Mehmet Kahraman

In this study, we used flexible and inexpensive diatomite/AgNPs strips and Layer‐by‐Layer (LbL) assembly to develop sandwich assays for the detection of cancer biomarkers of interest via Surface‐enhanced Raman scattering (SERS). The modified SERS active strips were used to capture antigens of interest, incubate Raman probes, and generate SERS maps based on specific Raman peaks of the Raman probes. Simultaneous detection and quantification of multiple biomarkers, including HER2, CA15‐3, CA27‐29, PSA, and MUC4, were demonstrated by the immobilization of specific antibodies and the use of uniquely labeled Raman probes. Spatially resolved and co‐immobilized multiplexing strategies were developed and successfully validated, showing high specificity and sensitivity with negligible background interference from serum components. The flexible and cost‐effective nature of the diatomite/AgNPs substrate, combined with its strong SERS enhancement, offers significant advantages over traditional immunoassay techniques.

Detection of microbial contamination is essential for hygiene monitoring in food production, healthcare, and environmental settings. While ATP-based bioluminescence assays are widely used for assessing cleanliness, commercial luminometers are costly, require large sample volumes, are non-specific for bacteria, and exhibit low dynamic range that limit their use. We report here a compact biosensing platform for sensitive ATP quantification and species-selective detection of Escherichia coli by integrating SuperSnap ATP chemistry and MicroSnap E. coli enzyme-specific assays. We use a backside-illuminated CMOS image sensor operated in contact-mode, allowing efficient detection of chemiluminescence from sample volumes as low as 10 µL. ATP measurements in SuperSnap buffer exhibit strong linearity and good agreement with commercial luminometers both at low and high concentration ranges, with limits of detection down to 3.51 nM ATP. When normalized to total ATP amount, the CMOS-based platform achieves sensitivity close to the luminometer, despite 30-fold smaller sample volume. Importantly, while the luminometer response saturates at ~75 nM ATP, the CMOS-based system maintains quantification up to 200 nM, demonstrating significantly larger dynamic range, which is a key practical advantage for real-world hygiene monitoring. Species-selective detection of E. coli was demonstrated using MicroSnap chemistry, achieving a detection limit of approximately 412 CFU mL⁻¹ following a 6-hour enrichment and a 10-minute detection step. Selectivity was preserved in mixed bacterial samples containing Staphylococcus epidermidis, confirming enzyme-mediated specificity. These results establish CMOS-based bioluminescent biosensing as a low-cost, portable, and versatile tool for rapid hygiene monitoring and targeted microbial detection at the point-of-need.

Cancer is one of the most actively researched diseases having a high mortality rate when not detected at an early stage. Thus, rapid, simultaneous, and sensitive quantification of cancer biomarkers plays an important role in early diagnosis, with patient impact to disability adjusted life years. Herein, a diatomite-based SERS flexible platform for the rapid and sensitive detection of circulating cancer-specific protein biomarkers in serum is presented. In this approach, diatomite/AgNPs strips with maximum SERS activity prepared using the layer-by-layer (LbL) technique were modified with specific antibodies, and specific antigens (HER2, CA15-3, PSA, and MUC4) were captured and detected. By using Raman probes specific to the captured antigens in serum, a SERS limit of detection (LOD) of 0.1 ng/mL was measured (calculated LOD < 0.1 ng/mL). This value is lower than the cutoff amount of cancer antigens in the person's blood. The specificity for the antigens of each antibody was calculated to be higher than 95%. As a result, an immunosensor for rapid detection of cancer biomarkers in serum with good specificity, high sensitivity, good reproducibility, and low cost has been demonstrated. Overall, we show that the prepared diatomite-based SERS substrate with a high surface-to-volume ratio is a useable platform for immunoassay tests.

Surface-enhanced Raman scattering (SERS) is an emerging spectroscopy technique for detecting and characterizing chemical or biological structures in the vicinity of plasmonic nanostructures. Colloidal, solid, and flexible nanostructures are widely used in SERS experiments to enhance the Raman intensity. The nanostructure used in SERS is one of the main influencing parameters and a growing research area. Fabrication of simple and cheap SERS substrates with a high enhancement factor is desired. In this study, we fabricated a reproducible, cheap, and flexible SERS active strip by coating natural diatomite (biosilica) with silver nanoparticles (AgNPs) using the layer-by-layer assembly method and the fabricated strip is used for the label-free identification of circulating cancer protein biomarkers. SERS active strips were fabricated having different numbers of AgNP layers on natural diatomite and comprehensive characterization techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and UV/vis absorption spectrophotometry were used. SERS activities of the strips depending on the number of layers were evaluated using 4-aminothiophenol (4-ATP) and rhodamine 6G (Rh6G) molecules. We found that the SERS intensity is strongly dependent on the number of AgNP layers, with the maximum SERS intensity obtained from the strip with 5 layers of AgNPs, having a 2.0 × 10⁵ enhancement factor. The strip with the highest SERS activity was used for the label-free identification of circulating cancer protein biomarkers (HER2, CA15-3, PSA, MUC4, and CA27-29). The results demonstrate that the fabricated strip can help in the effective label-free identification of circulating protein biomarkers and open new directions for SERS-based label-free biosensing applications.

Real-time polymerase chain reaction,atomic force microscopy,microbiology,surface roughness,low-friction elastomeric ligatures

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.