chloride concentration, pH 9, temperature 40 °C, 1000 mg/L urea concentration, 3 days incubation period, and 66.66 mg/L hydroquinone (redox mediator). The interactive effect of pH, incubation time, temperature, and dye concentration in a second-order quadratic optimization of process conditions was found to further enhance the biodegradation efficiency of P. aeruginosa by 88.37%. The metabolites of the aliquot mixture of the optimized conditions were analyzed using Fourier transform infrared (FTIR), GC-MS, proton, and carbon 13 Nuclear Magnetic Resonance (NMR) spectroscopic techniques. FTIR results confirmed the reduction of the azo bond of methyl red. The Gas Chromatography–Mass Spectrometry (GC-MS) results revealed that the degraded dye contains benzoic acid and o-xylene as the predominant constituents. Even benzoic acid was isolated from the silica gel column and identified by ¹H and ¹³C NMR spectroscopy. These results indicated that P. aeruginosa can be utilized as an efficient strain for the detoxification and remediation of industrial wastewater containing methyl red and other azo dyes.

Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convolutional neural network (ML-DCNN) architecture on retinal fundus images to diagnose glaucoma. We collected a retinal fundus images database from the local hospital. The fundus images are pre-processed by an adaptive histogram equalizer to reduce the noise of images. The ML-DCNN architecture is used for features extraction and classification into two phases, one for glaucoma detection known as detection-net and the second one is classification-net used for classification of affected retinal glaucoma images into three different categories: Advanced, Moderate and Early. The proposed model is tested on 1338 retinal glaucoma images and performance is measured in the form of different statistical terms known as sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE). On average, SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2% are achieved. The obtained outcomes are comparable to the state-of-the-art systems and achieved competitive results to solve the glaucoma eye disease problems for complex glaucoma eye disease cases.

The cold extraction method was used to obtain the aqueous extract of Vitex leucoxylon leaves in a ratio of 1:10. Iron nanoparticles (FeNPs) were synthesized using aqueous leaf extract of V. leucoxylon as a reducing agent. The phytoreducing approach was used to make FeNPs by mixing 1 mL of plant extract with 1 mM of ferric sulfate. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), Ultraviolet–visible spectroscopy (UV-Vis), and energy-dispersive X-ray spectroscopy were used to examine the synthesized FeNPs. The reducing reaction was shown by a change in the color of the solution, and the formation of black color confirms that FeNPs have been formed. The greatest absorption peak (max) was found at 395 nm in UV-Vis spectral analysis. The FTIR spectra of V. leucoxylon aqueous leaf extract showed shifts in some peaks, namely 923.96 cm⁻¹ and 1709.89 cm⁻¹, with functional groups carboxylic acids, unsaturated aldehydes, and ketones, which were lacking in the FTIR spectra of FeNPs and are responsible for FeNPs formation. FeNPs with diameters between 45 and 100 nm were observed in SEM images. The creation of FeNPs was confirmed by EDX, which shows a strong signal in the metallic iron region at 6–8 Kev. XRD revealed a crystalline nature and an average diameter of 136.43 nm. Antioxidant, anti-inflammatory, cytotoxic, and wound healing in vitro tests reported significant activity of the FeNPs. The cumulative findings of the present study indicate that the green synthesis of FeNPs boosts its biological activity and may serve as a possible dermal wound-healing agent and cytotoxic agent against cancer. Future study is needed on the identification of mechanisms involved in the synthesis of FeNPs by V. leucoxylon and its biomedical applications.