Early Cancer Diagnosis Research Articles

Diagnostic Accuracy of PSMA PET-Guided Prostate Biopsy in Prostate Cancer—A Systematic Review and Meta-analysis

Purpose Early diagnosis and treatment of prostate cancer (PC) are crucial for effective management and improved patient outcomes. Newer imaging modalities like prostate-specific membrane antigen PET have shown superior diagnostic performance in detecting PC and clinically significant PC (csPC). This systematic review and meta-analysis aims to synthesize evidence on the diagnostic performance of PSMA PET-guided prostate biopsy in detecting PC and csPC. Patients and Methods The study followed the PRISMA-DTA guidelines. Using a predefined search strategy, 3 databases (PubMed, Embase, and Web of Science) were systematically searched using appropriate keywords. A meta-analysis was conducted using diagnostic accuracy parameters of the included studies. Risk of bias assessment was done using the QUADAS-2 tool. Results Out of 378 articles, 20 were assessed for full-text screening and 10 articles with 874 patients were finally included. Eight studies reported a pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of 0.90 (95%confidence interval [CI], 0.82–0.95), 0.93 (95% CI, 0.57–0.99), 12.3 (95% CI, 1.5–98.9), 0.10 (95% CI, 0.05–0.20), and 117 (95% CI, 12–1178), respectively, for detecting PC using PSMA PET-guided prostate biopsy with an area under the summary receiver operating characteristics curve of 0.94 (95% CI, 0.92–0.96). Similarly, 6 studies reported a pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of 0.89 (95% CI, 0.82–0.94), 0.65 (95% CI, 0.49–0.79), 2.6 (95% CI, 1.6–4.1), 0.17 (95% CI, 0.09–0.31), and 15 (95% CI, 6–41), respectively, for detecting csPC using PSMA PET-guided prostate biopsy with area under summary receiver operating characteristics curve of 0.86 (95% CI, 0.82–0.88). Conclusions PSMA PET-guided prostate biopsy has a high diagnostic accuracy in detecting PC and csPC in patients with clinical suspicion of PC, and provides a 1-stop solution for early diagnosis and staging of PC.

Early Cervical Cancer Diagnosis with SWIN-Transformer and Convolutional Neural Networks.

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train-test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models' effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.

BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models.

Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from the World Health Organization show an alarming annual incidence of nearly 2.3 million new cases, drawing the attention of patients, healthcare professionals, and governments alike. Through the examination of histopathological pictures, this study aims to revolutionize the early and precise identification of breast cancer by utilizing the capabilities of a deep convolutional neural network (CNN)-based model. The model's performance is improved by including numerous classifiers, including support vector machine (SVM), decision tree, and K-nearest neighbors (KNN), using transfer learning techniques. The studies include evaluating two separate feature vectors, one with and one without principal component analysis (PCA). Extensive comparisons are made to measure the model's performance against current deep learning models, including critical metrics such as false positive rate, true positive rate, accuracy, precision, and recall. The data show that the SVM algorithm with PCA features achieves excellent speed and accuracy, with an amazing accuracy of 99.5%. Furthermore, although being somewhat slower than SVM, the decision tree model has the greatest accuracy of 99.4% without PCA. This study suggests a viable strategy for improving early breast cancer diagnosis, opening the path for more effective healthcare treatments and better patient outcomes.

AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net

Breast cancer is a leading cause of death among women worldwide, making early detection and diagnosis critical for effective treatment and improved patient outcomes. Ultrasound imaging is a common diagnostic tool for breast cancer, but interpreting ultrasound images can be challenging due to the complexity of breast tissue and the variability of image quality. This study proposed an Attention Gated Multi ResU-Net model for medical image segmentation tasks, that has shown promising results for breast cancer ultrasound image segmentation. The model’s multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications. The model’s quantitative test showed an adequate degree of agreement with expert manual annotations, demonstrating its potential for improving early identification and diagnosis of breast cancer. The model’s multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications, achieving a Dice coefficient of 0.93, sensitivity of 93%, and specificity of 99%. These results underscore the model’s high precision and reliability in medical image analysis.

Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset

Aim: The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis. Background: Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives. Objective: This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances. Method: In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively. Result: Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis. Conclusion: This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

Proteomic Characterization of Urinary Exosomes with Pancreatic Cancer by Phosphatidylserine Imprinted Polymer Enrichment and Mass Spectrometry Analysis.

Exosomes, as carriers of cell-to-cell communication, can serve as promising biomarkers for probing the early diagnosis of cancer. Pancreatic cancer is a common malignant tumor of the pancreas with an insidious onset and difficult early diagnosis. The aim of this study was to capture exosomes in urine samples by phosphatidylserine-molecularly imprinted polymers (PS-MIPs). Transmission electron microscopy and nanoparticle tracking analysis as well as Western blot showed that our molecularly imprinted material can effectively capture urinary exosomes. Three parallel tests verified the reproducibility of the mass spectrometry assay and the stability of the material capture efficiency. Mass Spectrometry with nontargeted proteomics was combined to show differentially expressed proteins in exosomes between 5 pancreatic cancer patients and 5 healthy controls. The most significant changes in the proteomic profile in pancreatic cancer patients compared to healthy controls were the overexpression of SLC9A3R1, SPAG9, and ferritin light chain (FTL) These proteins may have an important role in diagnosis and prognostic assessment, supporting further scientific and clinical studies on pancreatic cancer.

Association of serum and salivary dipeptidyl peptidase-4 (DPP-4) with oral cancerous and precancerous lesions; an observational diagnostic accuracy study

BackgroundEnhancing the prognosis and treatment outcomes of oral cancer relies heavily on its early detection. This study aims to establish a connection between serum and salivary dipeptidyl peptidase-4 (DPP-4) levels and oral squamous cell carcinoma (OSCC), comparing them with oral potentially malignant lesions (OPMLs) and control subjects and validating salivary DPP-4 as a diagnostic marker for the early detection of oral cancer.MethodologyForty-five systemically healthy individuals were categorized into three groups: Group I consisted of 15 patients diagnosed with OSCC, Group II comprised 15 patients with OPMLs (including leukoplakia and oral lichen planus), and Group III included 15 participants without any oral mucosal lesions. Serum and whole unstimulated salivary samples were collected from all participants to assess DPP-4 levels using an enzyme-linked immunosorbent assay (ELISA) kit. Receiver operating characteristic (ROC) analysis was conducted to determine the area under the curve (AUC), sensitivity, specificity, and diagnostic accuracy of DPP-4.ResultsBoth serum and salivary DPP-4 levels were highest in the healthy group, followed by OPMLs, and lowest in the OSCC group, with statistically significant differences observed. ROC analysis demonstrated excellent diagnostic accuracy of salivary DPP-4 in distinguishing OSCC from healthy individuals, OPMLs from healthy individuals, and OSCC from OPMLs, with accuracies of 100%, 100%, and 96.67%, respectively. Salivary DPP-4 levels also exhibited a statistically significant correlation with OSCC grades.ConclusionsDPP-4 appears to play a protective, anti-oncogenic role in maintaining oral tissue health. The remarkable diagnostic accuracy of both serum and salivary DPP-4 in discriminating between OSCC, OPMLs, and healthy controls suggests its potential utility as a well-established marker for early oral cancer diagnosis. Salivary DPP-4, being non-invasive, could serve as a convenient chair-side diagnostic tool for the early detection of OSCC.Clinical trial registrationThe study was retrospectively registered on clinicaltrial.gov with NCT06087042, date of registration (first posted date): 17/10/2023

Development of a telemedicine network for early oral cancer diagnosis: the Argentine Patagonia experience—a perspective through a pilot study

Development of a telemedicine network for early oral cancer diagnosis: the Argentine Patagonia experience—a perspective through a pilot study

ESR Essentials: staging and restaging with FDG-PET/CT in oncology-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging.

Positron emission tomography (PET) stands as the paramount clinical molecular imaging modality, especially in oncology. Unlike conventional anatomical-morphological imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI), PET provides detailed visualizations of internal activity at the molecular and cellular levels. 18-fluorine-fluorodeoxyglucose ([18F]FDG)-PET combined with contrast-enhanced CT (ceCT) significantly improves the detection of various cancers. Appropriate patient selection is crucial, and physicians should carefully assess the appropriateness of [18F]FDG-PET/CT based on specific clinical criteria and evidence. Due to its high diagnostic accuracy, [18F]FDG-PET/CT is indispensable for evaluating the extent of disease, staging, and restaging known malignancies, and assessing the response to therapy. PET/CT imaging offers significant advantages in patient management, particularly by identifying occult metastases that might otherwise go undetected. This can help prevent unnecessary surgeries, allowing many patients to be redirected to systemic chemotherapy instead. However, it is important to note that the gold standard for surgical planning remains CT and/or MRI, depending on the body region. These imaging modalities, with or without associated angiography, provide superior contrast and spatial resolution, essential for detailed surgical preparation and planning. [18F]FDG-PET/CT has a central role in the precise and early diagnosis of cancer, contributing significantly to personalized treatment plans. However, it has limitations, including non-tumor-specific uptake and the potential to inaccurately capture the metabolic activity of certain tumor types due to low uptake in some well-differentiated tumor cell lines. Therefore, it should be utilized in clinical scenarios where it offers crucial diagnostic insights not readily available with other imaging modalities. KEY POINTS: Use [18F]FDG-PET/CT selectively based on clinical appropriateness criteria and existing evidence to optimize resource utilization and minimize patient exposure. Employ [18F]FDG-PET/CT in treatment planning and monitoring, particularly for assessing chemotherapy or radiotherapy response in FDG-avid lymphoma and solid tumors. When available, [18F]FDG-PET/CT can be integrated with other diagnostic tools, such as MRI, to enhance overall diagnostic accuracy.

DETECTION OF ORAL SQUAMOUS CELL CARCINOMA USING PRE-TRAINED DEEP LEARNING MODELS.

Oral squamous cell carcinoma (OSCC), the 13th most common type of cancer, claimed 364,339 lives in 2020. Researchers have established a strong correlation between early detection and better prognosis for this type of cancer. Tissue biopsy, the most common diagnostic method used by doctors, is both expensive and time-consuming. The recent growth in using transfer learning methodologies to aid in medical diagnosis, along with the improved 5-year survival rate from early diagnosis serve as motivation for this study. The aim of the study was to evaluate an innovative approach using transfer learning of pre-trained classification models and convolutional neural networks (CNN) for the binary classification of OSCC from histopathological images. The dataset used for the experiments consisted of 5192 histopathological images in total. The following pre-trained deep learning models were used for feature extraction: ResNet-50, VGG16, and InceptionV3 along with a tuned CNN for classification. The proposed methodologies were evaluated against the current state of the art. A high sensitivity and its importance in the medical field were highlighted. All three models were used in experiments with different hyperparameters and tested on a set of 126 histopathological images. The highest-performance developed model achieved an accuracy of 0.90, a sensitivity of 0.97, and an AUC of 0.94. The visualization of the results was done using ROC curves and confusion matrices. The study further interprets the results obtained and concludes with suggestions for future research. The study successfully demonstrated the potential of using transfer learning-based methodologies in the medical field. The interpretation of the results suggests their practical viability and offers directions for future research aimed at improving diagnostic precision and serving as a reliable tool to physicians in the early diagnosis of cancer.

MXene-montmorillonite nanocomposites-based scaffold sensors for early pancreatic cancer diagnosis

Pancreatic cancer is increasingly prevalent and characterized by a high mortality rate. Due to the limitations of current diagnostic methods, early-stage detection remains elusive, contributing to persistently low survival rates among affected individuals. Nanomaterials have garnered significant attention in cancer research for their potential diagnostic applications. Among these, MXenes – a novel family of two-dimensional nanomaterials composed of transition metal carbides, nitrides, and carbonitrides – are of particular interest due to their unique properties. These include high electrical conductivity, hydrophilicity, thermal stability, large interlayer spacing, tunable structure, and high surface area. These characteristics make MXenes highly effective for detecting trace amounts of various analytes. In addition, their tunable structure enables precise manipulation of their properties, allowing for optimized sensing responses. Montmorillonite nanoclay (MMT), a member of the smectite group of natural clay minerals, is known for its ability to promote bone development and influence cell behavior. When combined with MXenes, MMT forms promising nanocomposites for early pancreatic cancer detection through sensing applications. The Ti3C2 MXene-MMT nanocomposites exhibit potential as scaffold sensors capable of distinguishing cancerous from non-cancerous samples by observing the distinctive patterns in resistance changes. In addition, MXenes possess excellent selectivity, allowing for the reliable identification of targeted analytes from a complex mixture of chemical and biological analytes. Due to the advanced sensing capabilities of MXene-MMT composite scaffold sensors, they hold great promise for early cancer diagnosis and tissue regeneration, providing a novel therapeutic approach to improving patient outcomes.

On-Site Visualization Assay for Tumor-Associated miRNAs: Using Ru@TiO2 as a Peroxidase-like Nanozyme.

Accurate diagnosis of highly aggressive and deadly tumors is essential for effective treatment and improved patient outcomes, and microRNAs (miRNAs) have emerged as crucial biomarkers for their roles in tumor initiation, progression, and metastasis. Herein, we present an on-site visualization colorimetric assay for tumor-associated miRNAs using ruthenium nanoparticle decorated titanium dioxide nanoribbon (Ru@TiO2) as a peroxidase-like (POD) nanozyme. Remarkably, the Ru@TiO2 nanozyme can catalyze the oxidation of chromogenic substrates through its POD-like activity, which is effectively inhibited by pyrophosphate generated during the rolling circle amplification process, thereby enabling miRNA detection through a visible colorimetric readout. This approach provides a highly sensitive and specificity assay for miRNAs in diluted human serum with a detection limit of 100 pM. It shows great potential for clinical diagnostics and biological research, offering a promising tool for early cancer diagnosis and molecular diagnostics.

Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasmacell-free DNA.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Identification of plasma exosomal lncRNA as a biomarker for early diagnosis of gastric cancer.

There were about 1,090,000 gastric cancer (GC) cases in 2020 in China. The incidence and mortality rates ranked the fifth and third among all kinds of cancers in China. Early diagnosis plays an important role in the treatment and prognosis of gastric cancer. In recent years, noninvasive diagnosis, especially plasma exosome lncRNAs, has become a promissing biomarkers with high specificity and sensitivity for early diagnosis of cancers. In this study, plasma exosomes of patients with early gastric cancer were extracted efficiently by affinity membrane separation technology, including affinity adsorption, elution, affinity membrane regeneration and other steps. After identified by electron microscopy observation, particle size analysis and Western blot verification, the lncRNAs in the exosomes were extracted and were analysized by high-throughput RNA sequencing (RNA-Seq). The differentially expressed lncRNAs were verified by RT-qPCR in 93 patients with early gastric cancer and 49 normal controls. Electron microscopy, particle size analysis and Western blot showed that exosomes were successfully isolated from plasma. RNA-Seq results show that 76 lncRNAs were upregulated and 260 lncRNAs were downregulated in plasma exosomes of early gastric cancer patients compared with normal controls. RT-qPCR analysis indicated that a total of 6 lncRNAs were significantly and differentially expressed in gastric cancer patients compared to normal controls, with 2 (lncmstrg. 1319590, Lncmstrg. 2312697) highly expressed and 4 lowly expressed (lncmstr-g.1004024.1, lncmstrg. 2441832.8, lncmstrg. 315376.1, lncmstrg.907985.2,) (p < 0.05). The survival curve analysis indicated that lncmstrg.2441832.8 and lncmstrg.2312697 had higher sensitivity and specificity for the diagnosis of gastric cancer, respectively and AUC curve areas were 0.6211 and 0.631, p < 0.05, respectively, which were greater than the traditional clinical detection indexes CEA (0.61) and AFP (0.57). When combined lncmstrg.2441832.8 and lncmstrg.2312697 in gastric cancer diagnosis, AUC curve area reached 0.73, which was greater than CA199 (0.71). Lncmstrg.2441832.8 and lncmstrg.2312697 may be a potential and promissing biomarkers for early diagnosis of gastric cancer.

Hybrid Deep Learning Framework for Melanoma Diagnosis Using Dermoscopic Medical Images

Background: Melanoma, or skin cancer, is a dangerous form of cancer that is the major cause of the demise of thousands of people around the world. Methods: In recent years, deep learning has become more popular for analyzing and detecting these medical issues. In this paper, a hybrid deep learning approach has been proposed based on U-Net for image segmentation, Inception-ResNet-v2 for feature extraction, and the Vision Transformer model with a self-attention mechanism for refining the features for early and accurate diagnosis and classification of skin cancer. Furthermore, in the proposed approach, hyperparameter tuning helps to obtain more accurate and optimized results for image classification. Results: Dermoscopic shots gathered by the worldwide skin imaging collaboration (ISIC2020) challenge dataset are used in the proposed research work and achieved 98.65% accuracy, 99.20% sensitivity, and 98.03% specificity, which outperforms the other existing approaches for skin cancer classification. Furthermore, the HAM10000 dataset is used for ablation studies to compare and validate the performance of the proposed approach. Conclusions: The achieved outcome suggests that the proposed approach would be able to serve as a valuable tool for assisting dermatologists in the early detection of melanoma.

Skin cancer in Europe today and challenges for tomorrow.

One in every three cancers diagnosed is a skin cancer. Europe has the global lead in the number of UV-attributable cancer cases with the highest number of melanoma cases worldwide and the second highest number of keratinocyte cancers (KC). Further increases are expected in Europe for the coming decades. Projected increases are highest for KC with increases in incidence around 40% and increases in mortality around 50%, with KC mortality in males approximating melanoma mortality in females. The two main drivers for this skin cancer epidemic are ageing of the population but especially UV exposure. In conclusion, skin cancer represents a major challenge in the cancer field in Europe today and will continue to do so in the coming decades. This calls for a European skin cancer action plan intended to reduce avoidable UV exposure and to prepare the healthcare system to safeguard early diagnosis and treatment of skin cancer.

Co-infection of human papillomavirus and herpes simplex virus-2 with cervical dysplasia among women in Kaduna State, Nigeria

Background: The epidemiology of human papillomavirus (HPV) and co-infections with herpes simplex virus type 2 (HSV-2) remains poorly characterized in Africa. High risk HPV (hrHPV) infection is the primary cause of 99.7% all cervical cancer especially in the presence of genital ulcer disease (GUD) which is usually caused by HSV-2. Herpes simplex virus-2 might interact directly with hrHPV to increase the risk of cervical cancer. Co-infection of HPV and HSV-2 in asymptomatic women could provide a clue to early diagnosis of cervical cancer and this could aid in the development of new strategies for effective management and improvement of the quality of life of women who are infected. If the synergy between HPV and HSV-2 can be prevented, there may be significant reduction of the incidence of cervical cancer. Methodology: This was a descriptive cross-sectional study of 515 randomly selected apparently healthy women of reproductive age whose cervical samples were screened for HPV and HSV-2 in Kaduna State, Nigeria. The cervical samples were collected by liquid-based cytology (LBC) for detection of cervical epithelial cell abnormalities (CEA) and molecular detection of HPV and HSV-2 using conventional polymerase chain reaction (PCR) assay. Extracted viral DNAs from the samples were amplified by convectional PCR using specific hrHPV (HPV 16,18, 31 and 45) primer sets and a broad spectrum MY09/11 and GP5+/6+ primers for a wider range of HPV genotypes while HSV-2 DNA was detected by florescence-based PCR assay with HSV-2 gG primers and probes. Results: PCR assay revealed that 14.7% (n=76) of the 515 selected women harbored HPV, HSV-2 or both. The prevalence of HPV, hrHPV, HSV-2 and HPV/HSV-2 co-infection among in the study participants are 11.8% (n=61), 9.3% (n=48), 5.6% 4% (n=29) and 2.3% (n=12) respectively. The frequency of HPV detection in HSV-2 infected participants (41.4%, 12/29) was significantly higher (OR=6.295, p&lt;0.0001) than in HSV-2 negative participants (10.1%, 49/437), but only co-infections of HPV-31 (p=0.004) and HPV-18 (p=0.040) with HSV-2 were significantly associated. Cervical cytology reports on the smears revealed prevalence of cervical epithelial abnormalities (CEA) of 16.7% (n=86), with cervical dysplasia prevalence of 6.4% (n=33), consisting of high grade squamous intraepithelial lesion (HGSIL) of 1.6% (n=8), low grade squamous intraepithelial lesion (LGSIL) of 4.1% (n=21) and atypical squamous cells of uncertain significance (ASCUS) of 0.8% (n=4). The frequency of HPV detection in women with cervical dysplasia (93.9%, 31/33) was significantly higher (OR=14.22, p&lt;0.0001) than in women without cervical dysplasia (6.2%, 30/482), and all HPV genotypes were significantly associated (p&lt;0.05) with cervical dysplasia except HPV 16 (p=0.051). Also, the frequency of HPV/HSV-2 co-infection among women with cervical dysplasia (15.2%, 5/33) was significantly higher than among women without cervical dysplasia (1.5%, 7/482) (OR=12.12, p&lt;0.0001). Comparing women with cervical inflammatory and atrophic changes (n=53), women with cervical dysplasia (HGSIL, LGSIL and ASC-US) had significantly higher frequency of HPV infection (χ2=42.829, p=0.000), HSV-2 infection (χ2=25.140, p=0.001) and HPV/HSV-2 co-infections (χ2=66.602, p=0.000). Conclusion: Co-infection rate of hrHPV and HSV-2 among women in Kaduna State is 2.3%. Demographic factors significantly influencing the rate of co-infections included age and marital status. In spite of the screening method using the traditional Pap smear, GUD and cervical cancer continues to be a major public health problem, thus more sensitive and specific methods such as liquid based cytology technique and polymerase chain reaction for early detection of cervical dysplasia and hrHPV or HSV-2 in asymptomatic women should be adopted for routine use

The importance of blood parameters in the detection of intestinal metaplasia and early diagnosis of gastric cancer

Background: We aimed to determine whether complete blood count(CBC) parameters, such as the white blood count(WBC), hemoglobin(Hb), platelet(PLT), red cell distribution width(RDW), mean platelet volume(MPV), platelet distribution width(PDW), neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), and monocyte-to-lymphocyte ratio(MLR), have a predictive value in the detection of gastric cancer(GC) and intestinal metaplasia(IM). Methods: While proven GC, IM, and healthy control(HC) patients were included, patients with the comorbid disease were excluded, and univariate analyses compared three groups. The receiver operating characteristic(ROC) curve analysis was run for CBC parameters. The area under the curve(AUC) was evaluated, and a cut-off value was determined. The sensitivity and specificity of each parameter were considered. Results: The GC, IM, and HC groups consisted of 72(33%), 73(34%), and 72(33%) patients, respectively. RDW, PLT, NLR, PLR, and MLR were significant between GC and IM. The highest AUC (0.727) was obtained for the PLT yielding a 56.9% sensitivity and 79.4% specificity at a cut-off value of 151.8. The AUC of RDW was found as 0.691 and 0.626 for pairwise comparisons of GC-HC and IM-HC, respectively. At a cut-off value of 13.4, PLR yielded 70.8% sensitivity and 61.1% specificity in the detection of GC, while 64.4% sensitivity and 51.1% specificity for IM. Conclusion: CBC parameters, such as RDW, PLT, NLR, PLR, and MLR, have value in detecting GC. RDW also has diagnostic value in helping to detect IM. PLR can help to distinguish patients with GC from those with IM. These inexpensive, easily accessible parameters may help in the timely diagnosis of GC and IM. Keywords: Gastric cancer; intestinal Metaplasia; Monocyte to lymphocyte ratio; red cell distribution; hemoglobin; platelet; neutrophil-lymphocyte ratio; platelet-lymphocyte ratio; inflammation.

Comparative Analysis of Mutation in the Buccal Epithelium and Blood in Patients with Lung Cancer and Healthy People

Lung cancer is one of the leading causes of cancer death. Finding new methods for the early and accurate diagnosis of lung cancer is critical for effective treatment. We have shown that patients with lung cancer have more mutations in the FLT3, PDGFRA, KDR, PIK3CA, HRAS, FGFR3 genes in the buccal epithelium than people without diagnosed lung cancer. Thus, study of molecular alterations may be used as a method for the accurate diagnosis of lung cancer in the early stages of investigational procedure.

Enhanced Cancer Biomarker Detection using Fe3O4 Magnetic Nanoparticles: Synthesis, Surface Modifications and Diagnostic Applications: A Review

Fe3O4 magnetic nanoparticles exhibit significant potential for cancer detection due to their unique magnetic properties and versatility. This review examines their role in enhancing cancer biomarker detection, focusing on synthesis methods, surface modifications and diagnostic accuracy. Fe3O4 nanoparticles serve as sensitive MRI contrast agents, facilitating early cancer diagnosis and improving patient prognosis. Synthesis techniques influence their properties, morphology and dimensions, which affect performance. Surface modifications enhance targeting and reduce immunological reactions, enabling precise biomarker detection and effective drug delivery to tumours, minimizing damage to healthy tissue. Despite these advantages, challenges remain in optimizing delivery efficiency and overcoming medication resistance. Further research is required to understand the pharmacokinetics and pharmacodynamics of these nanoparticles, including their distribution, metabolism and physiological impacts. This review provides insights into the potential of Fe3O4 magnetic nanoparticles as cancer detection agents, aiming to guide future research towards developing safer and more efficient applications in medical diagnostics and treatment. HIGHLIGHTS Fe3O4 magnetic nanoparticles exhibit unique magnetic properties that are highly effective in detecting cancer biomarkers. These nanoparticles enable early cancer diagnosis by identifying biomarkers at low concentrations with high sensitivity. The review assesses various synthesis techniques for Fe3O4 magnetic nanoparticles to optimize their detection capabilities. Exploration of surface modification strategies to enhance the functionality and efficacy of Fe3O4 nanoparticles in cancer biomarker detection. Enhanced diagnostic accuracy through the application of Fe3O4 magnetic nanoparticles, promising improved outcomes in cancer detection technologies. GRAPHICAL ABSTRACT