Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

The COVID-19 pandemic, caused by a new coronavirus, has affected economic and social standards as governments and healthcare regulatory agencies throughout the world expressed worry and explored harsh preventative measures to counteract the disease’s spread and intensity. Several academics and experts are primarily concerned with halting the continuous spread of the unique virus. Social separation, the closing of borders, the avoidance of big gatherings, contactless transit, and quarantine are important methods. Multiple nations employ autonomous, digital, wireless, and other promising technologies to tackle this coronary pneumonia. This research examines a number of potential technologies, including unmanned aerial vehicles (UAVs), artificial intelligence (AI), blockchain, deep learning (DL), the Internet of Things (IoT), edge computing, and virtual reality (VR), in an effort to mitigate the danger of COVID-19. Due to their ability to transport food and medical supplies to a specific location, UAVs are currently being utilized as an innovative method to combat this illness. This research intends to examine the possibilities of UAVs in the context of the COVID-19 pandemic from several angles. UAVs offer intriguing options for delivering medical supplies, spraying disinfectants, broadcasting communications, conducting surveillance, inspecting, and screening patients for infection. This article examines the use of drones in healthcare as well as the advantages and disadvantages of strict adoption. Finally, challenges, opportunities, and future work are discussed to assist in adopting drone technology to tackle COVID-19-like diseases.

This research includes the design and synthesis of new derivatives for rhodanine azo compounds (4a – c) containing a naphthalene ring. Physiochemical properties of the synthesized compounds were determined by their melting points, FTIR, ¹H-NMR, ¹³C-NMR, and elemental analysis spectroscopic techniques. The biological activities of the newly prepared azo rhodanine compounds were evaluated against some pathogenic bacteria using three different bacterial species including (Escherichia coli., Pseudomonas aeruginosa, Staphylococcus aureus) and compared with amoxicillin as a reference drug. The results showed that our compounds have moderate-to-good vital activity against the mentioned pathogenic bacteria. The selectivity and sensitivity of the newly prepared rhodanine azo compounds with transition metals Co²⁺, Cu²⁺, Zn²⁺, Ni²⁺, and Fe³⁺ were studied using UV–vis and fluorescence spectroscopy techniques. Among the synthesized azos, azo 4 c showed affinity toward Fe³⁺ ions with an association constant of 4.63 × 10⁸ M⁻¹. Furthermore, this azo showed high sensitivity toward Fe³⁺ ions with detection limits of 5.14 µM. The molar ratio and Benesi–Hildebrand methods confirmed the formation of complexes between azo 4 c and Fe³⁺ with 1:2 binding stoichiometry. Therefore, azo 4 c showed excellent potential for developing efficient Fe³⁺ chemosensors.

In this study, the chemoselective synthesis of two new thiophene derivatives is presented. The structure of newly synthesized thiophenes derivatives; ethyl 4-acetyl-3-phenyl-5-(phenylamino)thiophene-2-carboxylate (5) and ethyl (E)-4-(3-(dimethylamino)acryloyl)-3-phenyl-5-(phenylamino)thiophene-2-carboxylate (8) were established using different FTIR and NMR spectral analyses. Compound 8 was isolated as single crystal and its 3D structure was determined using X-ray crystallographic analysis. Possible intermolecular interactions that control the molecular packing of 8 were elucidated using Hirshfeld topology analysis. The O…H (13.7%), H…H (55.3%) and C…C (2.3%) intermolecular interactions are the most significant. Fukui functions showed that C4 in thiophene 5 and C3 in thiophene 8 are the most reactive atoms for nucleophilic attack, while N9 in thiophene 5 and C1 in thiophene 8 are the most reactive atoms for electrophilic attack. Antitumor activity of thiophene 5 was assessed and the results showed higher activity against HepG-2 (7.46 µg/mL) compared to the HCT 116 (12.60 µg/mL) cell line.

This study evaluates the antioxidant properties of leaves, unripe and ripe fruits of Ziziphus mauritiana from the UAE. Phenolic compounds show a strong correlation with antioxidant activity, with the leaves exhibiting the highest efficiency. The results suggest that Ziziphus mauritiana leaves are a promising source of natural antioxidants with potential therapeutic applications.