Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.

Gene editing, especially with clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9), has advanced gene function science. Gene editing’s rapid advancement has increased its medical/clinical value. Due to its great specificity and efficiency, CRISPR/Cas9 can accurately and swiftly screen the whole genome. This simplifies disease-specific gene therapy. To study tumor origins, development, and metastasis, CRISPR/Cas9 can change genomes. In recent years, tumor treatment research has increasingly employed this method. CRISPR/Cas9 can treat cancer by removing genes or correcting mutations. Numerous preliminary tumor treatment studies have been conducted in relevant fields. CRISPR/Cas9 may treat gene-level tumors. CRISPR/Cas9-based personalized and targeted medicines may shape tumor treatment. This review examines CRISPR/Cas9 for tumor therapy research, which will be helpful in providing references for future studies on the pathogenesis of malignancy and its treatment.

The currently available topical formulations of tacrolimus have minimal and variable absorption, elevated mean disposition half-life, and skin irritation effects resulting in patient noncompliance. In our study, we fabricated tacrolimus-loaded solid lipid nanoparticles (SLNs) that were converted into a gel for improved topical applications. The SLNs were prepared using a solvent evaporation method and characterized for their physicochemical properties. The particle size of the SLNs was in the range of 439 nm to 669 nm with a PDI of ≤0.4, indicating a monodispersed system. The Zeta potential of uncoated SLNs (F1–F5) ranged from −25.80 to −15.40 mV. Those values reverted to positive values for chitosan-decorated formulation (F6). The drug content and entrapment efficiency ranged between 0.86 ± 0.03 and 0.91 ± 0.03 mg/mL and 68.95 ± 0.03 and 83.68 ± 0.04%, respectively. The pH values of 5.45 to 5.53 depict their compatibility for skin application. The surface tension of the SLNs decreased with increasing surfactant concentration that could increase the adherence of the SLNs to the skin. The release of drug from gel formulations was significantly retarded in comparison to their corresponding SLN counterparts (p ≤ 0.05). Both SLNs and their corresponding gel achieved the same level of drug permeation, but the retention of the drug was significantly improved with the conversion of SLNs into their corresponding gel formulation (p ≤ 0.05) due to its higher bioadhesive properties.

In the present study, chitosan-decorated multiple nanoemulsion (MNE) was formulated using a two-step emulsification process. The formulated multiple nanoemuslion was evaluated physiochemically for its size and zeta potential, surface morphology, creaming and cracking, viscosity and pH. A Franz diffusion cell apparatus was used to carry out in vitro drug-release and permeation studies. The formulated nanoemulsion showed uniform droplet size and zeta potential. The pH and viscosity of the formulated emulsion were in the range of and suitable for topical delivery. The drug contents of the simple nanoemulsion (SNE), the chitosan-decorated nanoemulsion (CNE) and the MNE were 71 ± 2%, 82 ± 2% and 90 ± 2%, respectively. The formulated MNE showed controlled release of itraconazole as compared with that of the SNE and CNE. This was attributed to the chitosan decoration as well as to formulating multiple emulsions. The significant permeation and skin drug retention profile of the MNE were attributed to using the surfactants tween 80 and span 20 and the co-surfactant PEG 400. ATR-FTIR analysis confirmed that the MNE mainly affects the lipids and proteins of the skin, particularly the stratum corneum, which results in significantly higher permeation and retention of the drug. It was concluded that the proposed MNE formulation delivers drug to the target site of the skin and can be therapeutically used for various cutaneous fungal infections.

As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Microbial lipase is one of the major sources of the enzyme that has been broadly exploited in the food, detergent, and pharmaceutical industries due to its high catalytic activity, high yield, and environmental friendliness and cost-effectiveness. Therefore, the aim of this study was to optimize the medium for the submerged fermentation for lipase production by a novel strain, Bacillus salmalaya strain 139SI. The media subjected to lipase production was Luria Bertani (LB) with different carbon sources and surfactants supplemented to determine which would give the highest lipase activity of Bacillus salmalaya. The Lipase activity of the supernatant containing lipase enzyme was ddetermined using the titrimetric method with hydrolysis reaction. Results showed that the olive oil that was used as a carbon source, induced the highest lipase activity (11.0 U/mL) compared to sunflower oil (9.6 U/mL) and cooking oil waste (7.8 U/mL). For surfactants, LB medium supplemented with tween 80 enhanced higher lipase activity (6.8 U/mL) compared to tween 20 (6.0 U/mL) and sodium dodecyl sulphate (SDS) (2.0 U/mL). Thus, it can be concluded that submerged fermentation allows optimization of the culture medium whereby, among carbon sources, olive oil induced the highest lipase production, whereas Tween 80 was the best lipase inducer compared to other surfactants.