Metal-organic frameworks (MOFs) are a fascinating class of crystalline porous materials composed of metal ions and organic ligands. Due to their attractive properties, MOFs can potentially offer biomedical field applications, such as drug delivery and imaging. This study aimed to systematically identify the affecting factors on the MOF characteristics and their effects on structural and biological characteristics. An electronic search was performed in four databases containing PubMed, Scopus, Web of Science, and Embase, using the relevant keywords. After analyzing the studies, 20 eligible studies were included in this review. As a result, various factors such as additives and organic ligand can influence the size and structure of MOFs. Additives are materials that can compete with ligand and may affect the nucleation and growth processes and, consequently, particle size. The nature and structure of ligand are influential in determining the size and structure of MOF. Moreover, synthesis parameters like the reaction time and initial reagents ratio are critical factors that should be optimized to regulate the size and structure. Of note is that the nature of the ligand and using a suitable additive can control the porosity of MOF. The more extended ligands aid in forming large pores. The choice of metallic nodes and organic ligand, and the MOF concentration are important factors since they can determine toxicity and biocompatibility of the final structure. The physicochemical properties of MOFs, such as hydrophobicity, affect the toxicity of nanoparticles. An increase in hydrophobicity causes increased toxicity of MOF. The biodegradability of MOF, as another property, depends on the organic ligand and metal ion and environmental conditions like pH. Photocleavable ligands can be served for controlled degradation of MOFs. Generally, by optimizing these affecting factors, MOFs with desirable properties will be obtained for biomedical applications.

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utilization of biological systems to develop new products at one end of the spectrum to the application of technology for solving the problems in biology at the other end. [1] According to the new experimental technologies like DNA nucleotide sequencing and RNA sequencing genomics, [2] Mass Spectrometry [3] as well as X-ray crystallography and Nuclear Magnetic Resonance spectroscopy [4] in proteomics, large datasets are generated and sorted in the field of biotechnology that make new opportunities for researchers along with companies that present services and products in this area. Nowadays, performing stateof-the-art research in biotechnology is roughly impossible without exploiting database and artificial intelligence (AI) technologies. [5] The integration of AI and biotechno logy is now prevalent. AI elucidates computational systems which extract signals and learn from the input. [1] Historically, AI has a close relationship with biology. Precisely, this relation is between statistics and genetics. Compared to text or image datasets, biological data is more multidimensional, complicated, and noisy. Hence, AI models are more pertinent than simple statistical models for biological datasets. [6] Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.