Non-invasive Cancer Research Articles

New concept in selecting blue dye injection site effect on clinical outcome of early-stage breast cancer patients: a retrospective cohort

BackgroundClinico-anatomical review and pilot studies demonstrated that intraparenchymal injection at any site, even those not containing the index lesion, or periareolar injections should provide concordant outcomes to peritumoral injections.MethodThis was a single-center retrospective cohort at King Chulalongkorn Memorial Hospital. The electronic medical records of patients were characterized into conventional and new injection concept groups. The inclusion criteria were patients who had either a mastectomy or BCS along with SLNB. We excluded patients who underwent ALND, received neoadjuvant therapy, or had non-invasive breast cancer. The primary outcome was the 5-year rate of breast cancer regional recurrence. Additionally, we reported on the re-operation rate, disease-free period, distant disease-free period, mortality rate, and recurrence rates both locoregional and systemic. Recurrences were identified through clinical assessments and imaging. Surgical technique: 3 ml of 1%isosulfan blue dye was injected, with the injection site varying according to the specific concept being applied. In cases of SSM and NSM following the new concept, the blue dye was injected at non-periareolar and non-peritumoral sites. After the injection, a 10-minute interval was observed without massaging the injection site. Following this interval, an incision was made to access the SLNs, which were subsequently identified, excised, and sent for either frozen section analysis or permanent section examination.ResultThere were no significant differences in DFS, DDFS or BCSS between the two groups (p = 0.832, 0.712, 0.157). Although the re-operation rate in the NI group was approximately half that of the CI group, this difference was not statistically significant (p = 0.355).ConclusionOur study suggests that tailoring isosulfan blue dye injection site based on operation type rather than tumor location is safe and effective approach for SLN localization in early-stage breast cancer. However, this study has limitations, including being a single-center study with low recurrence and death cases. Future studies should aim to increase the sample size and follow-up period.

Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization

ProblemsRaman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model. AimsUsing Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information. MethodsOptimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks. ResultsCompared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions. ConclusionRaman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.

Quality of Life Among Breast Cancer Patients in Mures County, Romania: A Cross-Sectional Study.

Background and aim Psychiatric pathology does not always start on its own but may be conditioned or triggered by a comorbidity with a high impact on the human psyche. When there are comorbidities, psychiatric pathology can occur due to the high diagnostic burden. Our study aims to find out if there is a correlation between the diagnosis of breast cancer and its severity,and psychiatric symptoms such as depressive mood, atypical anxiety, or even autolytic ideation that directly influence the quality of life of patients. Materials and methods The study is a prospective, cross-sectional, single-center study carried out between December 2023 and June 2024 at the Mureș County Clinical Hospital in Romania. The sample population had to be at least 18 years old and had to bediagnosed with breast cancer recently.We applied two tests, WHODAS 2 (World Health Organization's Disability Assessment Schedule 2.0) and level 1 (level 1 of cross-sectional measurements of symptoms), to be able to measure and aid assessment of mental health domains that are important across psychiatric diagnoses and also the degree of disability triggered by breast cancer. The statistical analysis included descriptive statistics and interferential statistics. Statistical tests, such as Shapiro-Wilk, Kruskal-Wallis, and Mann-Whitney U tests with Bonferroni correctiontests, were used.The p-value was set to 0.05 with a confidence interval (CI) of 95%. Results The study included 120 women diagnosed with breast cancer, with a mean age of 56.64 ± 9.46 years. Regarding the severity of the diagnosis, 44 women (36.66%) had non-invasive cancer, 58 (48.33%) had invasive cancer, and 18 (15%) had metastases.There was a statistically significant difference between three of the five selected level 1 domains across cancer types.The WHODAS 2.0 disability scores showed a significant difference between groups (p < 0.001). Subjects with non-invasive cancer had the lowest WHODAS 2.0 score, followed by the invasive group, while metastases had the highest score. Conclusions Following the application of the two tests, level 1 and WHODAS 2.0, to our group of subjects, statistically significant differences were observed between the three categories of subjects. The degree of disability and the occurrence of psychological symptoms differed according to the severity of breast cancer. Adapting to the status of an oncological patient entails multiple changes from a psycho-emotional, social, occupational, and professional point of view. Although the most recent medications prolong survival, a holistic approach that considers psychological aspects can improve patients' long-term results.

Exploring Salivary Biomarkers for Tumor Diagnosis: A Narrative Review.

A promising method for non-invasive cancer diagnosis and prognosis is through salivary biomarkers. By utilizing the distinct characteristics of saliva and the progress made in biomarker studies, these markers provide more accurate diagnoses for a wider range of malignancies. An attempt was made to thoroughly investigate the field of salivary biomarkers for tumor prognosis and diagnosis, with an emphasis on their use in various cancer forms. Predetermined search criteria were utilized for a systematic search across numerous databases for peer-reviewed papers from 2009 to 2021. Studies concentrating on the detection, validation, and clinical use of salivary biomarkers for different types of cancers were included in the inclusion criteria. Initially, 238 articles were found, of which 15 relevant articles satisfied the inclusion requirements. Information on study aims, methodology, findings, and conclusions were gathered for data extraction. We identified recurrent themes, patterns, and contradictions by a thematic analysis. We also assessedstate-of-the-art salivary biomarkers for tumor diagnosis and prognosis. One major finding is the identification of biomolecules in saliva linked to several cancer forms, including pancreatic, oral, breast, lung, and stomach cancers. There is an increasing amount of evidence demonstrating the value of saliva-based diagnostics in oncology. This is due to new detection methodsand developments in salivary proteomics and genomics. Identification of exosomes and microvesicles as salivary biomarker profiles offered molecular understandings of the etiology and evolution of cancer, thereby opening new avenues for diagnosis and treatment. Salivary biomarkers are a non-invasive approach for the early detection and prediction of cancer, thanks to the unique properties of saliva and advancements in biomarker research. This potential revolution could enhance patient outcomes and reduce cancer-related deaths.

Peptide-IR820 Conjugate: A Promising Strategy for Efficient Vascular Disruption and Hypoxia Induction in Melanoma.

Photothermal therapy (PTT) has emerged as a noninvasive and precise cancer treatment modality known for its high selectivity and lack of drug resistance. However, the clinical translation of many PTT agents is hindered by the limited biodegradability of inorganic nanoparticles and the instability of organic dyes. In this study, a peptide conjugate, IR820-Cys-Trp-Glu-Trp-Thr-Trp-Tyr (IR820-C), was designed to self-assemble into nanoparticles for both potent PTT and vascular disruption in melanoma treatment. When co-assembled with the poorly soluble vascular disrupting agent (VDA) combretastatin A4 (CA4), the resulting nanoparticles (IR820-C@CA4 NPs) accumulate efficiently in tumors, activate systemic antitumor immune responses, and effectively ablate melanoma with a single treatment and near-infrared irradiation, as confirmed by our in vivo experiments. Furthermore, by exploiting the resulting tumor hypoxia, we subsequently administered the hypoxia-activated prodrug tirapazamine (TPZ) to capitalize on the created microenvironment, thereby boosting therapeutic efficacy and antimetastatic potential. This study showcases the potential of short-peptide-based nanocarriers for the design and development of stable and efficient photothermal platforms. The multifaceted therapeutic strategy, which merges photothermal ablation with vascular disruption and hypoxia-activated chemotherapy, holds great promise for advancing the efficacy and scope of cancer treatment modalities.

Downregulated expression of exosomal long non-coding RNA UCA1 in plasma of patients with non-small cell lung cance

Long non-coding RNAs (lncRNAs), mainly found in the nucleus of cells, are involved in different gene expression and regulation processes. Growing evidence has shown that abnormalities in the expression levels of lncRNAs play an important role in tumour metastasis, invasion, and proliferation, especially in non-small cell lung cancer (NSCLC). LncRNAs were also found to be upregulated or downregulated in exosomes secreted by cancer cells, thus they have the potential to be used as a non-invasive cancer biomarker. In this study, the expression of exosomal lncRNA UCA1 was determined on 29 plasma samples of patients with NSCLC and 29 controls who did not have NSCLC or related lung diseases by quantitative PCR using SYBR Green. Then, statistical analyses were used to evaluate the association between the expression of lncRNA UCA1 and some pathological features of NSCLC. The results showed that lncRNA UCA1 was downregulated in the exosomes of NSCLC patients compared with the control group (p=0.050), especially in the early stage (stage I + II). However, there was no association between the expression of lncRNA UCA1 and some pathological features of NSCLC, such as age, sex, smoking, alcohol consumption, tumour size, disease stage, lymph nodes, or tumour metastasis. This study provided the initial data on the expression of exosomal lncRNA UCA1 in the plasma of Vietnamese patients with NSCLC and further studies on the larger samples are needed to be continued.

Integrated microfluidic chip with a MIP-gated organic electrochemical transistor for continuous, selective, and label-free quantification of tumor marker

Early diagnosis of cancer is crucial to effective treatment of the patients and to higher survival rates. Tumor marker detection as an effective and noninvasive cancer diagnosis method has important clinical significance. N1, N12-diacetylspermine (DAS) is a new tumor marker. The development of its detection methods is an urgent need for medical diagnosis and human health monitoring. The conventional detection methods of DAS are labor-intensive and time-consuming approaches. In this work, a novel electrochemical detection platform was established by integrating an organic electrochemical transistor (OECT) with a microfluidic chip and applied for trace measurement of DAS. The sensing unit is a gate electrode in the OECT modified with molecularly imprinted polymers (MIPs), which can be used as an artificial receptor to realize label-free detection. The high specific recognition ability of MIPs films combined with the amplified function of an OECT yielded a highly sensitive and selective electrochemical detection chip. The integrated microfluidic channel controlledthe exact amount of solution to be delivered and provided real-time detection of DAS. The experimental results indicated that changes in channel current was proportional to DAS concentrations from 1 nM to 10 μM, with the detection limits of 0.8 nM. It is the first demonstration of the integration of a microfluidic system with a MIP-gated OECT for real-time tumor marker detection. The study results of this work helped to develop Point-Of-Care (POC) tools for the early diagnosis of lung cancer, colon cancer and breast cancer.

Effectiveness and Patient Experiences of Rhenium Skin Cancer Therapy for Nonmelanoma Skin Cancer: Interim Results from the EPIC-Skin Study.

Nonmelanoma skin cancer and its treatment represent a significant global cancer burden for health care systems and patients. Rhenium skin cancer therapy (Rhenium SCT) is a novel noninvasive radionuclide nonmelanoma skin cancer treatment, which can be provided in a single outpatient session. The aim of this prospective, multicenter, single-arm, international, phase IV study (EPIC-Skin) is to assess clinic- and patient-reported outcomes of Rhenium SCT as a treatment for basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Methods: Eligible patients had biopsy-proven stage I or stage II BCC or SCC lesions no more than 3 mm deep and no larger than 8 cm2 in area. Rhenium SCT resin was applied to an adhesive foil affixed to the target lesion in a single session. Interim efficacy and safety analysis were planned once 50% of target patients had recorded a 6-mo follow-up visit. Primary outcome is the proportion of lesions achieving complete response using modified RECIST. Secondary and other outcome measures include patient-reported quality of life (QoL), treatment comfort, and cosmesis. Results: A total of 182 patients was enrolled and administered Rhenium SCT (50 Gy dose to deepest point of target) to at least 1 BCC or SCC. Of 81 patients who reached the 6-mo posttreatment follow-up, it was found that 97.2% (103/106) of lesions showed complete responses and 2.8% (3/106) had partial responses. Improvements in QoL were also reported, whereas no patients reported any pain or discomfort during treatment. Adverse events were reported in 15.9% (29/182) of patients and were rated grade 1 (n = 19), grade 2 (n = 9), or grade 3 (n = 1). Conclusion: This preliminary analysis of the EPIC-Skin study indicates that Rhenium SCT is safe and effective for the treatment of BCC and SCC and is associated with significant QoL improvements. It will be particularly beneficial for lesions that are difficult to treat surgically because of size and location. It is also beneficial for patients with comorbidities or those unable to receive conventional fractionated radiotherapy.

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.

Tyndall, Rayleigh, Mei, and Raman scattering: Understanding their role in aesthetics.

The role of various light scattering phenomena in aesthetics and clinical practice is explored in this review. Four main types of light scattering, Tyndall, Rayleigh, Mie, and Raman, are discussed. Each type is explained in terms of its physical principles and its applications in aesthetic medicine. Tyndall scattering is relevant in understanding the blue appearance of certain dermal fillers. Rayleigh scattering contributes to skin tone perception and plays a role in certain laser treatments. Mie scattering is important in laser hair removal and the appearance of skin conditions like melasma. Raman scattering, while primarily used in research, shows promise for non-invasive skin analysis, personalized skincare, treatment monitoring, and early skin cancer detection. It is important to understand these scattering phenomena for optimizing light-based aesthetic procedures and developing effective treatments. Properly applying the appropriate scattering theory based on relative particle size is crucial in clinical aesthetic practice.

Exploring a Desirable Quadr-mRNAs Panel for Non-invasive and Ultrasensitive Bladder Cancer Diagnosis: In-silico and Clinical Studies.

Most patients with non-muscle invasive bladder cancer (NMIBC) have a high direction for recurrence and disease progression, which remains a significant unresolved challenge in bladder cancer patients. Therefore, a constant search is necessary for identifying appropriate and reliable biomarkers for early diagnosis of NMIBC. The current study has aimed to search for valuable diagnostic biomarkers in the tissue and urine specimens of NMIBC patients. The changes of twelve candidate mRNAs in a screening phase (40 tissue samples of NMIBC patients and their corresponding 40 urine specimens) and a subsequent independent validation phase (40 urine specimens) were estimated using real-time polymerase chain reaction (RT-qPCR). The receiver operating characteristic (ROC) analysis was executed to determine the potential diagnostic values of mRNAs. The mRNA levels of seven candidate genes were markedly higher in tissue specimens relative to their neighboring tissues. Among them, four mRNAs, including ERBB2, CCND1, MKI67, and MAGEA6, were differentially expressed in urine samples of NMIBC patients relative to control subjects. Further, the expression of these four mRNAs was validated in the validation step. Combining these biomarkers showed better diagnostic performance than single biomarkers in the urine sample for non-invasive NMIBC detection. The combination of these mRNAs and cytology enhanced the sensitivity of cytology from 37% to 87%. Our findings suggested that a four-mRNA panel may be promising in the non-invasive diagnosis of NMIBC, which deserves further investigation.

The Role of Biomarkers in HPV-Positive Head and Neck Squamous Cell Carcinoma: Towards Precision Medicine.

Head and neck cancer (HNC) represents a significant global health challenge, with squamous cell carcinomas (SCCs) accounting for approximately 90% of all HNC cases. These malignancies, collectively referred to as head and neck squamous cell carcinoma (HNSCC), originate from the mucosal epithelium lining the larynx, pharynx, and oral cavity. The primary risk factors associated with HNSCC in economically disadvantaged nations have been chronic alcohol consumption and tobacco use. However, in more affluent countries, the landscape of HNSCC has shifted with the identification of human papillomavirus (HPV) infection, particularly HPV-16, as a major risk factor, especially among nonsmokers. Understanding the evolving risk factors and the distinct biological behaviors of HPV-positive and HPV-negative HNSCC is critical for developing targeted treatment strategies and improving patient outcomes in this complex and diverse group of cancers. Accurate diagnosis of HPV-positive HNSCC is essential for developing a comprehensive model that integrates the molecular characteristics, immune microenvironment, and clinical outcomes. The aim of this comprehensive review was to summarize the current knowledge and advances in the identification of DNA, RNA, and protein biomarkers in bodily fluids and tissues that have introduced new possibilities for minimally or non-invasive cancer diagnosis, monitoring, and assessment of therapeutic responses.

A portable smartphone detection of ctDNA using MnB2 nanozyme and paper-based analytical device

The development of point-of-care testing (POCT) for circulating tumor DNA (ctDNA) is meaningful for the non-invasive cancers screening and diagnosis, particularly in resource-limited settings. The microfluidic paper-based analytical device (μPAD) provides an ideal platform, its application in ctDNA assays remains underexplored. In this work, a multifunctional μPAD was manufactured, which can enhance the efficiency and reduce the cost of ctDNA sensing. Additionally, a smartphone-based application analysis was fabricated for convenient, portable detection and colorimetric signal readout. Moreover, the novel oxidase-like MnB2 nanozyme was introduced in the sandwiches sensing strategy, utilizing its catalytic properties to effectively generate a colorimetric signal. The use of MnB2 nanozyme in sensing application is relatively novel, and its catalytic performance and mechanism was thoroughly evaluated via experiment and density functional theory (DFT) calculations. After optimizing the detection conditions, the proposed biosensor exhibited satisfactory results. Furthermore, the method was successfully used to detect ctDNA in tumor cell lysates and peripheral blood samples from tumor-bearing mice. The results were consistent with standard qPCR method, affirming the reliability of our POCT analysis device in ctDNA detection. Thus, this work not only provides a paper-based POCT device and intelligent analysis tool for portable cancers diagnosis, but it also paves a new application path for MnB2 nanozyme in the sensing filed.

Mixed Supervision of Histopathology Improves Prostate Cancer Classification From MRI.

Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=198 ) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).

Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome.

This study explores the use of cfDNA methylomes for the classification of lung cancer subtypes, vital for effective treatment. By identifying specific methylation markers in tumor tissues, we developed a robust classification model achieving high accuracy for noninvasive subtype detection. This cfDNA methylome approach offers promising avenues for early detection and monitoring.

Innovative Semi-Nested Realtime PCR Assay with Extendable Blocking Probe for Enhanced Analysis of SEPT9 Methylation in Colorectal Cancer.

(1) Background: The detection of methylated SEPT9 (mSEPT9) in plasma is a promising approach to non-invasive colorectal cancer (CRC) screening. Traditional approaches have limitations in sensitivity and cost-effectiveness, particularly in resource-limited settings. (2) Methods: We developed a semi-nested realtime PCR assay utilizing extendable blocking probes (ExBP) to enhance the detection of low-level mSEPT9 based on DNA melting. This assay allows for the discrimination of mSEPT9 in the presence of high concentrations of non-methylated SEPT9 (up to 100,000 times higher). (3) Results: The assay demonstrated a sensitivity of 73.91% and specificity of 80%, showcasing its ability to detect very low levels of methylated DNA effectively. The innovative use of ExBP without costly modified probes simplifies the assay setup and reduces the overall costs, enhancing its applicability in diverse clinical settings. (4) Conclusions: This novel assay significantly improves the detection of mSEPT9, offering a potential advance in CRC screening and monitoring. Its cost-efficiency and high sensitivity make it particularly suitable for the early detection and management of CRC, especially in settings with limited resources. Future studies are encouraged to validate this assay in larger populations to establish its clinical benefits and practical utility.

Canine Cancer Diagnostics by X-ray Diffraction of Claws.

We report the results of X-ray diffraction (XRD) measurements of the dogs' claws and show the feasibility of using this approach for early, non-invasive cancer detection. The obtained two-dimensional XRD patterns can be described by Fourier coefficients, which were calculated for the radial and circular (angular) directions. We analyzed these coefficients using the supervised learning algorithm, which implies optimization of the random forest classifier by using samples from the training group and following the calculation of mean cancer probability per patient for the blind dataset. The proposed algorithm achieved a balanced accuracy of 85% and ROC-AUC of 0.91 for a blind group of 68 dogs. The transition from samples to patients additionally improved the ROC-AUC by 10%. The best specificity and sensitivity values for 68 patients were 97.4% and 72.4%, respectively. We also found that the structural parameter (biomarker) most important for the diagnostics is the intermolecular distance.

A pathway for detection of gastric cancer biomarkers via using a layer-by-layer coated D-shaped grinding long-period fiber grating sensor

Gastric cancer significantly contributes to global cancer mortality, often leading to inoperable stages and high recurrence rates post-surgery. Elevated levels of G-17 and G-gly have been identified as potential risk factors, particularly in patients with duodenal ulcers. This study introduces an innovative D-shaped grinding long-period fiber grating sensor (D-LLPFGs) designed for non-invasive detection of the gastrin G-17 antigen, employing a layer-by-layer chemical self-assembly to bond G-17 antibodies onto the fiber surface through hydrogen bonding. The D-LLPFGs sensor demonstrated significant spectral shifts within 1 min of antigen-antibody interaction, highlighting its rapid detection capability. At an optimized antibody concentration of 4 μg/ml, antigen testing across different concentrations (10, 12.5, 20, 50 μg/ml) showed peak changes at 12.5 μg/ml antigen, with a 1.186 nm shift and 0.503 dB loss. The sensor exhibited a wavelength sensitivity of 0.095 nm/μg/ml, indicating its high sensitivity and potential in gastric cancer classification, diagnosis, and treatment. This research concludes that the D-shaped fiber sensor is an effective and sensitive tool for detecting G-17 antigen levels, presenting a significant advancement in non-invasive gastric cancer diagnosis. Its quick response time and high sensitivity highlight its potential for broad biomedical applications, offering a new avenue for early cancer detection and improving patient prognosis. The success of this study opens the door to further exploration and implementation of fiber optic sensors in clinical settings, marking a significant step forward in the fight against gastric cancer.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

The Role of Semaphorin 6D (Sema6D) in Non-Muscle-Invasive Bladder Cancer-A Preliminary Study on Human Plasma and Urine.

The incidence of bladder cancer worldwide in the last three decades has been increasing in both men and women. So far, there is no established non-invasive bladder cancer biomarker in daily clinical practice. Semaphorin 6D (sema6D) is a transmembrane protein that belongs to the class VI semaphorins. The aim of this study was to evaluate for the first time the potential role of sema6D in bladder cancer. The study group consisted of 40 patients with non-muscle-invasive bladder cancer (NMIBC) and the control group of 20 patients without malignancies. There was a statistically significantly higher urinary sema6D concentration in patients than controls (p < 0.05) but no significant difference in plasma 6D. There were no statistically significant differences in urinary or plasma concentration of sema6D between low- or high-grade cancer and according to the tumor stage in TNM classification. There was a statistically significant negative correlation between plasma sema6D and age of patients (R = -0.6; p = 0.019). Plasma sema6D does not seem to be useful in the clinical practice at this point. However, the urinary sema6D concentration could potentially serve as a marker of NMIBC used for diagnostic purposes, monitoring, and early relapse detection or the assessment of the treatment efficacy. Urinary sema6D is probably not associated with the grading or staging of NMIBC, so it cannot be used for the prediction of disease prognosis.