Surgery or Percutaneous Ablation for Liver Tumors? The Key Points Are: When, Where, and How Large

The most recent comparisons between liver resection (LR) and percutaneous thermal ablation (PTA) reported similar efficacy and survival outcomes for primary and secondary liver tumors ≤ 3 cm in size. Nevertheless, LR still remains the most popular treatment strategy worldwide, and percutaneous ablation is usually reserved to patients who are not surgical candidates. However, in our opinion, the debate should no longer be what is the most effective treatment for patients with resectable small liver cancer who are not candidates for liver transplantation, but rather when LR or PTA are best suited to the individual patient. Subcapsular tumors or tumors closely adjacent to critical structures or vulnerable organs should undergo LR because ablation can often not achieve an adequate safety margin. Conversely, PTA should be considered the first choice to treat central tumors because it has lower complication rates, lower costs, and shorter hospital stay. Furthermore, recent technical improvements in tumor targeting and accurate assessment of the extent of the safety margin, such as stereotactic navigation, fusion imaging and software powered by Artificial Intelligence enabling the immediate comparison between the pre-procedure planned margins and the ablation area, are also changing the approach to tumors larger than 3 cm. The next trials should be aimed at investigating up to what tumor size PTA supported by these advanced technologies can achieve outcomes comparable to LR.

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.

Assessing the quality of whole slide images in cytology from nuclei features

Background and objective

Implementation of machine learning and artificial intelligence algorithms into digital pathology laboratories faces several challenges, notably the variation in whole slide image preparation protocols. The diversity of preparation pipelines forces algorithms to be protocol-dependant. Moreover, the error susceptibility of each stage in the preparation process implies a need of quality control tools. To address these challenges, this article introduces a straightforward, interpretable, and computationally efficient quality control module to ensure optimal algorithmic performance.

Methods

The proposed quality control module ensures algorithmic performance by representing an algorithm by a reference whole slide image preparation protocol validated on it. Then, inspired by data description methods, a preparation protocol is represented by nuclei feature distributions, obtained for several whole slide images it has produced. The quality of a preparation protocol is evaluated according to several reference preparation protocols, by comparing their feature distributions with a weighted distance.

Results

Through empirical analysis conducted on seven distinct preparation protocols, we demonstrated that the proposed method build a quality module that clearly discriminates each preparation. Additionally, we showed that this module performs well on more larger and realistic corpus from laboratories routine, detecting quality deviations.

Conclusion

Even if the proposed method necessitates minimal data and few computational resources, we showed that it is interpretable and relevant on realistic corpus from several laboratories' routine. We strongly believe in the necessity of quality control from the algorithmic perspective and hope this kind of approach will be extended to improve quality and reliability of digital pathology whole slide images.

Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook

Background

Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL).

Objective

This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.

Methods

A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included "Artificial Intelligence (AI)," "Machine Learning (ML)," and "Deep Learning (DL)" combined with "chemotherapy development," "cancer diagnosis," and "cancer treatment." Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.

Conclusion

This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI's potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI's integration into oncology 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.

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images

IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the increasing use of enhanced CT scans, a new opportunity for early thyroid cancer screening has emerged. However, existing CT-based models face challenges due to limited datasets, small sample sizes, and high noise.MethodsTo address these challenges, we collected enhanced CT scan image data from 240 patients in Guangdong and Xinjiang, China, and established a CT dataset for early thyroid cancer screening. We propose a deep learning model, the DVT model, which combines transformer DNN and transfer learning techniques to integrate time series data and address small sample sizes and high noise.ResultsThe experimental results show that the DVT model achieves a prediction accuracy of 0.96, AUROC of 0.97, specificity of 1, and sensitivity of 0.94. These results indicate that the DVT model is a highly effective tool for early thyroid cancer screening.ConclusionThe DVT model has the potential to assist clinicians in identifying potential thyroid cancer patients and reducing patient expenses. Our study provides a new approach to thyroid cancer screening using enhanced CT scans and demonstrates the effectiveness of deep learning techniques in addressing the challenges associated with CT-based models.

Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives

Using artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies' design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies.

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.

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determinants of tumor entities and sub-entities. This is a task where artificial intelligence (AI)/machine learning (ML) systems could significantly contribute to help with tumor outcome prediction and the search for new diagnostic or treatment stratification biomarkers. AI systems are increasingly integrated into routine pathology workflows to improve accuracy, reproducibility, productivity and to reveal difficult-to-see features in complicated histological slides, including the quantification of important markers for tumor grading and staging. In this article, we review the infrastructure needed to facilitate digital and computational pathology. We address the barriers for its full deployment in the clinical setting and describe the use of AI in intraoperative or postoperative settings were frozen or formalin-fixed, paraffin-embedded materials are used. We also summarize quality assessment issues of slide digitization, new spatial biology approaches, and the determination of specific gene-expression from whole slide images. Finally, we highlight new innovative and future technologies, such as large language models, optical biopsies, and mass spectrometry imaging.

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.

Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review

Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The contamination of agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites of fungi) is a major risk to the health of the human population. Common methods for mycotoxin detection include chromatographic separation, often combined with mass spectrometry (accurate but time-consuming to prepare the sample and requiring skilled technicians). Artificial intelligence (AI) has been introduced as a new technique for mycotoxin detection in food, providing high credibility and accuracy. This review article provides an overview of recent studies on the use of AI methods for the discovery of mycotoxins in food. The new approach demonstrated that a variety of AI technologies could be correlated. Deep learning models, machine learning algorithms, and neural networks were implemented to analyze elaborate datasets from different analytical platforms. In addition, this review focuses on the advancement of AI to work concomitantly with smart sensing technologies or other non-conventional techniques such as spectroscopy, biosensors, and imaging techniques for rapid and less damaging mycotoxin detection. We question the requirement for large and diverse datasets to train AI models, discuss the standardization of analytical methodologies, and discuss avenues for regulatory approval of AI-based approaches, among other top-of-mind issues in this domain. In addition, this research provides some interesting use cases and real commercial applications where AI has been able to outperform other traditional methods in terms of sensitivity, specificity, and time required. This review aims to provide insights for future directions in AI-enabled mycotoxin detection by incorporating the latest research results and stressing the necessity of multidisciplinary collaboration among food scientists, engineers, and computer scientists. Ultimately, the use of AI could revolutionize systems monitoring mycotoxins, improving food safety and safeguarding global public health.

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.

Nucleic acid-based drugs for patients with solid tumours

The treatment of patients with advanced-stage solid tumours typically involves a multimodality approach (including surgery, chemotherapy, radiotherapy, targeted therapy and/or immunotherapy), which is often ultimately ineffective. Nucleic acid-based drugs, either as monotherapies or in combination with standard-of-care therapies, are rapidly emerging as novel treatments capable of generating responses in otherwise refractory tumours. These therapies include those using viral vectors (also referred to as gene therapies), several of which have now been approved by regulatory agencies, and nanoparticles containing mRNAs and a range of other nucleotides. In this Review, we describe the development and clinical activity of viral and non-viral nucleic acid-based treatments, including their mechanisms of action, tolerability and available efficacy data from patients with solid tumours. We also describe the effects of the tumour microenvironment on drug delivery for both systemically administered and locally administered agents. Finally, we discuss important trends resulting from ongoing clinical trials and preclinical testing, and manufacturing and/or stability considerations that are expected to underpin the next generation of nucleic acid agents for patients with solid tumours.

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.

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence's Pioneering Role in Colorectal Carcinoma Histopathology

Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis and prognosis. Artificial intelligence (AI) emerges as a transformative force in this realm, offering innovative solutions to enhance traditional histopathological methods. This narrative review explores AI's pioneering role in colorectal carcinoma histopathology, encompassing its evolution, techniques, and advancements. AI algorithms, notably machine learning and deep learning, have revolutionized image analysis, facilitating accurate diagnosis and prognosis prediction. Furthermore, AI-driven histopathological analysis unveils potential biomarkers and therapeutic targets, heralding personalized treatment approaches. Despite its promise, challenges persist, including data quality, interpretability, and integration. Collaborative efforts among researchers, clinicians, and AI developers are imperative to surmount these hurdles and realize AI's full potential in colorectal carcinoma care. This review underscores AI's transformative impact and implications for future oncology research, clinical practice, and interdisciplinary collaboration.

[Artificial intelligence in pathological anatomy]

The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence. The article also discusses three options for the further development of AI assistants for pathologists based on the technologies of large language models (strong AI) ChatGPT (PathAsst), Flan-PaLM2 and LIMA. As a result of the analysis of the literature, key problems in the field were identified: the equipment of pathology institutions, the lack of experts in training neural networks, the lack of strict criteria for the clinical viability of AI diagnostic technologies.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.

Impact of the COVID-19 on the surgical management of bone and soft tissue sarcoma: A systematic review

Background

The COVID-19 pandemic had greatly and negatively impacted health services including the management of bone and soft tissue sarcoma. As disease progression is time-sensitive, decision taken by the oncology orthopedic surgeon on performing surgical treatment determines the patient outcome. On the other hand, as the world tried to control the spread of COVID-19 infection, treatment re-prioritization based on urgency level had to be done which consequently affect treatment provision for sarcoma patients. Patient and clinician's concern regarding the outbreak have also inflicted on treatment decision making. A systematic review was thought to be necessary to summarize the changes seen in managing primary malignant bone and soft tissue tumors.

Methods

We performed this systematic review in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Statement. The review protocol had been registered on PROSPERO with submission number CRD42022329430. We included studies which reported primary malignant tumor diagnosis and its surgical intervention from March 11th, 2020 onwards. The main outcome is to report changes implemented by different centers around the world in managing primary malignant bone tumors surgically in response to the pandemic. Three electronic medical databases were scoured and by applying eligibility criteria. Individual authors evaluated the articles' quality and risk of bias using the Newcastle-Ottawa Quality Assessment Scale other instruments developed by JBI of the University of Adelaide. The overall quality assessment of this systematic review was self-evaluated using the AMSTAR (Assessing the Methodological Quality of Systematic Reviews) Checklist.

Results

There were 26 studies included in the review with various study designs, conveyed in almost all continents. The outcomes from this review are change in surgery time, change in surgery type, and change in surgery indication in patients with primary bone and soft tissue sarcoma. Surgery timing has been experiencing delay since the pandemic occurred, including delay in the multidisciplinary forum, which were all related to lockdown regulations and travel restrictions. For surgery type, limb amputation was preferred compared to limb-salvage procedures due to shorter duration and simpler reconstruction with better control of malignancy. Meanwhile, the indications for surgical management are still based on the patient's demographics and disease stages. However, some would stall surgery regardless of malignancy infiltration and fracture risks which are indication for amputation. As expected, our meta-analysis showed higher post-surgical mortality in patients with malignant bone and soft tissue sarcoma during the COVID-19 pandemic with odds ratio of 1.14.

Conclusion

Surgical management of patients with primary bone and soft tissue sarcoma has seriously been affected due to adjustments to the COVID-19 pandemic. Other than institutional restrictions to contain the infection, patient and clinician's decisions to postpone treatment due to COVID-19 transmission concern were also impactful in treatment course. Delay in surgery timing has caused higher risk of worse surgical outcome during the pandemic, which is aggravated if the patient is infected by COVID-19 as well. As we transition into a post-COVID-19 pandemic period, we expect patients to be more lenient in returning for their treatment but by then disease progression might have taken place, resulting in worse overall prognosis. Limitation to this study were few assumptions made in the synthesis of numerical data and meta-analysis only for changes in surgery time outcome and lack of intervention studies included.

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.

Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

Esophageal cancer (EC) is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa. It has been confirmed that early EC lesions can be cured by endoscopic therapy, and the curative effect is equivalent to that of surgical operation. Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis. The accuracy of endoscopic examination results largely depends on the professional level of the examiner. Artificial intelligence (AI) has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. This paper reviews the application of AI in the field of endoscopic detection of early EC, including squamous cell carcinoma and adenocarcinoma, and describes the relevant progress. Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images, AI-assisted real-time detection based on live-stream video may be the next step.

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.