Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention

Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.

Photoelectrochemical biosensors: Prospects of graphite carbon nitride-based sensors in prostate-specific antigen diagnosis

Prostate cancer (PC) is very common in old age and causes many deaths. Early diagnosis and monitoring of the progress of the disease and the effectiveness of PC treatment are critical. On the other hand, choosing a specific biomarker for PCs is essential. Prostate-specific antigen (PSA) is a specific biomarker secreted in the prostate epithelial cells, which increases in cancer cells. Between all employed sensing mechanism, electrochemical sensors based on nanomaterials have created many hopes. Meanwhile, graphite carbon nitride (g-C3N4) is interested in developing photoelectrochemical sensors due to its large surface area, stability, easy modification, and good photoelectronic properties. In this review, electrochemical sensors based on nanocomposites containing g-C3N4 have been investigated in PSA detection. After providing an overview of the characteristics of g-C3N4 and cancer biomarkers, it reviews the strategies and mechanisms involved in identifying PSA. Different approaches to photoelectrochemistry, impedimetric immunosensors, photocatalysis, and luminescence have been used in diagnostic mechanisms. Then, challenges and prospects for electrochemical sensors based on nanocomposites containing g-C3N4 in PSA detection have been analyzed. The recent review generally opens an efficient view in PSA diagnosis and the application of g-C3N4-based electrochemical sensors in personalized medicine diagnosis and treatment.

Role of artificial intelligence in cancer detection using protein p53: A Review

Normal cell development and prevention of tumor formation rely on the tumor-suppressor protein p53. This crucial protein is produced from the Tp53 gene, which encodes the p53 protein. The p53 protein plays a vital role in regulating cell growth, DNA repair, and apoptosis (programmed cell death), thereby maintaining the integrity of the genome and preventing the formation of tumors. Since p53 was discovered 43 years ago, many researchers have clarified its functions in the development of tumors. With the support of the protein p53 and targeted artificial intelligence modeling, it will be possible to detect cancer and tumor activity at an early stage. This will open up new research opportunities. In this review article, a comprehensive analysis was conducted on different machine learning techniques utilized in conjunction with the protein p53 to predict and speculate cancer. The study examined the types of data incorporated and evaluated the performance of these techniques. The aim was to provide a thorough understanding of the effectiveness of machine learning in predicting and speculating cancer using the protein p53.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose a lesion-decoupling-based segmentation (LDS) network for assisting early cancer diagnosis. We introduce a plug-and-play module called self-sampling similar feature disentangling module (FDM) to obtain accurate lesion boundaries. Then, we propose a feature separation loss (FSL) function to separate pathological features from normal ones. Moreover, since physicians make diagnoses with multimodal data, we propose a multimodal cooperative segmentation network with two different modal images as input: white-light images (WLIs) and narrowband images (NBIs). Our FDM and FSL show a good performance for both single-modal and multimodal segmentations. Extensive experiments on five backbones prove that our FDM and FSL can be easily applied to different backbones for a significant lesion segmentation accuracy improvement, and the maximum increase of mean Intersection over Union (mIoU) is 4.58. For colonoscopy, we can achieve up to mIoU of 91.49 on our Dataset A and 84.41 on the three public datasets. For esophagoscopy, mIoU of 64.32 is best achieved on the WLI dataset and 66.31 on the NBI dataset.

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.

Methodologies to Isolate and Purify Clinical Grade Extracellular Vesicles for Medical Applications

The use of extracellular vesicles (EV) in nano drug delivery has been demonstrated in many previous studies. In this study, we discuss the sources of extracellular vesicles, including plant, salivary and urinary sources which are easily available but less sought after compared with blood and tissue. Extensive research in the past decade has established that the breadth of EV applications is wide. However, the efforts on standardizing the isolation and purification methods have not brought us to a point that can match the potential of extracellular vesicles for clinical use. The standardization can open doors for many researchers and clinicians alike to experiment with the proposed clinical uses with lesser concerns regarding untraceable side effects. It can make it easier to identify the mechanism of therapeutic benefits and to track the mechanism of any unforeseen effects observed.

Heat shock protein 20 promotes sirtuin 1-dependent cell proliferation in induced pluripotent stem cells

BACKGROUND

Heat shock proteins (HSPs) are molecular chaperones that protect cells against cellular stresses or injury. However, it has been increasingly recognized that they also play crucial roles in regulating fundamental cellular processes. HSP20 has been implicated in cell proliferation, but conflicting studies have shown that it can either promote or suppress proliferation. The underlying mechanisms by which HSP20 regulates cell proliferation and pluripotency remain unexplored. While the effect of HSP20 on cell proliferation has been recognized, its role in inducing pluripotency in human-induced pluripotent stem cells (iPSCs) has not been addressed.

AIM

To evaluate the efficacy of HSP20 overexpression in human iPSCs and evaluate the ability to promote cell proliferation. The purpose of this study was to investigate whether overexpression of HSP20 in iPSCs can increase pluripotency and regeneration.

METHODS

We used iPSCs, which retain their potential for cell proliferation. HSP20 overexpression effectively enhanced cell proliferation and pluripotency. Overexpression of HSP20 in iPSCs was characterized by immunocytochemistry staining and real-time polymerase chain reaction. We also used cell culture, cell counting, western blotting, and flow cytometry analyses to validate HSP20 overexpression and its mechanism.

RESULTS

This study demonstrated that overexpression of HSP20 can increase the pluripotency in iPSCs. Furthermore, by overexpressing HSP20 in iPSCs, we showed that HSP20 upregulated proliferation markers, induced pluripotent genes, and drove cell proliferation in a sirtuin 1 (SIRT1)-dependent manner. These data have practical applications in the field of stem cell-based therapies where the mass expansion of cells is needed to generate large quantities of stem cell-derived cells for transplantation purposes.

CONCLUSION

We found that the overexpression of HSP20 enhanced the proliferation of iPSCs in a SIRT1-dependent manner. Herein, we established the distinct crosstalk between HSP20 and SIRT1 in regulating cell proliferation and pluripotency. Our study provides novel insights into the mechanisms controlling cell proliferation that can potentially be exploited to improve the expansion and pluripotency of human iPSCs for cell transplantation therapies. These results suggest that iPSCs overexpressing HSP20 exert regenerative and proliferative effects and may have the potential to improve clinical outcomes.

Microbubbles versus Extracellular Vesicles as Therapeutic Cargo for Targeting Drug Delivery

Extracellular vesicles (EVs) and microbubbles are nanoparticles in drug-delivery systems that are both considered important for clinical translation. Current research has found that both microbubbles and EVs have the potential to be utilized as drug-delivery agents for therapeutic targets in various diseases. In combination with EVs, microbubbles are capable of delivering chemotherapeutic drugs to tumor sites and neighboring sites of damaged tissues. However, there are no standards to evaluate or to compare the benefits of EVs (natural carrier) versus microbubbles (synthetic carrier) as drug carriers. Both drug carriers are being investigated for release patterns and for pharmacokinetics; however, few researchers have focused on their targeted delivery or efficacy. In this Perspective, we compare EVs and microbubbles for a better understanding of their utility in terms of delivering drugs to their site of action and future clinical translation.