Technological advancements in cancer diagnostics: Improvements and limitations

Diagnostic tools in respiratory medicine (Review)

Recent advancements in diagnostic technologies have significantly transformed the landscape of respiratory medicine, aiming for early detection, improved specificity and personalized therapeutic strategies. Innovations in imaging such as multi-slice computed tomography (CT) scanners, high-resolution CT and magnetic resonance imaging (MRI) have revolutionized our ability to visualize and assess the structural and functional aspects of the respiratory system. These techniques are complemented by breakthroughs in molecular biology that have identified specific biomarkers and genetic determinants of respiratory diseases, enabling targeted diagnostic approaches. Additionally, functional tests including spirometry and exercise testing continue to provide valuable insights into pulmonary function and capacity. The integration of artificial intelligence is poised to further refine these diagnostic tools, enhancing their accuracy and efficiency. The present narrative review explores these developments and their impact on the management and outcomes of respiratory conditions, underscoring the ongoing shift towards more precise and less invasive diagnostic modalities in respiratory medicine.

Evaluating the feasibility of region-of-interest X-ray phase contrast imaging for lung cancer diagnostics

Lung cancer is one of the world's deadliest cancers, often not diagnosed until it has spread beyond the lung, in part due to limitations in current medical imaging. Insufficient detail in diagnostic images can mean that patients require a biopsy to gain a full understanding of their prognosis. Here, we investigate the use of propagation-based X-ray phase-contrast imaging to capture high-resolution region-of-interest 3D Computed-Tomography (CT) images of suspicious masses, using a human chest phantom. In this study, we imaged a 3.5 cm region within the chest phantom, with each CT slice amounting to approximately 1% of the area of the whole chest at that height. X-ray energies ranging from 50 to 80 keV and propagation distances of 0 to 7 m were tested. We were able to quantify the experimental parameters that would be required to increase soft-tissue sensitivity and spatial resolution relative to conventional X-ray imaging methods, and hence improve the capacity for tumour characterisation. Our results suggest that propagation-based phase-contrast region-of-interest CT imaging could enable better tumour visualisation, which may aid in earlier detection and a better outcome for lung cancer patients.

Thyroid Cancer: Epidemiology, Classification, Risk Factors, Diagnostic and Prognostic Markers, and Current Treatment Strategies

Thyroid cancer (TC) invariably remains the most prevalent endocrine cancer in the world. Major histological forms of TC include papillary (PTC), follicular (FTC), medullary (MTC), and anaplastic thyroid carcinoma (ATC), each of which has a unique clinical and molecular profile. The incidence rate of TC is higher in females, and unfortunately, it has tended to increase over the last several years. Yet the treatment of advanced or aggressive TC forms has improved recently because of developments in immunotherapy and targeted medicines, including PD-1 inhibitors and tyrosine kinase inhibitors (e.g., lenvatinib, sorafenib). Imaging, fine-needle aspiration biopsies, and molecular testing are implemented in the diagnostic process, e.g., in search of mutations that might affect prognosis and provide the most successful treatment option. Chemotherapy, immunotherapy, radioactive iodine therapy (RAI), surgery (such as a total thyroidectomy), and molecularly targeted therapies are currently standard treatment modalities in TC. Optimizing patient outcomes requires better diagnostic precision and individualized treatment regimens based on the genetic profile and tumor subtype. To improve survival and quality of life, it is critical to comprehend the complex etiology of TC and the changing therapeutic landscape.

New Fluorescent Synthetic Retinoids as Potential RAR Agonists with Anticancer, Molecular Docking and ADME Assessments

Natural and synthetic retinoids are derivatives of vitamin A that mediate different transcriptional activities. Synthesis of fluorescent probes can be a useful tool in cellular imaging and understanding different regulatory signaling pathways. In this study, new fluorescent synthetic retinoid analogues (3a, 3b, 4a, and 4b) were prepared and biologically screened against six different cancer and normal cell lines for assessment of their anticancer and fluorescent activities. A combination of biological assays, such as MTT, flow cytometry, gene and protein expression analysis, DNA fragmentation, and ATPase activity assays, as well as in-silico and ADME studies, was performed. All new compounds showed anti-proliferative activity (2.09-132.70 µM) against different cancer cell types with minimal cytotoxicity (SI > 1), and the Caco-2 cancer cell line was selected for further in vitro investigation. All compounds showed cell cycle arrest at the SubG0-G1 phase with induction of early apoptosis and necrosis. Compound 4b showed a significant apoptotic effect by overexpression of Caspase-3 and Cyt-c genes, followed by compound 3a. All compounds showed anti-inflammatory activity by significantly down-regulating the IL-10 pro-inflammatory marker, while compounds 3b, 4a, and 4b specifically down-regulated IL-6. Retinoic acid receptors (RARs) were suggested as molecular targets confirmed by their overexpression of both gene and protein levels with molecular docking and molecular dynamic simulation studies. All fluorescent compounds showed intracellular fluorescent emission spectra with intracellular lipophilic properties. The new fluorescent synthetic retinoids showed dual activities as anticancer agents with fluorescence properties and can be used as useful probes in a variety of cellular imaging investigations.

Global Burden of Rare Cancers: Insights from GLOBOCAN 2022 Estimates

Background: Rare cancers, defined as those with an incidence rate of fewer than 6 cases per 100,000 individuals, contribute to a substantial portion of the global cancer burden. Despite their impact, they receive less attention than more common malignancies, leading to challenges in early detection, treatment strategies, and research funding. This study aims to assess the global incidence and mortality patterns of rare cancers in 2022 to better understand their contribution to the overall cancer burden and regional disparities. Methods: We conducted a comprehensive analysis using 2022 GLOBOCAN estimates to assess the global incidence and mortality of rare cancers. The study included 24 major rare cancers, such as bladder cancer, leukemia, non-Hodgkin lymphoma, esophageal cancer, and pancreatic cancer. Age-standardized rates (ASRs) were calculated to compare cancer burden across continents, and absolute case and death counts were reported. Results: In 2022, rare cancers accounted for 26.7% of all new cancer cases (5,347,784 cases) and 30% of all cancer-related deaths (2,959,369 deaths) worldwide. Bladder cancer was the most common rare cancer, with an incidence rate of 5.58 per 100,000, followed by non-Hodgkin lymphoma (5.57) and leukemia (5.26). Mortality rates were highest for pancreatic, esophageal, and brain cancers, reflecting their aggressive nature and limited treatment options. Significant regional disparities were observed, with Europe and North America reporting the highest incidence rates for bladder cancer and leukemia, while Asia bore the largest absolute burden of rare cancers. Conclusions: Rare cancers represent a considerable share of the global cancer burden, with notable geographic variations in incidence and mortality. These findings underscore the need for improved early detection, expanded treatment access, and targeted research efforts to address disparities and improve outcomes for patients with rare cancers worldwide.

Multiclassification of Colorectal Polyps from Colonoscopy Images Using AI for Early Diagnosis

Background/Objectives: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the critical need for the accurate classification of precancerous polyps. This research presents an extensive analysis of the multiclassification framework leveraging various deep learning (DL) architectures for the automated classification of colorectal polyps from colonoscopy images. Methods: The proposed methodology integrates real-time data for training and utilizes a publicly available dataset for testing, ensuring generalizability. The real-time images were cautiously annotated and verified by a panel of experts, including post-graduate medical doctors and gastroenterology specialists. The DL models were designed to categorize the preprocessed colonoscopy images into four clinically significant classes: hyperplastic, serrated, adenoma, and normal. A suite of state-of-the-art models, including VGG16, VGG19, ResNet50, DenseNet121, EfficientNetV2, InceptionNetV3, Vision Transformer (ViT), and the custom-developed CRP-ViT, were trained and rigorously evaluated for this task. Results: Notably, the CRP-ViT model exhibited superior capability in capturing intricate features, achieving an impressive accuracy of 97.28% during training and 96.02% during validation with real-time images. Furthermore, the model demonstrated remarkable performance during testing on the public dataset, attaining an accuracy of 95.69%. To facilitate real-time interaction and clinical applicability, a user-friendly interface was developed using Gradio, allowing healthcare professionals to upload colonoscopy images and receive instant classification results. Conclusions: The CRP-ViT model effectively predicts and categorizes colonoscopy images into clinically relevant classes, aiding gastroenterologists in decision-making. This study highlights the potential of integrating AI-driven models into routine clinical practice to improve colorectal cancer screening outcomes and reduce diagnostic variability.

Overall Survival of Hungarian Cancer Patients Diagnosed Between 2011 and 2019, Based on the Health Insurance Fund Database

Background: Assessing cancer survival trends is crucial for monitoring progress in cancer management and prevention. As part of the broader HUN-CANCER EPI study, this analysis examined overall survival (OS) in the Hungarian cancer population between 2011 and 2019. Methods: Using data extracted from the Hungarian National Health Insurance Fund (NHIF) database, short- and long-term OS were estimated for various cancer types according to age, sex, and diagnostic period using Kaplan-Meier analysis. The study also identified cancer types with significant early mortality following diagnosis. Results: From 2011 to 2019, a total of 528,808 patients were diagnosed with cancer. During the 2015-2019 diagnostic period, the lowest 5-year OS rates were observed for esophageal (7.0%), pancreatic (10.7%), liver (12.5%), gallbladder (13.9%), and lung cancer (18.4%). Conversely, tumor types with better OS included testicular cancer (91.6%), thyroid cancer (89.0%), Hodgkin's lymphoma (84.0%), melanoma (78.6%), and breast cancer (74.1%). A notable proportion of deaths occurred within 2 months of diagnosis for liver (33.2%), pancreatic (27.9%), and gallbladder cancer (29.0%). Significant early mortality within 6 months post-diagnosis was also noted for esophageal (51.3%), stomach (42.9%), and lung cancer (41.7%). Conclusions: The HUN-CANCER EPI study conducted between 2011 and 2019 provides valuable insights into cancer survival patterns in Hungary, emphasizing the importance of early detection and targeted interventions to improve patient outcomes.

Current updates on the structural and functional aspects of the CRISPR/Cas13 system for RNA targeting and editing: A next‑generation tool for cancer management (Review)

For centuries, a competitive evolutionary race between prokaryotes and related phages or other mobile genetic elements has led to the diversification of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR‑associated sequence (Cas) genome‑editing systems. Among the different CRISPR/Cas systems, the CRISPR/Cas9 system has been widely studied for its precise DNA manipulation; however, due to certain limitations of direct DNA targeting, off‑target effects and delivery challenges, researchers are looking to perform transient knockdown of gene expression by targeting RNA. In this context, the more recently discovered type VI CRISPR/Cas13 system, a programmable single‑subunit RNA‑guided endonuclease system that has the capacity to target and edit any RNA sequence of interest, has emerged as a powerful platform to modulate gene expression outcomes. All the Cas13 effectors known so far possess two distinct ribonuclease activities. Pre‑CRISPR RNA processing is performed by one RNase activity, whereas the two higher eukaryotes and prokaryotes nucleotide‑binding domains provide the other RNase activity required for target RNA degradation. Recent innovative applications of the type VI CRISPR/Cas13 system in nucleic acid detection, viral interference, transcriptome engineering and RNA imaging hold great promise for disease management. This genome editing system can also be employed by the Specific High Sensitivity Enzymatic Reporter Unlocking platform to identify any tumor DNA. The discovery of this system has added a new dimension to targeting, tracking and editing circulating microRNA/RNA/DNA/cancer proteins for the management of cancer. However, there is still a lack of thorough understanding of the mechanisms underlying some of their functions. The present review summarizes the recent updates on the type VI CRISPR/Cas system in terms of its structural and mechanistic properties and some novel applications of this genome‑editing tool in cancer management. However, some issues, such as collateral degradation of bystander RNA, impose major limitations on its in vivo application. Furthermore, additional challenges and future prospects for this genome editing system are described in the present review.

Shugoshin 1 expression in various cancers: a potential target for therapy

Shugoshin 1 (SGO1) is one of the Shugoshin (guardian spirit) family proteins, which is reported to be majorly involved in the protection of centromeres and proper segregation of chromosomes during cell division. Recent studies found that the altered expression of SGO1 is associated with various cancers and genetic disorders, and suggested as a target for therapy. In the present study, we have reviewed the available literature on SGO1 gene and protein expression in various cancer-cell lines, animal models and cancer patients, and targeting SGO1 with siRNA/shRNA. A significant increase in the expression of SGO1 mRNA and protein levels were observed in prostate, renal, lung, breast, neuroblastoma, leukemia, hepatocellular, and colon cancer-cell lines and the levels were associated with increased cellular proliferation, invasion, and metastasis. The altered SGO1 levels were observed in SGO1 knockout/haploinsufficient mice compared to wild type and the levels were associated with increased chromosome instability and tumorigenesis. Consistent with cell lines, higher SGO1 expression was also observed in tumor tissues of cancer patients compared to adjacent normal tissue and the levels were positively correlated with tumor stage, grade, size, and hormonal status. Higher SGO1 expression was related to resistance to chemotherapeutic agents and the knockdown of SGO1 increased sensitivity to those agents. Furthermore, targeting SGO1 with siRNA/shRNA reduced the expression of SGO1 and proliferation, and induced apoptosis of cancer cells. Overall, the SGO1 expression levels were significantly higher in various cancers, and targeting SGO1 with siRNA and shRNA reduced the levels of SGO1, proliferation and metastasis of cancers.

Recent advances in DNA nanotechnology for cancer detection and therapy: A review

Deoxyribonucleic acid (DNA) nanotechnology has rapidly emerged as a transformative field in biomedical research, offering innovative solutions for the detection and treatment of cancer. This review provides a comprehensive analysis of the role of DNA-based nanosystems in oncology, emphasizing their potential to address the limitations of conventional diagnostic and therapeutic approaches. Key advancements in DNA nanotechnology include the development of highly specific and sensitive nanostructures for early cancer detection, as well as precision-targeted delivery systems that enhance the efficacy of cancer therapies while minimizing side effects. The objectives of this review are threefold: first, to summarize the latest advancements in DNA nanotechnology, highlighting innovations in cancer biomarker detection and therapeutic applications; second, to explore the molecular mechanisms that enable these DNA-based nanosystems to interact with cancer cells with remarkable precision, including their design principles, self-assembly processes, and biological interactions; and third, to discuss the future implications of these technologies, considering the challenges, potential breakthroughs, and the steps needed to integrate DNA nanotechnology into clinical practice. By achieving these objectives, the review aims to offer insights into how DNA nanotechnology could revolutionize cancer care, providing new strategies for more personalized and effective treatments, and ultimately improving patient outcomes in the battle against cancer.

Evaluating the Diagnostic Potential of Biomarker Panels in Breast Cancer and Prostate Adenocarcinoma

Background

Noninvasive diagnostic methods are essential for early cancer detection and improved patient outcomes. Circulating biomarkers, measurable indicators of pathological processes, offer a promising avenue, yet optimal panels for reliable cancer diagnosis remain undefined. This study evaluates the diagnostic performance of selected plasma biomarkers in distinguishing breast cancer and prostate adenocarcinoma patients from healthy individuals, using statistical analysis and machine learning.

Materials and Methods

We analyzed blood samples from 162 participants (73 cancer patients: 51 with breast cancer and 22 with prostate adenocarcinoma; 89 healthy controls). Levels of 12 cancer-associated biomarkers-including Ki67, DNMT1, BRCA1, and MPO-were quantified using enzyme-linked immunosorbent assays (ELISA). Statistical analyses, including the Mann-Whitney U test and machine learning models (random forest), were employed to assess the predictive accuracy of these biomarkers in distinguishing between cancerous and healthy states.

Results

Biomarkers such as Ki67, DNMT1, and MPO were significantly elevated in cancer groups. Random forest models using selected combinations (e.g., BRCA1-CTA-TP53) achieved perfect classification accuracy (AUC = 1.00). However, high inter-marker correlations suggested potential redundancy, underscoring the need for biomarker panel optimization.

Conclusion

Our findings support the potential of biomarker panels for accurate, noninvasive cancer diagnostics. Further validation in larger, more diverse cohorts is warranted to establish clinical utility and generalizability.

Evaluating Accountable Care Organizations impact on gastrointestinal cancer care: are they falling short on health outcomes?

BACKGROUND

Accountable Care Organizations (ACOs) have emerged as a value-based care model. However, the effect on gastrointestinal (GI) cancer care remains poorly defined. Therefore, the current study aimed to investigate the effect of hospital ACO participation on surgical and financial outcomes.

METHODS

Patients who underwent GI cancer surgery between 2016 and 2020 were identified from the Medicare Standard Analytic Files. Difference-in-differences (DID) analysis was performed to examine the effect of hospital ACO participation on surgical outcomes and healthcare expenditures.

RESULTS

A total of 23,357 beneficiaries underwent GI cancer surgery at 2180 hospitals (ACO participating: 57 [2.7%]). The median patient age was 75 years (IQR, 71-81). Moreover, most patients were female (12,207 [52.3%]) and had a Charlson Comorbidity Index score of >2 (14,067 [54.3%]). On DID analysis, after adjusting for patient and hospital baseline characteristics, ACO participation was not associated with a reduced risk of complications during the index hospitalization (RRR [relative risk ratio], 1.03 [95% CI, 0.90-1.18]; P =.71), extended length of stay (RRR, 1.04 [95% CI, 0.92-1.67]; P =.52), 30-day complication (RRR, 1.96 [95% CI, 0.72-1.27]; P =.77), 30-day readmission (RRR, 1.07 [95% CI, 0.92-1.25]; P =.96), or 30-day mortality (RRR, 0.96 [95% CI, 0.77-1.21]; P =.74). Moreover, ACO participation was not associated with reduced healthcare costs.

CONCLUSION

Hospital participation in ACOs was not associated with improved surgical outcomes or reduced healthcare costs. There is a need for nuanced, targeted interventions within the ACO framework that address the unique needs of complex surgical patients.

Ultra-sensitive graphene micro-ribbon integrated THz biosensor for breast cancer cell detection

In recent decades, the rising incidence of cancer has made early and rapid diagnosis, along with precise characterization of cancer cells, more crucial than ever. The paper presents a novel metasurface-assisted biosensor operating in the THz regime, designed for non-invasive and rapid detection of breast cancer cells. The proposed biosensor incorporates graphene micro-ribbons to enhance THz wave interaction, boosting the biosensor's sensitivity and overall performance. When used for cancer cell sensing, the biosensor demonstrates three absorption peaks at 2.0012 THz, 2.8734 THz, and 3.2948 THz with the absorption of 99.18 %, 89.55 %, and 99.93 %, respectively. The biosensor achieves a maximum frequency shift of 49 GHz, a maximum theoretical sensitivity of 3.5 THz/RIU (Refractive Index Unit), and a figure of merit of 6.81 RIU-1. Additionally, the sensor offers an excellent detection limit of 0.26 RIU and a resolution of 0.91 THz. The ability of the proposed biosensor to detect small refractive index changes (as low as 0.26 RIU) adds to the sensor's versatility, allowing it to be used in a wide variety of clinical and laboratory settings. Given these features and performance, the proposed biosensor holds great promise for non-invasive cancer diagnostics, offering ultra-high sensitivity in a portable and miniaturized platform.

Development and Metrological Characterization of Low-Cost Wearable Pulse Oximeter

Pulse oximetry is essential for monitoring arterial oxygen saturation (SpO2) and heart rate (HR) in various medical scenarios. However, the traditional pulse oximeters face challenges related to high costs, motion artifacts, and susceptibility to ambient light interference. This work presents a low-cost experimental pulse oximeter prototype designed to address these limitations through design advancements. The device incorporates a 3D-printed finger support to minimize motion artifacts and excessive capillary pressure, along with an elastic element to enhance stability. Unlike conventional transmission-based oximetry, the prototype employs a reflectance-based measurement approach, improving versatility and enabling reliable readings even in cases of poor peripheral perfusion. Additionally, the integration of light-shielding materials mitigates the effects of ambient illumination, ensuring accurate operation in challenging environments such as surgical settings. Metrological characterization demonstrates that the prototype achieves accuracy comparable to that of the commercial GIMA Oxy-50 pulse oximeter while maintaining a production cost at approximately one-tenth of the commercial alternatives. This study highlights the potential of the prototype to deliver affordable and reliable pulse oximetry for different applications.

Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions

Accurate detection and segmentation of esophageal lesions are crucial for diagnosing and treating gastrointestinal diseases. However, early detection of esophageal cancer remains challenging, contributing to a reduced five-year survival rate among patients. This paper introduces a novel multitask deep learning model for automatic diagnosis that integrates classification and segmentation tasks to assist endoscopists effectively. Our approach leverages the MobileNetV2 deep learning architecture enhanced with a mutual attention module, significantly improving the model's performance in determining the locations of esophageal lesions. Unlike traditional models, the proposed model is designed not to replace endoscopists but to empower them to correct false predictions when provided with additional Supporting Information. We evaluated the proposed model on three well-known data sets: Early Esophageal Cancer (EEC), CVC-ClinicDB, and KVASIR. The experimental results demonstrate promising performance, achieving high classification accuracies of 98.72% (F1-score: 98.08%) on CVC-ClinicDB, 98.95% (F1-score: 98.32%) on KVASIR, and 99.12% (F1-score: 99.00%) on our generated EEC data set. Compared to state-of-the-art models, our classification results show significant improvement. For the segmentation task, the model attained a Dice coefficient of 92.73% and an Intersection over Union (IoU) of 91.54%. These findings suggest that the proposed multitask deep learning model can effectively assist endoscopists in evaluating esophageal lesions, thereby alleviating their workload and enhancing diagnostic precision.

Transforming Healthcare in Low-Resource Settings With Artificial Intelligence: Recent Developments and Outcomes

BACKGROUND

Artificial intelligence now encompasses technologies like machine learning, natural language processing, and robotics, allowing machines to undertake complex tasks traditionally done by humans. AI's application in healthcare has led to advancements in diagnostic tools, predictive analytics, and surgical precision.

AIM

This comprehensive review aims to explore the transformative impact of AI across diverse healthcare domains, highlighting its applications, advancements, challenges, and contributions to enhancing patient care.

METHODOLOGY

A comprehensive literature search was conducted across multiple databases, covering publications from 2014 to 2024. Keywords related to AI applications in healthcare were used to gather data, focusing on studies exploring AI's role in medical specialties.

RESULTS

AI has demonstrated substantial benefits across various fields of medicine. In cardiology, it aids in automated image interpretation, risk prediction, and the management of cardiovascular diseases. In oncology, AI enhances cancer detection, treatment planning, and personalized drug selection. Radiology benefits from improved image analysis and diagnostic accuracy, while critical care sees advancements in patient triage and resource optimization. AI's integration into pediatrics, surgery, public health, neurology, pathology, and mental health has similarly shown significant improvements in diagnostic precision, personalized treatment, and overall patient care. The implementation of AI in low-resource settings has been particularly impactful, enhancing access to advanced diagnostic tools and treatments.

CONCLUSION

AI is rapidly changing the healthcare industry by greatly increasing the accuracy of diagnoses, streamlining treatment plans, and improving patient outcomes across a variety of medical specializations. This review underscores AI's transformative potential, from early disease detection to personalized treatment plans, and its ability to augment healthcare delivery, particularly in resource-limited settings.

Bone tumors: a systematic review of prevalence, risk determinants, and survival patterns

BACKGROUND

Though relatively rare, bone tumors significantly impact patient health and treatment outcomes.

OBJECTIVE

This systematic review analyzes the incidence, types, survival rates, and risk factors associated with bone tumors, including both benign and malignant forms.

METHODS

This systematic review was conducted using the keywords "bone tumors," "epidemiology," "benign bone tumors," "malignant bone tumors," "osteosarcoma," "Ewing sarcoma," "chondrosarcoma," "risk factors," and "survival" in electronic databases including PubMed, Scopus, Web of Science, and Google Scholar from 2000 to 2024. The search strategy was based on the PRISMA statement. Finally, 9 articles were selected for inclusion in the study.

RESULTS

The systematic review highlights that primary bone tumors can be classified into benign and malignant types, with osteosarcoma being the most prevalent malignant form, particularly among adolescents and young adults. The epidemiology of bone tumors is influenced by factors such as age, gender, geographic location, and genetic predispositions. Recent advancements in imaging techniques have improved the detection of these tumors, contributing to an increasing recognition of their prevalence. Data shows that the limited-duration prevalence of malignant bone tumors has increased significantly. This increase is from 0.00069% in 2000 to 0.00749% in 2018, indicating an increasing recognition and diagnosis of these rare tumors over time. Survival rates vary significantly by tumor type, with approximately 50-60% for osteosarcoma and around 70% for Ewing's sarcoma, though these rates decrease with metastasis. Key risk factors identified include genetic predispositions such as Li-Fraumeni syndrome and TP53 mutations, environmental exposures like radiation, and growth patterns related to height.

CONCLUSION

The review highlights the importance of early diagnosis and treatment intervention, as survival rates are significantly better for patients with localized disease compared to those with metastatic conditions. The observed variations in survival rates across different tumor types underscore the need for tailored treatment strategies. Key risk factors include genetic predispositions and environmental exposures, highlighting the need for targeted screening and ongoing research to enhance diagnostic accuracy and treatment strategies.

Integrating alternative therapies in overcoming chemotherapy resistance in tumors

Chemotherapy-resistant tumors present a significant challenge in oncology, often leading to treatment failures owing to mechanisms such as genetic mutations, drug efflux, altered metabolism, and adaptations within the tumor microenvironment. These factors limit the effectiveness of treatment and contribute to tumor resistance. This review highlights the role of alternative therapies aimed at overcoming resistance mechanisms. Several alternative strategies with high efficacy rate against tumor resistance are being explored, including targeted therapies (58-64%), immunotherapy (80%), hormone therapy (22-61%), and emerging approaches such as herbal therapies (90%), probiotics (34-90%), metabolic therapies (> 50%), epigenetic therapies (51-89%), microbiome-based therapies (50%), gene therapy (67-80%), photodynamic therapy/hypothermia (86-99%), and nanotechnology (50-67%). Integrating these alternative strategies with conventional treatments has the potent-al to augment the therapeutic efficacy and patient outcomes. Despite this progress, limitations in cancer therapeutics include the lack of predictive biomarkers, resistance mechanisms, and tumor heterogeneity, all of which contribute to treatment failure and relapse. To address these limitations, advancements in molecular diagnostics, as well as early detection through liquid biopsies, and the use of biomarkers to monitor resistance and guide treatment are crucial. Additionally, expanding clinical trials is essential to validate new therapies and improve patient outcomes.

Comprehensive pan-cancer analysis of LAMA3: implications for prognosis and immunotherapy

OBJECTIVES

Laminin subunit alpha 3 (LAMA3) has been implicated in various cellular processes relevant to cancer progression, including cell proliferation, migration, and adhesion. In this study, we explored the expression, prognostic significance, and functional role of LAMA3 across multiple cancer types.

METHODOLOGY

The in silico analyses involve using various bioinformatics tools and databases, such as The Cancer Genome Atlas (TCGA), TIMER2.0, GEPIA2, UALCAN, Kaplan-Meier (KM) plotter, GENT2, Human Protein Atlas (HPA), OncoDB, Gene Set Cancer Analysis (GSCA), and TISIDB. The in vitro analyses include cell culture, gene knockdown, and assays for cell proliferation, colony formation, and wound healing.

RESULTS

Pan-cancer analysis revealed significant variations in LAMA3 expression, with upregulation observed in cancers such as pancreatic adenocarcinoma (PAAD) and stomach adenocarcinoma (STAD), and downregulation in breast cancer (BRCA) and colon adenocarcinoma (COAD). Prognostic analyses indicated high LAMA3 expression correlated with poor overall survival (OS) in PAAD and STAD, whereas low expression was associated with adverse outcomes in BRCA. Validation analysis confirmed differential expression and localized LAMA3 primarily to the endoplasmic reticulum. Analysis of clinical features in BRCA, PAAD, and STAD showed consistent expression trends across different stages, races, and age groups. Additionally, mutational and copy number variations (CNVs) analyses revealed prevalent heterozygous amplifications and deletions in LAMA3 across BRCA, PAAD, and STAD. Promoter methylation was inversely correlated with LAMA3 expression in BRCA, PAAD, and STAD, although survival outcomes were unaffected. Protein-protein interaction (PPI) and gene enrichment analyses indicated LAMA3's involvement in ECM-receptor interactions and PI3K-Akt signaling, pathways critical in cancer. Finally, functional assays following LAMA3 knockdown in HT-29 cells demonstrated reduced cell proliferation, colony formation, and wound healing, implicating LAMA3 in tumor growth and metastasis.

CONCLUSION

Overall, these findings suggest that LAMA3 plays a multifaceted role in tumorigenesis and holds potential as a prognostic biomarker and therapeutic target in multiple cancers.

The role of Prolyl 3-Hydroxylase 1 (P3H1) in tumor development and prognosis: a pan-cancer analysis with validation in colonic adenocarcinoma

BACKGROUND

Cancer is a multifaceted disease characterized by unregulated cell proliferation, evasion of apoptosis, and metastasis. Recent studies have highlighted the importance of extracellular matrix remodeling and post-translational modifications in tumorigenesis. Prolyl 3-hydroxylase 1 (P3H1), an enzyme involved in collagen hydroxylation, has gained attention for its role in cancer progression.

METHODS

This study investigates P3H1 expression, prognostic value, and functional relevance across multiple human cancers using a combination of bioinformatic and experimental approaches.

RESULTS

Using The Cancer Genome Atlas (TCGA) data from TIMER2.0 and UALCAN databases, we observed a significant upregulation of P3H1 mRNA and protein in various cancers. Prognostic analysis using GEPIA2 and KM plotter revealed that high P3H1 expression correlates with poorer overall survival in colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC). Further, genetic and promoter methylation analyses showed low mutation frequencies and reduced methylation of P3H1 in specific cancer types. Functional and pathway enrichment analyses indicated that P3H1 is involved in collagen formation, endoplasmic reticulum activity, and pathways such as ECM-receptor interaction and PI3K-Akt signaling. Validation by enzyme linked immunosorbent assay in COAD patient serum samples demonstrated significantly elevated P3H1 levels compared to healthy controls, with an AUC approaching 1.0 by receiver operating characteristic (ROC) curve analysis. This suggests its potential as a diagnostic biomarker. Additionally, functional experiments were conducted in COAD cells to assess P3H1's role in tumorigenesis. Knockdown of P3H1 in HCT116 cells resulted in a significant reduction in cell proliferation, colony formation, and migratory abilities of these cells.

CONCLUSION

These findings emphasize P3H1's relevance in COAD, KIRC, and LIHC pathogenesis and possible utility in clinical diagnosis.

Nano-Radiopharmaceuticals in Colon Cancer: Current Applications, Challenges, and Future Directions

Colon cancer remains a significant global health challenge; however, the treatment outcome for colon patients can be improved through early detection and effective treatment. Nano-radiopharmaceuticals, combining nanotechnology with radiopharmaceuticals, are emerging as a revolutionary approach in both colon cancer diagnostic imaging and therapy, playing a significant role in the management of colon cancer patients. This review examines the use of nano-radiopharmaceuticals in the diagnosis and treatment of colon cancer, highlighting current applications, challenges, and future directions. Nanocarriers of radionuclides have shown potential in improving cancer treatment, including liposomes, microparticles, nanoparticles, micelles, dendrimers, and hydrogels, which are approved by the FDA. These nanocarriers can deliver targeted drugs into malignant cells without affecting normal cells, reducing side effects. Antibody-guided systemic radionuclide-targeted therapy has shown potential for treating cancer. Novel cancer nanomedicines, like Hensify and 32P BioSilicon, are under clinical development for targeted radiation delivery in percutaneous intratumoral injections. Although using nano-radiopharmaceuticals is a superior technique for diagnosing and treating colon cancer, there are limitations and challenges, such as the unintentional accumulation of nanoparticles in healthy tissues, which leads to toxicity due to biodistribution issues, as well as high manufacturing costs that limit their availability for patients. However, the future direction is moving toward providing more precise radiopharmaceuticals, which is crucial for enhancing the diagnosis and treatment of colon cancer and reducing production costs.

Hyaluronic Acid-Coated SPIONs with Attached Folic Acid as Potential T2 MRI Contrasts for Anticancer Therapies

Superparamagnetic iron oxide nanoparticles (SPIONs) are known to be good MRI contrasts, but they have a high tendency to aggregate and their biocompatibility is limited. Hyaluronic acid is highly biocompatible, can provide SPION with colloidal stability, and interacts specifically with tumor cells through the CD44 receptor; therefore, it was used as a stabilizing layer. We successfully obtained SPION coated with hyaluronic acid and further functionalized it with folic acid to construct a dual-targeted system. The physicochemical properties of the nanoparticles were investigated using DLS/ELS, AFM, XRD, and ATR-FTIR. Their magnetic characterization was performed by magnetometry, Mössbauer spectroscopy, 1H NMR T1 and T2 measurements, and MRI. The nanoparticles' biocompatibility was verified on blood and hepatocytes, and their cytotoxicity was tested on glioma and adenocarcinoma cells using the MTT assay. The nanoparticles were spherical, colloidally stable, and had low dispersity. Their cores were formed by 7 nm crystallites of magnetite in its oxidized form, maghemite. Our SPIONs were superparamagnetic and could potentially serve as effective T2 contrasts for MRI. The performance of SPIONs modified with folic acid was superior to that observed for commercial contrasts. Our nanoparticles were also hemocompatible and were efficiently taken up by glioblastoma cancer cells. Folic acid-modified SPIONs could also reduce viability of tumor cells in a dose-dependent manner. Thus, the proposed system has potential application as both a diagnostic tool and a therapeutic agent for targeted anticancer therapies.

Magnetic-Plasmonic Core-Shell Nanoparticles: Properties, Synthesis and Applications for Cancer Detection and Treatment

Nanoparticles have been widely used in cancer diagnostics and treatment research due to their unique properties. Magnetic nanoparticles are popular in imaging techniques due to their ability to alter the magnetization field around them. Plasmonic nanoparticles are mainly applied in cancer treatments like photothermal therapy due to their ability to convert light into heat. While these nanoparticles are popular among their respective fields, magnetic-plasmonic core-shell nanoparticles (MPNPs) have gained popularity in recent years due to the combined magnetic and optical properties from the core and shell. MPNPs have stood out in cancer theranostics as a multimodal platform capable of serving as a contrast agent for imaging, a guidable drug carrier, and causing cellular ablation through photothermal energy conversion. In this review, we summarize the different properties of MPNPs and the most common synthesis approaches. We particularly discuss applications of MPNPs in cancer diagnosis and treatment based on different mechanisms using the magnetic and optical properties of the particles. Lastly, we look into current challenges they face for clinical applications and future perspectives using MPNPs for cancer detection and therapy.

Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries

Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries, while providing recommendations for advancing tumor diagnostics in resource-constrained environments.

Reactivity of 2-((3-Cyano-4-(4-Fluorophenyl)-6-(Naphthalen-2-yl)Pyridin-2-yl)Oxy)Acetohydrazide Toward Some Reagents for Preparing a Promising Anticancer Agents and Molecular Docking Study

This study aims to synthesize a novel series of nicotinonitriles incorporating pyrazole, oxadiazole, isoindoline, thiadiazole, and thiazolidinone moieties (compounds 4-11). The synthesis utilizes 2-((3-cyano-4-(4-fluorophenyl)-6-(naphthalen-2-yl)pyridin-2-yloxy)acetohydrazide (3) as a key starting material to enhance potential anticancer activity. The molecular structures of compounds 4-11 were elucidated using various spectroscopic techniques and elemental analysis. The synthesized compounds were screened for cytotoxic activity against human cancer cell lines, including MCF-7 (human Caucasian breast adenocarcinoma), MDA-MB-231 (breast ductal carcinoma), and PC-3 (prostate cancer), using an MTT assay with doxorubicin as a reference drug. Among the tested compounds, 4, 6b, and 7 exhibited the most promising cytotoxic activity, with IC50 values ranging from 22.5 to 91.3 µM. The safety profile of these compounds was further evaluated using noncancerous human skin fibroblast cells (BJ-1). Notably, 6b and 7 demonstrated high selectivity indices (SI > 3) against cancer cells, indicating preferential cytotoxicity, whereas compound 4 lacked selectivity. Docking studies, consistent with experimental data, further supported the potential anticancer properties of compounds 4, 6b, and 7. Given their significant inhibitory effects on cancer cell lines with minimal to no impact on normal cells, compounds 6b and 7 are strong candidates for further drug development as potential anticancer agents.

Biosynthesis of trimetallic nanoparticles and their biological applications: a recent review

Trimetallic nanoparticles (TMNPs) have emerged as a pivotal area of research due to their unique properties and diverse applications across medicine, agriculture, and environmental sciences. This review provides several novel contributions that distinguish it from existing literature on trimetallic nanoparticles (TMNPs). Firstly, it offers a focused exploration of TMNPs, specifically addressing their unique properties and applications, which have been less examined compared to other multimetallic nanoparticles. This targeted analysis fills a significant gap in current research. Secondly, the review emphasizes innovative biosynthesis methods utilizing microorganisms and plant extracts, positioning these green synthesis approaches as environmentally friendly alternatives to traditional chemical methods. This focus aligns with the increasing demand for sustainable practices in nanotechnology. Furthermore, the review integrates discussions on both medical and agricultural applications of TMNPs, highlighting their multifunctional potential across diverse fields. This comprehensive perspective enhances our understanding of how TMNPs can address various challenges. Additionally, the review explores the synergistic effects among the different metals in TMNPs, providing insights into how these interactions can be harnessed to optimize their properties for specific applications. Such discussions are often overlooked in existing studies. Moreover, this review identifies critical research gaps and challenges within the field, outlining future directions that encourage further investigation and innovation in TMNP development. By doing so, it proactively contributes to advancing the field. Finally, the review advocates for interdisciplinary collaboration among material scientists, biologists, and environmental scientists, emphasizing the importance of diverse expertise in enhancing the research and application of TMNPs.

Potential effects of liver dysfunction at the time of diagnosis in patients with acute myeloid leukemia

Whilst severe liver dysfunction is rarely encountered at the time of diagnosis for patients with acute myeloid leukemia (AML), mild elevations aminotransferase (<5 times the upper limit of normal) may be more frequently seen. Liver dysfunction at the time of diagnosis of AML is a parameter that requires investigation and can assist the clinicians in predicting prognosis. The aim of the present study was to investigate liver dysfunction at the time of diagnosis using the assoicated parameters in patients with AML. The present retrospective study included 90 patients diagnosed with AML who were hospitalised in the Hematology Clinic of Dışkapı Yıldırım Beyazıt Training and Research Hospital (Ankara, Turkey). The demographic characteristics of the patients were recorded together with hemogram results, anemia parameters, measurable residual disease positivity (MRD) and risk category, the presence of hepatosplenomegaly, infection, neutrophil recovery time (NRT), platelet recovery time (PRT) and liver dysfunction. The patients were analyzed in two groups following sorting into the liver dysfunction (n=45) and normal liver function test group (n=45). In the analysis of independent quantitative data (age, white blood cell count, hemoglobin, platelet, international normalized ratio, albumin, B12 vitamin, NRT, PRT) the Mann Whitney U-test was used. Independent qualitative data (sex, hepatomegaly, splenomegaly, MRD, risk category, infection) were analyzed using the χ2 test or the Fischer test. The effect level was investigated using univariate and multivariate logistic regression. A receiver operating characteristic curve was applied to determine the effect level and cut-off values. In the group with liver dysfunction, NRT, PRT, MRD positivity, risk category and the presence of infection were found to be statistically significantly higher. These findings suggest that during the first evaluation of patients diagnosed with AML, liver function tests are simple, rapid and necessary. The results obtained in the present study showed that liver dysfunction at diagnosis can be associated with the high-risk group, in addition to more frequent infection, poorer prognosis and mortality.

Leveraging nanomaterials for ultrasensitive biosensors in early cancer detection: a review

Cancer remains a major global health challenge with a high mortality rate, as evidenced by the rise in new cases every year. Conventional diagnostic methods like PET scans, MRIs, and biopsies, despite being widely used, suffer from significant drawbacks such as high radiation exposure, difficulty in distinguishing malignant from benign tumors, and invasiveness. Early detection, which is crucial for improving treatment outcomes and survival rates, is hindered by the asymptomatic nature of early-stage cancer and the limitations of current diagnostic tools. Cancer biomarkers, detectable in body fluids, offer valuable diagnostic information, and recent advances in nanotechnology have led to the development of highly sensitive nano-biosensors. This review explores recent advancements (2022-2024) in the field of ultrasensitive nano-biosensors, emphasizing the strategic integration of nanomaterials to enhance sensitivity and accuracy in cancer biomarker detection. It highlights how precise nanomaterial positioning in sensor components like electrodes and bioreceptors enables early cancer diagnosis at low biomarker concentrations. These innovations underscore the transformative potential of nanomaterials in revolutionizing early cancer diagnostics, improving patient care, and enhancing survival outcomes.

Impact of the COVID-19 Pandemic on Histopathological Cancer Diagnostics in Lower Silesia: A Comparative Analysis of Prostate, Breast, and Colorectal Cancer from 2018 to 2022

BACKGROUND/OBJECTIVE

The COVID-19 pandemic significantly disrupted healthcare systems worldwide including cancer diagnostics. This study aimed to assess the impact of the pandemic on histopathological cancer diagnostics in Lower Silesia, Poland, specifically focusing on prostate, breast, and colorectal cancer cases from 2018 to 2022. The objective was to evaluate diagnostic volumes and trends before, during, and after the pandemic and to understand the effect of healthcare disruptions on cancer detection.

METHODS

Histopathological and cytological data were collected from multiple laboratories across Lower Silesia. Samples were categorized into three periods: pre-pandemic (January 2018-February 2020), pandemic (March 2020-May 2022), and post-pandemic (June-December 2022). Statistical analyses included comparisons of diagnostic volumes and positive diagnoses across these periods.

RESULTS

A significant reduction in the number of histopathological examinations occurred during the pandemic, particularly during its early phase. This decline was accompanied by a higher frequency of positive cancer diagnoses, suggesting the prioritization of high-risk cases. Post-pandemic, diagnostic activity showed partial recovery, though it remained below the pre-pandemic levels, with notable differences across cancer types.

CONCLUSIONS

The COVID-19 pandemic significantly disrupted cancer diagnostics in Lower Silesia, delaying detection and highlighting healthcare system vulnerabilities. These findings underscore the importance of resilient healthcare systems that can ensure the continuity of essential diagnostic services and address inequalities in access to care during crises.

Transforming Cancer Care: The Impact of AI-driven Strategies

AI is a critical component in healthcare, especially in the application of precision medicine where patients' characteristics, including genetic makeup, determine the treatment options that should be implemented. AI sorts big data, predicting people's reactions to specific treatments, the right combinations of drugs, and possible side effects, therefore increasing the efficiency of the treatment process and decreasing negative outcomes. This article briefly presents the ethical issues and concerns that might arise due to the integration of AI in society, such as the privacy of data, the issues of bias in the algorithms, and the issues of interpretability of the AI systems. Nevertheless, there is no doubt that AI can bring qualitative changes in cancer care based on its potential to enhance patient prognosis and reduce health care costs, as well as become a defining feature of the standard of care.

Early detection of carcinoma: correlating quantifiable tumor biomarkers with High-Resolution Microscopy (HRME) findings

INTRODUCTION

Cancer ranks as the second most prevalent cause of death worldwide, responsible for approximately 9.6 million deaths annually. Approximately one out of every six deaths is caused by cancer. About 80% of cancer deals with epithelial tissues located on the outer lines of the body cavity.

AREAS COVERED

This review study selected and analyzed recent works in the field of High Resolution Microendoscopy (HRME) that have been used to diagnose cancer in various organs such as cervical, esophageal, head & neck, and gastrointestinal.

EXPERT OPINION

The HRME modality will play a vital role in improving the diagnostic accuracy of carcinoma. HRME has shown promising statistical outcomes for diagnosing carcinoma, enabling the clinician to gain additional information before performing conventional tissue biopsy. A multimodal probe consisting of a macroscopic investigation aided by HRME modality for microscopic investigation can significantly reduce the number of unnecessary biopsies leading to overall improvement in patient wellness. The new directions of the HRME research would be in the light source and detection configuration, increasing the number of optical fiber cores, which improves the resolution of the image, AI-assisted automatic quantification of the key HRME parameters, and clinical studies with newer near-infrared regime-based contrast agents.

Stomach and duodenal ulcer as a cause of death in patients with cancer: a cohort study

INTRODUCTION

Non-cancer deaths are now becoming a significant threat to the health of cancer patients. Death from stomach and duodenal ulcer is linked to cancer due to the side effects of treatment and its pathogenesis. However, guidelines for identifying cancer patients at the highest risk of death from stomach and duodenal ulcer remain unclear.

METHODS

Data of all patients diagnosed with cancer between 2000 and 2021 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Data regarding the causes of death and clinicopathological features such as sex, age, race, marital status, SEER stage, and treatment procedures were extracted. We calculated standardized mortality ratios (SMRs) using the SEER*Stat software V8.4.3.

RESULTS

Of the 6,891,191 cancer patients, 2,318 died of stomach and duodenal ulcer, a rate higher than that in the general population (SMR = 1.58, 95% CI [1.52-1.65]). Stomach and duodenal ulcer-related deaths decreased over time from 870 deaths between 2000 and 2004 to 294 deaths between 2015 and 2019. Among the 2,318 stomach and duodenal ulcer deaths, the highest numbers were observed in patients with prostate cancer (n = 389, 16.8%), and lung and bronchus cancer (n = 255, 11%). Patients with liver and intrahepatic bile duct cancers (SMR = 10.53, 95% CI [8.3-13.18]), and pancreatic cancer (SMR = 6.84, 95% CI [5.11-8.97]) had a significantly higher rate of death from stomach and duodenal ulcer than the general population.

CONCLUSION

Our study revealed a significantly higher risk of stomach and duodenal ulcer mortality among patients with cancer in the United States, underscoring the critical need for integrated care strategies that address both cancer and ulcer-related complications. To reduce ulcer-related mortality, we recommend the implementation of targeted prevention protocols, including routine gastrointestinal screenings for high-risk cancer patients, proactive management of ulcer risk factors, and collaboration between oncology, gastroenterology, and surgical teams.

Probing the Biomolecular Interactions of DNA/HSA with the New Sn(IV) Complex and Computational Perspectives: Design, Synthesis, Characterization, Anticancer Activity, and Molecular Modeling Approach

The ligands 2,2'-bipyridyl and indole-3-carboxylic acid were used to create a Sn(IV) complex, which was then synthesized and carefully characterized using elemental analysis and spectroscopic techniques (UV-vis, IR, 1H, 13C, and 119Sn NMR, and ESI-MS) and RXPD. Utilizing biophysical techniques such as UV-vis, fluorescence titrations, circular dichroism, FTIR (for HSA), and cleavage activity (for DNA), in vitro binding studies of Sn(IV) complex and DNA/HSA were satisfied with the strong electrostatic binding interaction of the Sn(IV) complex via the phosphate backbone of the DNA helix as well as in the subdomain IIA of HSA. The observed trend in the binding interactions and computational studies of the Sn(IV) complex was attributed to the nature of the ligands bound to the Sn(IV) center that influences their in vitro activities. The Sn(IV) complex showed sufficient effectiveness to be considered a viable candidate for the creation of anticancer medications.

Exosomes in cancer diagnosis based on the Latest Evidence: Where are We?

Exosomes are small extracellular vesicles (EVs) derived from various cellular sources and have emerged as favorable biomarkers for cancer diagnosis and prognosis. These vesicles contain a variety of molecular components, including nucleic acids, proteins, and lipids, which can provide valuable information for cancer detection, classification, and monitoring. However, the clinical application of exosomes faces significant challenges, primarily related to the standardization and scalability of their use. In order to overcome these challenges, sophisticated methods such as liquid biopsy and imaging are being combined to augment the diagnostic capabilities of exosomes. Additionally, a deeper understanding of the interaction between exosomes and immune system components within the tumor microenvironment (TME) is essential. This review discusses the biogenesis and composition of exosomes, addresses the current challenges in their clinical translation, and highlights recent technological advancements and integrative approaches that support the role of exosomes in cancer diagnosis and prognosis.

Update on Occupational Cancer for Better Cancer Prevention and Control

Occupational cancer remains an ongoing and emerging issue in cancer prevention and control and is more easily preventable in practice than other causes. As of 2024, changes in various aspects, such as methods for estimating the burden of cancer, evidence on novel carcinogens and classification systems, modernization of working conditions, job characteristics, occupational exposure, and societal changes have played a significant role. Furthermore, advances in technology, including wearable devices, exposome, and biomedical technology, offer more precise methods for identifying the associations between occupational carcinogens and cancer. Diagnosing occupational cancer and investigating clusters are crucial for understanding its etiology. Prevention at every level- from primary to quarterly prevention- and promotional activities are crucial for exposed workers, often outweighing the importance of treatment, which can be costly. This updated information, as reviewed in this article, and cooperative work with occupational medicine physicians, could contribute to improving clinical practices for better cancer prevention and control.

Breast cancer epidemiology, diagnostic barriers, and contemporary trends in breast nanotheranostics and mechanisms of targeting

INTRODUCTION

Breast cancer is one of the main causes of mortality in women globally. Early and accurate diagnosis represents a milestone in cancer management. Several breast cancer diagnostic agents are available. Many chemotherapeutic agents in conventional dosage forms are approved; nevertheless, they lack cancer cell specificity, resulting in improper treatment and undesirable side effects. Recently, nanotheranostics has emerged as a new paradigm to achieve safe and effective cancer diagnosis and management.

AREA COVERED

This review provides insight into breast cancer epidemiology, barriers hindering the early diagnosis, and effective delivery of chemotherapeutics. Also, conventional diagnostic agents and recent nanotheranostic platforms have been used in breast cancer. In addition, mechanisms of cancer cell targeting and nano-carrier surface functionalization as an effective approach for chemotherapeutic targeting were reviewed along with future perspectives.

EXPERT OPINION

We proposed that modified nano-carriers may provide an efficacious approach for breast cancer drug targeting. These nanotheranostics need more clinical evaluations to confirm their efficacy in cancer management. In addition, we recommend the use of artificial intelligence (AI) as a promising approach for early and efficient assessment of breast lesions. AI allows better interpretation and analysis of nanotheranostic data, which minimizes misdiagnosis and avoids the belated intervention of health care providers.

Diagnostics and Therapy for Malignant Tumors

Malignant tumors remain one of the most significant global health challenges and contribute to high mortality rates across various cancer types. The complex nature of these tumors requires multifaceted diagnostic and therapeutic approaches. This review explores current advancements in diagnostic methods, including molecular imaging, biomarkers, and liquid biopsies. It also delves into the evolution of therapeutic strategies, including surgery, chemotherapy, radiation therapy, and novel targeted therapies such as immunotherapy and gene therapy. Although significant progress has been made in the understanding of cancer biology, the future of oncology lies in the integration of precision medicine, improved diagnostic tools, and personalized therapeutic approaches that address tumor heterogeneity. This review aims to provide a comprehensive overview of the current state of cancer diagnostics and treatments while highlighting emerging trends and challenges that lie ahead.

Comprehensive analysis of electrochemical biosensors for early ovarian cancer detection

Ovarian cancer is one of the leading causes of mortality among women worldwide. However, early detection can significantly reduce mortality rates and mitigate subsequent complications related to both economic burden and mental well-being. Despite the development in the field of medical diagnosis, the death rates due to ovarian cancer have sharply increased. Among the recent technologies suggested as suitable diagnostic techniques for the early detection of ovarian cancer, biosensor technology has emerged as a cutting-edge technology, with electrochemical biosensors providing one of the most efficient types of biosensors. Therefore, this review discusses the application of electrochemical biosensors as a viable alternative to conventional diagnostic techniques for the timely identification of ovarian cancer, its advantages over other types of biosensors and conventional diagnostic techniques, and the types of electrochemical biosensors.

Precise diagnosis of tumor cells and hemocytes using ultrasensitive, stable, selective cuprous oxide composite SERS bioprobes assisted with high-efficiency separation microfluidic chips

Efficient enrichment and accurate diagnosis of cancer cells from biological samples can guide effective treatment strategies. However, the accessibility and accuracy of rapid identification of tumor cells have been hampered due to the overlap of white blood cells (WBCs) and cancer cells in size. Therefore, a diagnosis system for the identification of tumor cells using reliable surface-enhanced Raman spectroscopy (SERS) bioprobes assisted with high-efficiency microfluidic chips for rapid enrichment of cancer cells was developed. According to this, a homogeneous flower-like Cu2O@Ag composite with high SERS performance was constructed. It showed a favorable spectral stability of 5.81% and can detect trace alizarin red (10-9 mol L-1). Finite-difference time-domain (FDTD) simulation of Cu2O, Ag and Cu2O@Ag, decreased the fluorescence lifetime of methylene blue after adsorption on Cu2O@Ag, and surface defects of Cu2O observed using a spherical aberration-corrected transmission electron microscope (AC-TEM) demonstrated that the combined effects of electromagnetic enhancement and promoted charge transfer endowed the Cu2O@Ag with good SERS activity. In addition, the modulation of the absorption properties of flower-like Cu2O@Ag composites significantly improved electromagnetic enhancement and charge transfer effects at 532 nm, providing a reliable basis for the label-free SERS detection. After the cancer cells in blood were separated by a spiral inertial microfluidic chip (purity >80%), machine learning-assisted linear discriminant analysis (LDA) successfully distinguished three types of cancer cells and WBCs with high accuracy (>90%). In conclusion, this study provides a profound reference for the rational design of SERS probes and the efficient diagnosis of malignant tumors.

Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment

Currently, eight million people in the United States suffer from cancer and it is a major global health concern. Early detection and interventions are urgently needed for all cancers, including colorectal cancer. Colorectal cancer is the third most common type of cancer worldwide. Based on the diagnostic efforts to general awareness and lifestyle choices, it is understandable why colorectal cancer is so prevalent today. There is a notable lack of awareness concerning the impact of this cancer and its connection to lifestyle elements, as well as people sometimes mistaking symptoms for a different gastrointestinal condition. Artificial intelligence (AI) may assist in the early detection of all cancers, including colorectal cancer. The usage of AI has exponentially grown in healthcare through extensive research, and since clinical implementation, it has succeeded in improving patient lifestyles, modernizing diagnostic processes, and innovating current treatment strategies. Numerous challenges arise for patients with colorectal cancer and oncologists alike during treatment. For initial screening phases, conventional methods often result in misdiagnosis. Moreover, after detection, determining the course of which colorectal cancer can sometimes contribute to treatment delays. This article touches on recent advancements in AI and its clinical application while shedding light on why this disease is so common today.

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800-900 cm-1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881-0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories.

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs

BACKGROUND

Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.

METHODS

In this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application.

RESULTS

We extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme.

CONCLUSIONS

The proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo , respectively.

CFTR Exon 10 deleterious mutations in patients with congenital bilateral absence of vas deferens in a cohort of Pakistani patients

Congenital bilateral absence of vas deferens (CBAVD) is a urological syndrome of Wolffian ducts and is responsible for male infertility and obstructive azoospermia. This study is designed to explore the integrity of exon 10 of CFTR and its role in male infertility in a cohort of CBVAD patients in Pakistan. Genomic DNA was extracted from 17 male patients with CBAVD having clinical symptoms, and 10 healthy controls via phenol-chloroform method. Exon 10 of the CFTR gene was amplified, using PCR with specific primers and DNA screening was done by Sanger sequencing. Sequencing results were analyzed using freeware Serial Cloner, SnapGene, BioEdit and FinchTV. Furthermore, bioinformatics tools were used to analyze the mutations and their impact on the protein function and stability. We have identified 4 mutations on exon 10 of CFTR in 6 out of 17 patients. Two of the mutations were missense variants V456A, K464E, and the other two were silent mutations G437G, S431S. The identified variant V456A was present in 4 of the studied patients. Whereas, the presence of K464E in our patients further weighs on the crucial importance for its strategic location to influence the gene function at post-transcriptional and protein level. Furthermore, Polyphen-2 and SIFT analyze the mutations as harmful and deleterious. The recurrence of V456A and tactically conserved locality of K464E are evidence of their potential role in CBAVD patients and in male infertility. The data can contribute in developing genetic testing and treatment of CBAVD.

Clinicopathological correlations and prognostic insights in osteosarcoma: a retrospective analysis

Osteosarcoma (OS) is the most prevalent primary bone malignancy, predominantly affecting adolescents and young adults, and presents significant clinical challenges due to its aggressive nature and high potential for metastasis. This retrospective study analyzed 34 cases of primary OS, aged 10 to 65 years, to identify clinicopathological correlations that could inform future research and treatment strategies. The findings aim to guide larger cohort studies, essential for validating these correlations and developing tailored approaches that enhance patient outcomes. The analysis focused on demographic factors, sex, tumor grade, stage, size, and histological subtype, utilizing criteria established by the American Joint Committee on Cancer (AJCC) and the World Health Organization (WHO). The results revealed a predominance of the disease in males under 25 years of age, with the femur being the most common site of occurrence. Conventional osteoblastic OS emerged as the most frequent subtype, accounting for 50% of the cases, predominantly presenting as high-grade (G3) tumors. Over 70% of the tumors were T1 in extension (≤8 cm) and classified as stage IIA, indicating a locally advanced disease state. Correlations were observed between histological type, grade, and stage, underscoring the importance of detailed histopathological (HP) assessments in determining prognosis and guiding treatment. The findings highlight correlations between histological subtype, grade, and stage, reaffirming the critical role of detailed HP assessments in prognosis and treatment planning. While the limited sample size necessitates cautious interpretation, this study provides valuable regional and age-specific insights that could inform clinical decision-making. Future research should prioritize multi-center studies and delve into the genetic and molecular underpinnings of OS subtypes to enhance understanding and develop targeted therapies.

lncRNAs as prognostic markers and therapeutic targets in cuproptosis-mediated cancer

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various cellular processes, including cancer progression and stress response. Recent studies have demonstrated that copper accumulation induces a unique form of cell death known as cuproptosis, with lncRNAs playing a key role in regulating cuproptosis-associated pathways. These lncRNAs may trigger cell-specific responses to copper stress, presenting new opportunities as prognostic markers and therapeutic targets. This paper delves into the role of lncRNAs in cuproptosis-mediated cancer, underscoring their potential as biomarkers and targets for innovative therapeutic strategies. A thorough review of scientific literature was conducted, utilizing databases such as PubMed, Google Scholar, and ScienceDirect, with search terms like 'lncRNAs,' 'cuproptosis,' and 'cancer.' Studies were selected based on their relevance to lncRNA regulation of cuproptosis pathways and their implications for cancer prognosis and treatment. The review highlights the significant contribution of lncRNAs in regulating cuproptosis-related genes and pathways, impacting copper metabolism, mitochondrial stress responses, and apoptotic signaling. Specific lncRNAs are potential prognostic markers in breast, lung, liver, ovarian, pancreatic, and gastric cancers. The objective of this article is to explore the role of lncRNAs as potential prognostic markers and therapeutic targets in cancers mediated by cuproptosis.

The Significance of C-Reactive Protein Value and Tumor Grading for Malignant Tumors: A Systematic Review

BACKGROUND

Malignant tumors represent a significant pathology with a profound global impact on the medical system. The fight against cancer represents a significant challenge, with multidisciplinary teams identifying numerous areas requiring improvement to enhance the prognosis. Facilitating the patient's journey from diagnosis to treatment represents one such area of concern. One area of research interest is the use of various biomarkers to accurately predict the outcome of these patients. A substantial body of research has been conducted over the years examining the relationship between C-reactive protein (CRP) and malignant tumors. The existing literature suggests that combining imaging diagnostic modalities with biomarkers, such as CRP, may enhance diagnostic accuracy.

METHODS

A systematic review was conducted on the PubMed and Web of Science platforms with the objective of documenting the interrelationship between CRP value and tumor grading for malignant tumors. After the application of the exclusion and inclusion criteria, 17 studies were identified, published between 2002 and 2024, comprising a total of 9727 patients.

RESULTS

These studies indicate this interrelationship for soft tissue sarcomas and for renal, colorectal, esophageal, pancreatic, brain, bronchopulmonary, ovarian, and mesenchymal tumors.

CONCLUSIONS

Elevated CRP levels are correlated with higher grading, thereby underscoring the potential utility of this biomarker in clinical prognostication.

Synthesis, anticancer activity, docking and computational studies of new pyridyl-glycosyl hybrids and acyclic analogs

New pyridine-O-glycosides and their acyclic nucleoside analogues were prepared by heterocyclization and glycosylation. The anticancer activity against HCT-116, HepG2 and MCF-7 human cancer cells and BJ-1 cell revealed that the galacto- and xylopyranosyl glycosides possessing 4-bromophenyl have superior cytotoxic activities against HepG2 cell while glycosides 7-9 resulted in superior cytotoxic activities regarding MCF-7 breast cell. In case of HCT-116 colorectal carcinoma cells, two products and the derived glycosides and acyclic analogues showed potent activities. The most potent compounds were investigated for their possible binding affinities to the active site of CDK2 enzyme via in silico molecular docking simulation in addition to computational studies. The results support the antiproliferative effect and elucidate the interactions of 3a and 8 with catalytic sites.

Nucleic Acid Based Testing (NABing): A Game Changer Technology for Public Health

Timely and accurate detection of the causal agent of a disease is crucial to restrict suffering and save lives. Mere symptoms are often not enough to detect the root cause of the disease. Better diagnostics applied for screening at a population level and sensitive detection assays remain the crucial component of disease surveillance which may include clinical, plant, and environmental samples, including wastewater. The recent advances in genome sequencing, nucleic acid amplification, and detection methods have revolutionized nucleic acid-based testing (NABing) and screening assays. A typical NABing assay consists of three modules: isolation of the nucleic acid from the collected sample, identification of the target sequence, and final reading the target with the help of a signal, which may be in the form of color, fluorescence, etc. Here, we review current NABing assays covering the different aspects of all three modules. We also describe the frequently used target amplification or signal amplification procedures along with the variety of applications of this fast-evolving technology and challenges in implementation of NABing in the context of disease management especially in low-resource settings.

Trend analysis of hematological tumors in adolescents and young adults from 1990 to 2019 and predictive trends from 2020 to 2044: A Global Burden of Disease study

INTRODUCTION

Cancer constitutes the primary disease spectrum contributing to the Global Burden of Disease (GBD). Adolescents and young adults (AYA) aged 15-39 have received relatively less attention regarding tumor prevention, diagnosis, and treatment compared to older adults and children. This study aimed to analyze the changes in the disease burden of hematological malignancies among the global AYA over the past three decades based on the GBD database.

METHODS

The changes in the disease burden of hematological malignancies were analyzed among the AYA over the past three decades based on the information from the GBD database. The future trends were predicted using the Nordpred package in R.

RESULTS

Our results showed that leukemia ranked first as the leading tumor burden among AYA in 2019, but the incidence rate and mortality rate of leukemia decreased year by year, with a projected age-standardized incidence rate (ASIR) of 1.65/100,000 for females and 2.40/100,000 for males by the year 2044. In addition, the incidence of non-Hodgkin's lymphoma has been gradually increasing in recent years, with an ASIR of 1.73/100,000 from 2020 to 2024. The results may serve as a basis for developing strategies to reduce the burden of hematological malignancies in the AYA population in different regions.