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.

How is artificial intelligence applied in solid tumor imaging?

How is artificial intelligence (AI) applied in solid tumor imaging? What is the essential value of AI for tumor precision diagnosis and can it wholly replace the human beings? Some opinions in this letter should be considered.

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.

Cancer recognition of artificial intelligence

The recognition mechanism of artificial intelligence (AI) is an interesting topic in understanding AI neural networks and their application in therapeutics. A number of multilayered neural networks can recognize cancer through deep learning. It would be interesting to think about whether human insights and AI attention are associated with each other or should be translated, which is one of the main points in this editorial. The automatic detection of cancer with computer-aided diagnosis is being applied in the clinic and should be improved with feature mapping in neural networks. The subtypes and stages of cancer, in terms of progression and metastasis, should be classified with AI for optimized therapeutics. The determination of training and test data during learning and selection of appropriate AI models will be essential for therapeutic applications.

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

Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to convert raw input data into decision-making actions, like humans. AI programs are designed to make decisions, often using deep learning and computer-guided programs that analyze and process raw data into clinical decision making for effective treatment. New techniques for predicting cancer at an early stage are needed as conventional methods have poor accuracy and are not applicable to personalized medicine. AI has the potential to use smart, intelligent computer systems for image interpretation and early diagnosis of cancer. AI has been changing almost all the areas of the medical field by integrating with new emerging technologies. AI has revolutionized the entire health care system through innovative digital diagnostics with greater precision and accuracy. AI is capable of detecting cancer at an early stage with accurate diagnosis and improved survival outcomes. AI is an innovative technology of the future that can be used for early prediction, diagnosis and treatment of cancer.