Early Detection Research Articles

The 2-week wait pathway for suspected head and neck cancers in patients with throat and voice symptoms: referral patterns, common clinical practice and diagnostic efficacy of NICE guidelines.

The 2-week wait (2ww) referral pathway has been introduced into UK clinical practice to increase the early detection of cancer and improve survivals. The efficiency of this system for head and neck (H&N) cancers has been questioned over the years because of evidence of low pick-up rates. H&N cancers present with a wide variety of non-specific symptoms, particularly throat and voice symptoms. These symptoms need to be accurately interpreted together with risk factors if they are to be addressed adequately and overload of cancer facilities avoided. One of the most common outcomes of H&N 2ww referrals is laryngopharyngeal reflux (LPR), a common condition that could be diagnosed and managed in the primary care setting with a prescription of proton pump inhibitors (PPI) trials. We retrospectively analysed a cohort of consecutive patients referred on the 2ww pathway for throat and voice symptoms at University College London Hospital H&N cancer clinic during two months in 2019. A total of 101 patients (43.6% men, mean age 53.3 years) were included. Throat and voice symptoms were described as intermittent in 52.5% and non-lateralised in 88.1%. Diagnosis of LPR was made in 59.4% of the referrals. A PPI trial was prescribed by general practitioners (GPs) in only 7.9% of cases. The cancer pick-up rate in our cohort is 2.9%. An improved awareness of the symptoms of LPR could guide GPs to prescribe trials of PPIs in low-risk patients before rushing into a referral on a cancer pathway. This would improve the 2ww process on many levels, reduce the burden on the National Health Service and avoid patients' psychological distress.

Abstract 4138449: High Output Heart Failure from an Aortocaval Fistula

Introduction: High output heart failure (HOHF) due to a post-traumatic aortocaval fistula is a rare entity. This case seeks to explore the presentation as well as complexities of management of these patients. Case Presentation: A 50-year-old male with a prior right abdominal stab wound requiring exploratory laparotomy 20 years ago presented with worsening dyspnea on exertion and palpitations. Physical examination was remarkable for jugular venous distention, lower extremity edema, and a pulsatile mass in the right abdomen with an audible bruit. A 12-lead ECG showed rapid atrial flutter. A transthoracic echocardiogram (TTE) showed a reduced left ventricular ejection fraction (LVEF) of 30-35%, severely dilated atria and inferior vena cava (IVC) with high velocity flow. Abdominal computed tomography venogram showed dilation of the IVC up to 8cm and an aortocaval fistula at the level of the right renal vessels, resulting in an atrophic right kidney. Cardiac catheterization was notable for a cardiac output (CO) of 12L/min, cardiac index (CI) of 7L/min/m2 and reduced systemic vascular resistance (SVR). He was managed with beta blockers and diuretics, followed by TEE-guided cardioversion for atrial flutter to restore sinus rhythm. Vascular surgery performed a successful, first of its kind endovascular repair of the abdominal aorta with stenting of the right common iliac artery and placement of a 20mm Amplatzer Septal Occluder device in the right renal artery. He tolerated the procedure well, and post-operative TTE showed a recovered LVEF of 55-60% and CO of 6L/min. Discussion: HOHF is defined by a CO more than 8L/min or CI more than 4L/min/m2 along with clinical symptoms. Decreased SVR leads to sympathetic activation and stimulation of the renin-angiotensin-aldosterone system, leading to fluid retention, cardiac remodeling, and exacerbation of symptoms. Challenges present in the management of HOHF and the use of vasodilatory agents should be avoided, as they can exacerbate the problem. In our case, limitations were also faced in the use of many traditional HF therapies due to renal dysfunction as a result of the fistula. Therefore, repair was the only viable long-term solution. Conclusion: This case displays an uncommon etiology of HOHF, emphasizing the need for meticulous evaluation to ensure proper management. Early detection of aortocaval fistulas is crucial to prevent worsening cardiac dysfunction, with symptoms resolving rapidly post-repair.

Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms

ObjectiveAbdominal aortic aneurysm (AAA) is a life-threatening vascular condition. This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.MethodsSourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. Additionally, a separate dataset (GSE7084) was designated as the validation set. Enrichment analyses were carried out to explore the underlying biological mechanisms using Disease Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology. We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. Finally, the single sample gene set enrichment analysis algorithm was applied to probe the immune landscape in AAA and its connection to the selected feature genes.ResultsWe discovered 72 differentially expressed genes (DEGs) when comparing healthy and AAA samples, including 36 upregulated and 36 downregulated genes. Functional enrichment analysis revealed that the DEGs associated with AAA are primarily involved in inflammatory regulation and immune response. By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. The diagnostic performance of all these genes was strong, as revealed by the ROC analysis. A significant increase in 15 immune cell types in AAA samples was observed, based on the analysis of immune cell infiltration. In addition, the 3 feature genes show a strong linkage with different types of immune cells.ConclusionThree feature genes (MRAP2, PPP1R14A, and PLN) related to the development of AAA were identified. These genes are linked to immune cell activity and the inflammatory microenvironment, providing potential biomarkers for early detection and a basis for further research into AAA progression.

Detection and genetic characterization of multidrug-resistant staphylococci isolated from public areas in an international airport.

The environmental realm has been acknowledged as a pivotal arena for the emergence and propagation of antimicrobial resistance. To further explore insight into antimicrobial resistance dynamics beyond clinical and veterinary settings, we embarked on an environmental surveillance initiative targeting the prevalence of antibiotic-resistant bacteria within the bustling confines of an international airport in Japan. Our findings illuminate a high prevalence of methicillin-resistant staphylococci (46.3%) on frequently contacted surfaces in the public domain. Notably, Staphylococcus haemolyticus and S. epidermidis emerged as the preeminent carriers of the mecA gene. Intriguingly, we encountered a virulent strain of livestock-associated MRSA harboring a PVL-positive ST1232 clone, CC398 lineage. Further scrutiny unveiled a repertoire of resistance mechanisms, the methicillin-resistant isolates exhibited two or more resistance genes conferring resistance against different types of antibiotics, including beta-lactams, macrolides, lincosamides, aminoglycosides, chloramphenicol, and fosfomycin. Revealing multidrug-resistant CoNS and a LA-MRSA across various surfaces in urban public areas unearths a looming public health hazard. Thus, implementation of molecular surveillance is imperative, augmenting our capacity for early detection and mitigation of the insidious spread and potential transfer of antibiotic resistance genes and virulence factors amidst urban settings, notably within pivotal nodes such as airports.

Do sociodemographic and health predictors affect the non-insulin-based insulin resistance index? A cross-sectional study.

Insulin resistance (IR) is considered a risk factor for cardiovascular diseases (CVD). Therefore, early diagnosis of IR is clinically significant for primary and secondary CVD prevention initiatives. In addition, non-insulin metabolic indices may be useful for diagnosing IR. The first objective was to estimate the triglyceride and glucose (TyG) index and the metabolic score for insulin resistance (METS-IR) index values in a local community with high social deprivation and increased cardiovascular risk according to the Systematic Coronary Risk Evaluation scale. The second objective was to identify significant sociodemographic and health predictors for the TyG index and METS-IR index. This cross-sectional study was conducted in the local community of Janów district in eastern Poland and consisted of 2 stages. The 1st stage involved basic research (n = 4,040), while the 2nd stage involved enhanced diagnostics (n = 2,657). Data from the 2nd stage was used for the analyses. Anthropometric and physiological measurements were taken, blood was drawn for laboratory tests, selected sociodemographic and health variables were evaluated, and the TyG index and METS-IR index were calculated. The mean TyG index score in the study group was 8.65 (±0.58), and the mean METS-IR index score was 41.45 (±9.02). Both indices were significantly associated with age, male sex, smoking, and systolic blood pressure (SBP) in a multivariable model. In addition, alcohol consumption and body mass index (BMI) were significantly correlated with the TyG index, whereas education was significantly associated with the METS-IR index. Our results show the association between IR and sociodemographic and health variables in a group with a high social deprivation rate and increased cardiovascular risk. Early detection of cardiometabolic risk is important for both primary and secondary CVD prevention. In primary healthcare, this can be accomplished through surrogate markers of IR.

Abstract 4124677: Left ventricular function and myocardial longitudinal strain analysis in patients with Chagas disease: case series and systematic literature review.

Introduction: Chagas disease is a significant cause of tropical disease-related mortality. It presents in acute, indeterminate, and chronic phases, with chronic cardiomyopathy being the most severe form, leading to heart failure, arrhythmias, and sudden cardiac death. Conventional echocardiography often fails to detect early subclinical dysfunction, whereas Global longitudinal strain (GLS) may provide earlier detection of myocardial strain abnormalities. Left ventricular dysfunction is an independent predictor of mortality and GLS is an emerging modality that may aid early detection of cardiac involvement. Objective: This study aims to compare left ventricular function using multiple echocardiographic parameters between patients with indeterminate and chronic forms of Chagas disease and to perform a systematic literature review. Methods: The observational study involved 11 patients (mean age 76.36 years) from a tertiary hospital in Brazil, classified as indeterminate (45%) or chronic (55%). Serological confirmation and detailed echocardiographic evaluations, including GLS, were performed. A systematic literature review was also conducted on GLS in Chagas cardiomyopathy. Results: Patients with chronic Chagas disease showed significantly lower LVEF and GLS compared to those with the indeterminate form. Segmental strain analysis revealed consistent contractility reductions across specific ventricular segments in both groups. Discussion: Chagas cardiomyopathy often leads to dilated cardiomyopathy with significant arrhythmias and heart failure. GLS can detect early myocardial changes even before a significant drop in LVEF, indicating its potential for early diagnosis and better management of Chagas cardiomyopathy. Literature review supports the use of GLS for detecting early cardiac involvement and predicting adverse outcomes. Conclusion: Incorporating GLS with conventional echocardiography offers enhanced early detection of myocardial changes in Chagas disease. While promising, further research is needed to establish the clinical significance and impact of these techniques in routine practice.

Abstract 4143267: Bicaudal-C Homolog 1 Plays a Significant Role in Cardiac Development and Congenital Heart Defects

Background: Congenital heart defects (CHDs) are the most common birth defects globally and often require palliative surgical interventions with limited long-term effectiveness. Prenatal detection of CHDs is challenging due to the absence of heart failure signs in most fetuses. Thus, early detection methods and therapeutic drugs to correct abnormal heart development are crucial. We have discovered that the RNA-binding protein Bicaudal-C homolog 1 (Bicc1) influences normal and abnormal heart development. Bicc1 is involved in post-transcriptional regulation and interacts with RNA interference (RNAi) components. While its role in left-right patterning, kidney development, and the interaction with PKD genes ( Pkd1 and Pkd2 ) is documented, the role of Bicc1 and its expression in heart development and whether it relies on interaction with Pkd1/Pkd2 are not understood. Methods and Results: To start addressing these knowledge gaps, we used a transgenic mouse line with an in-frame fusion of Bicc1 and the LacZ reporter gene to perform β-galactosidase staining, RNAscope and immunostaining to identify the cardiac cell types expressing Bicc1 . High Bicc1 expression was observed in embryonic and neonatal hearts, particularly in the ventricles, atria, and endothelial cells of blood vessels. Expression decreased in adulthood but persisted at basal levels. We observed that Bicc1 knockout (KO) developed heterotaxy of the heart and enlarged atria. Next, to elucidate the gene and miRNA regulatory networks involved in cardiac development disrupted by the absence of Bicc1, we carried out RNA- and miRNA-seq on hearts from wild-type and Bicc1 KO embryonic hearts at different developmental stages. Several genes and miRNAs associated with ciliopathy and heterotaxy, including miR-196 , Hand1 , and Lefy2, were significantly altered in Bicc1 KO mice. Moreover, as reported for the kidney, Pkd2 mRNA expression was altered in Bicc1 KO mice, while Pkd1 expression remained unchanged. Finally, we detected changes linked to lipid metabolism and related pathways, such as members of aldehyde dehydrogenase ( Adlh1a ) and cytochrome P450 family ( Cyp5a1 ), that were altered in the absence of Bicc1 . Conclusions: Our study reveals additional role(s) for Bicc1 in heart development suggesting its involvement in regulating heart metabolism in fetuses. These findings provide a conceptual basis for future investigations into the function of Bicc1 in heart development and how its disruption contributes to CHDs.

Mechanical Properties of Inflamed Appendix Tissues

Background/Objectives: Histopathological examination enables visualization of morphological changes in cells and tissues. In recent years, there has been increasing interest in assessing the mechanical properties of tissues that cannot be determined by standard histopathological examinations. Mechanobiology is crucial in human physiology and holds promise for uncovering new diagnostic markers for disease processes such as carcinogenesis and inflammation. In this study, we concentrated on measuring the mechanical properties of appendix biopsy specimens to identify potential mechanomarkers of inflammation. Appendix tissues provided the opportunity to measure mechanical properties both with an atomic force microscope and a shear rheometer. Methods: The atomic force microscope AFM—NanoWizard 4 BioScience JPK/Bruker was used for the evaluation of the elastic modulus (i.e., Young’s modulus) of appendix tissues. Young’s modulus was derived from the Hertz-Sneddon model applied to force-indentation curves. The rheological properties of macroscopic samples were measured on a parallel-plate, strain-controlled shear rheometer Anton Paar MCR302. Results: The data collected suggest that elasticity, expressed as Young’s modulus and the storage modulus, could be considered a marker indicating appendix tissue inflammation. Young’s modulus of inflamed appendix tissues was found to be significantly lower than that of healthy ones, with an average reduction of 67%. Furthermore, it was observed that inflamed appendix tissues, in comparison to healthy ones, respond differently under varying axial and shear stresses, enabling their identification. Conclusions: Our findings suggest that the specific mechanical properties of inflamed vermiform appendices could serve as novel mechanomarkers for the early detection and monitoring of appendicitis.

Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study.

Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. This study aims to examine the feasibility of developing a novel channel state information-based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing-based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults' Wi-Fi sensing technology acceptance. This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. DERR1-10.2196/53447.

Preterm Brain Injury: Mechanisms and Challenges.

Preterm fetuses and newborns have a high risk of neural injury and impaired neural maturation, leading to neurodevelopmental disability. Developing effective treatments is rather challenging, as preterm brain injury may occur at any time during pregnancy and postnatally, and many cases involve multiple pathogenic factors. This review examines research on how the preterm fetus responds to hypoxia-ischemia and how brain injury evolves after hypoxia-ischemia, offering windows of opportunity for treatment and insights into the mechanisms of injury during key phases. We highlight research showing that preterm fetuses can survive hypoxia-ischemia and continue development in utero with evolving brain injury. Early detection of fetal brain injury would provide an opportunity for treatments to reduce adverse neurodevelopmental outcomes, including cerebral palsy. However, this requires that we can detect injury using noninvasive methods. We discuss how circadian changes in fetal heart rate variability may offer utility as a biomarker for detecting injury and phases of injury.

Diagnostic and predictive value of radiomics-based machine learning for intracranial aneurysm rupture status: a systematic review and meta-analysis.

Currently, the growing interest in radiomics within the clinical practice has prompted some researchers to differentiate the rupture status of intracranial aneurysm (IA) by developing radiomics-based machine learning models. However, systematic evidence supporting its performance remains scarce. The purpose of this meta-analysis and systematic review is to assess the diagnostic performance of radiomics-based machine learning for the early detection of IA rupture and to offer evidence-based recommendations for the application of radiomics in this area.PubMed, Cochrane, Embase, and Web of Science databases were searched systematically up to March 2, 2024. The Radiomics Quality Score (RQS) was employed to assess the risk of bias in all included primary studies. We separately discussed the diagnostic or predictive performance of machine learning for IA rupture status based on task type (diagnosis or prediction).We finally included 15 original studies covering 9,111 IA cases. In the validation cohort, radiomics demonstrated a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, as well as SROC curve of 0.84 (95% CI: 0.76-0.90), 0.82 (95% CI: 0.77-0.86), 4.7 (95% CI: 3.7-5.8), 0.19 (95% CI: 0.13-0.29), and 24 (95% CI: 15-40), respectively, for the diagnostic task of aneurysm rupture status. Only 2 studies (3 models) addressed predictive tasks, with sensitivity and specificity ranging from 0.77 to 0.89 and from 0.69 to 0.87, respectively.Radiomics-based machine learning exhibits promising accuracy for early identification of IA rupture status, whereas evidence for its predictive capability is limited. Further research is needed to validate predictive models and provide insights for developing specialized strategies to prevent aneurysm rupture.

Utilizing C-Reactive Protein (CRP) and CRP Ratios for Early Detection of Postoperative Complications Following Rectal Cancer Surgery

Background: Surgical treatment for rectal cancer may cause complications that exacerbate the condition, lengthen hospital stays, and raise healthcare costs. Rectal cancer surgery complications can be detected immediately with postoperative C-reactive protein (CRP) monitoring. Elevated levels of CRP indicate the presence of inflammation and can serve as a predictive factor for future outcomes. Methods: A retrospective cohort analysis was performed on 67 rectal cancer surgery patients. Prior to and after surgery, CRP levels were measured on days 1–3 and 4–7. Postoperative complications were linked to CRP, surgical approach (open, laparoscopic, conversion), and patient characteristics. This study included t-tests, chi-square tests, Mann–Whitney U tests, ANOVA, Pearson and Spearman correlation analyses, and logistic regression. Results: There was a significant correlation between high levels of CRP on postoperative days 4–7 and the development of problems (p < 0.001). The ratios of CRP/albumin and CRP/platelet were highly predictive of problems over this period (p = 0.000033). Patients who encountered problems had a notably greater estimated blood loss (p = 0.0086). Logistic regression analysis demonstrated a statistically significant relationship between higher Charlson Comorbidity Index (CCI) scores and an elevated probability of experiencing problems (p = 0.0078). Moreover, patients who underwent laparoscopic surgery but had to be converted to an open method saw a notably greater incidence of complications (p = 0.0022). From a financial standpoint, the average cost per patient with complications was EUR 1128.75, resulting in a total cost of EUR 44,021.25 for all 39 patients. Conversely, patients who did not experience any difficulties had a cost of EUR 731.25 per patient, resulting in a total of EUR 20,475.00 for all 28 patients. Conclusions: Regularly monitoring CRP, particularly between days 4 and 7 following surgery for rectal cancer, can promptly identify any complications. Monitoring CRP levels and promptly managing any abnormalities can enhance surgical outcomes and reduce healthcare costs.

Abstract 4119891: Risk Factors of Anemia in Pulmonary Arterial Hypertension Patients with Endothelin Receptor Antagonists Administration and Related Treatment

Background: Endothelin receptor antagonists (ERAs) demonstrated significant efficiency in delaying clinical deterioration and improving survival of patients with pulmonary arterial hypertension (PAH). However, the frequent occurrence of anemia after ERAs administration has gained clinical attention currently, whereas the etiology of anemia is unclear. The risk factors of ERAs associated anemia remained undefined. Therefore, we aim to investigate risk factors of developing anemia after ERAs treatment and whether iron supplement could correct anemia. Method: A retrospective cohort study was performed among patients with PAH who received ERAs between January, 2017 and December, 2019 in Fuwai hospital. The primary outcome was development of anemia after ERAs therapy and secondary outcome was correction of anemia after iron supplement. Results: A total of 120 patients with PAH were included in the study and 28 patients developed anemia during follow up . Distinctively, all the patients who developed anemia were female. After ERAs administration, iron metabolism (Ferritin 95.29±144.79 μg/L versus 69.9±118.25 μg/L, P <0.001) and hemoglobin (154.21±23.80 g/L versus 136.12±28.61 g/L, P <0.001) reduced significantly in the total cohort with patients who developed anemia more significant. Baseline iron metabolism (Ferritin: HR 0.98, 95%CI 0.96-0.99, P =0.02) was independently associated with development of anemia after ERAs administration. Compared with patients with iron replete, patients with iron deficiency had over 9-fold risk of developing anemia after receiving ERAs (HR = 9.59, 95%CI 3.62-25.41, P < 0.01). Iron supplement could effectively correct anemia. Conclusion: It is advised that patients receiving ERAs monitor iron metabolism and hemoglobin regularly, especially females and those with low hemoglobin level and compromised iron homeostasis before ERAs administration, which facilitates prevention and early detection of anemia. ERAs-related anemia is mostly mild, oral iron supplement could effectively reverse anemia and aberrant iron metabolism.

Anomaly Detection and Analysis in Nuclear Power Plants

Industries are increasingly adopting digital systems to improve control and accessibility by providing real-time monitoring and early alerts for potential issues. While digital transformation fuels exponential growth, it exposes these industries to cyberattacks. For critical sectors such as nuclear power plants, a cyberattack not only risks damaging the facility but also endangers human lives. In today’s digital world, enormous amounts of data are generated, and the analysis of these data can help ensure effectiveness, including security. In this study, we analyzed the data using a deep learning model for early detection of abnormal behavior. We first examined the Asherah Nuclear Power Plant simulator by initiating three different cyberattacks, each targeting a different system, thereby collecting and analyzing data from the simulator. Second, a Bi-LSTM model was used to detect anomalies in the simulator, which detected it before the plant’s protection system was activated in response to a threat. Finally, we applied explainable AI (XAI) to acquire insight into how distinctive features contribute to the detection of anomalies. XAI provides valuable explanations of model behavior by revealing how specific features influence anomaly detection during attacks. This research proposes an effective anomaly detection technique and interpretability to better understand counter-cyber threats in critical industries, such as nuclear plants.

Mammography In Breast Cancer Screening – Current Knowledge, Challenges, The Impact of Artificial Intelligence, And Effectiveness With A Focus On Poland

Mammography is a critical tool in breast cancer screening and secondary prevention, enabling early detection and significantly reducing breast cancer mortality. This literature review assesses the effectiveness of mammography in identifying early-stage tumors, discussing its advantages, limitations, and specific challenges. Additionally, advancements in imaging techniques, such as 3D tomosynthesis, are examined, especially for their enhanced sensitivity in women with dense breast tissue, which reduces structural overlap and improves diagnostic precision. The integration of artificial intelligence (AI) in mammographic image analysis opens new opportunities, supporting radiologists by automating the detection and classification of potential lesions, thereby increasing screening efficiency and reducing false-positive rates. However, effective implementation of AI requires seamless integration with clinical systems and ongoing research to refine algorithms for diverse clinical needs. Furthermore, legal frameworks are essential to delineate responsibility in AI-supported diagnostics. This review emphasizes the status of breast cancer screening in Poland, where participation rates remain low despite the availability of free screenings. This highlights the need for improved public awareness and accessibility, especially in underserved areas. Overall, the findings underscore the fundamental role of mammography in breast cancer detection, with AI and other technological advancements significantly enhancing diagnostic accuracy while identifying areas for further improvement.

Abstract 4123501: Coronary Artery Calcium in Asymptomatic Arabs: Prevalence and Distribution

Background: Coronary artery calcification (CAC) is an established marker of subclinical atherosclerosis and predictor of cardiovascular events. Despite extensive research on CAC in various populations, data focusing on Arab populations, particularly asymptomatic individuals, is limited. Methods: We analyzed data from 2,739 asymptomatic self-referred Arabs included in the Kuwait Hospital Awareness Campaign for Early Detection of Atherosclerotic Coronary Artery Disease study. Traditional CVD risk factors were assessed, and ECG-gated cardiac CT imaging was used to measure using the Agatston method. Results: The mean±SD age of the sample was 48.4±9.8 and 41.8% were females. Kuwaitis comprised 61.6% of the sample, Egyptians 20.5%, and Levantines 16.4%. CAC was >0 in 32.3%, ≥100 in 11.5%, and ≥400 in 4% of the sample. CAC prevalence increased with age and was higher in Arab males with a 12 year earlier onset in males than females. The 50 th , 75 th , 90 th and 95 th percentiles of CAC for Arab females were, 0, 0, 39, and 167 and for males were 0, 25, 191, and 470, respectively. The presence of CAC was associated with traditional risk factors including diabetes, smoking, hypertension and LDL-C. Levantines and Kuwaitis had the highest prevalence of CAC and CVD risk factors; these groups were independently associated with higher CAC levels. In all age and sex subgroups except older males, the 75 th percentiles of CAC were below 100. Therefore, a larger number of subjects were identified as high risk using the 75 th age- and sex-specific CAC percentile than with CAC≥100 (19.3% vs 11.5%). Conclusion: This study provides comprehensive insights into subclinical atherosclerosis in asymptomatic Arabs, emphasizing the importance of early risk stratification for primary CVD prevention using CAC scoring. Our findings highlight that Arabs are not a homogeneous group with regards to prevalence of CVD risk factors and CAC. This study underscores the need to consider both absolute and age- and sex-specific CAC percentile values to inform primary prevention strategies in this predominantly young population.

From microbes to medicine: harnessing the power of the microbiome in esophageal cancer

Esophageal cancer (EC) is a malignancy with a high incidence and poor prognosis, significantly influenced by dysbiosis in the esophageal, oral, and gut microbiota. This review provides an overview of the roles of microbiota dysbiosis in EC pathogenesis, emphasizing their impact on tumor progression, drug efficacy, biomarker discovery, and therapeutic interventions. Lifestyle factors like smoking, alcohol consumption, and betel nut use are major contributors to dysbiosis and EC development. Recent studies utilizing advanced sequencing have revealed complex interactions between microbiota dysbiosis and EC, with oral pathogens such as Porphyromonas gingivalis and Fusobacterium nucleatum promoting inflammation and suppressing immune responses, thereby driving carcinogenesis. Altered esophageal microbiota, characterized by reduced beneficial bacteria and increased pathogenic species, further exacerbate local inflammation and tumor growth. Gut microbiota dysbiosis also affects systemic immunity, influencing chemotherapy and immunotherapy efficacy, with certain bacteria enhancing or inhibiting treatment responses. Microbiota composition shows potential as a non-invasive biomarker for early detection, prognosis, and personalized therapy. Novel therapeutic strategies targeting the microbiota—such as probiotics, dietary modifications, and fecal microbiota transplantation—offer promising avenues to restore balance and improve treatment efficacy, potentially enhancing patient outcomes. Integrating microbiome-focused strategies into current therapeutic frameworks could improve EC management, reduce adverse effects, and enhance patient survival. These findings highlight the need for further research into microbiota-tumor interactions and microbial interventions to transform EC treatment and prevention, particularly in cases of late-stage diagnosis and poor treatment response.

Utilization of Computed Tomography at the Aydin Adnan Menderes University Faculty of Veterinary Medicine Research and Application Hospital: An Analysis of Preliminary Results on Patient Demographics and Case Distribution

This study examines the distribution of computed tomography (CT) usage at Aydin Adnan Menderes University Faculty of Veterinary Medicine Research and Application Hospital between 2023 and 2024. CT, a widely adopted diagnostic tool in veterinary medicine, allows for detailed imaging of internal organs and anatomical structures, enhancing the diagnosis of complex cases. The study analyzed data from 143 animals, including dogs, cats, and other species, to assess the frequency of CT scans based on species, age, sex, breed, and imaged anatomical regions. A total of 350 CT scans were reviewed, with dogs (n=87) undergoing the majority of scans (200), followed by cats (n=50) with 175 scans, and other species accounting for 26 scans. Cranium, thorax, and abdomen were the most frequently imaged regions across all species. The study found that older animals, particularly large-breed dogs and male cats, required more imaging, while younger animals (0-6 months) underwent fewer CT scans. The findings emphasize the need for tailored diagnostic approaches based on species, age, and sex to improve early detection and treatment outcomes in veterinary practice. Furthermore, the study highlights the potential for expanding CT use in non-traditional veterinary species, with the goal of improving diagnostic precision. The results provide important insights for enhancing veterinary services through the strategic application of CT imaging.

Biomass-Derived Organonanomaterials as Contrast Agents for Efficient Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is a popular imaging tool that is valuable for the early detection and monitoring of malignancies because it does not involve radiation and is noninvasive. Metal-based contrast agents (CAs) are commonly used in clinical settings despite concerns about the toxicity of free metals. Therefore, finding alternative nontoxic imaging probes is vital. In this work, we have synthesized and effectively utilized sustainable biomass lignin-based all-organic nanoconjugates linked with nitroxide radicals as MRI CAs. Lignin grafted with poly(4-glycidyloxy-2,2,6,6-tetramethylpiperidine-1-oxyl) (LPGT) exhibits a longitudinal relaxivity of 0.54 mM-1 s-1. LPGT shows exceptional characteristics, including resistance to reduction and nontoxicity toward living organisms. LPGT displays enhanced MRI contrast in the BALB/c mouse model for a duration exceeding 4.35 h. Our primary goal is to design MRI agents that are exceptionally effective sustainable biomass-derived materials and do not require the use of metals. Nicely, LPGT offers adequate contrast enhancement at 5-fold lower (0.020 mmol/kg) than the standard dose (0.1 mmol/kg), easing worries about toxic metal buildup. Consequently, LPGT shows promise as a feasible CA for metal-free MRI.

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -The Recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi, and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyze only the nodules from the profile, respected features are calculated using image processing techniques. Based on the review, textural features were promising ones in medical image analysis and for solving computer vision problems. The significance of extracting the hidden features (textural) enables the higher performance of deep learning algorithms (DL) predominantly in medical imaging, which has resulted in improvement in accuracy in recent years. Earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. In this paper, we discussed the overview of lung cancer along with publicly available datasets for research purposes. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages, and limitations regarding possible research gaps. The paper compared the recent studies of deep learning models with and without feature extraction and concluded that DL models that include feature extraction were better than the others.