Unstained Cells Research Articles

Quantum Weak Value Enabled Quantitative Phase Imaging

AbstractQuantitative phase imaging (QPI) has been a powerful approach in bioimaging because it can protect specimens from staining and quantify important physical properties of biological specimen, such as local thickness and refractive index. Here, a simple mechanism is proposed to implement QPI based on the quantum weak‐value theory. Two weak values are employed. One is momentum weak value, which inherently gives the phase gradient of the cells and tissues, and the other is polarization weak value leading to the biased imaging. Following the essence of weak value amplification in precision metrology, the weak value can also enhance the contrast in imaging. By combining two converse bias images and a phase gradient intensity image to capture phase gradient data, the original phase of the specimen can be quantitatively reconstructed. A proof‐of‐principle experiment is conducted by imaging the phase targets and unstained Paramecium cells. The ability to capture quantitative phase data is validated through imaging various samples with a phase resolution, demonstrating height deviations of less than 10 nm compared to atomic force microscopy (AFM). This work proposes weak‐value‐based QPI that possess the potential for improving the measurement precision and provides deep insights into physics and applications of weak value.

Morphology Classification of Live Unstained Human Sperm Using Ensemble Deep Learning

Sperm morphology analysis is crucial in infertility diagnosis and treatment. However, current clinical analytical methods use either chemical stains that render cells unusable for treatment or rely on subjective manual inspection. Here, an ensemble deep‐learning model is presented for classification of live, unstained human sperm using whole‐cell morphology. This model achieves an accuracy and precision of 94% benchmarked against the consensus of three andrology scientists who classified the images independently. The model loses less than a 12% prediction performance even when image resolution is reduced by over sixfold. This ensures compatibility across varied clinical imaging setups. This model also provides a high certainty and robust classification of challenging images, which divided the experts. By providing a consistent, automated approach for classifying live, unstained cells using quantitative data, this model offers promising future opportunities for enhancing clinical sperm selection practices and reducing day‐to‐day variability in clinics.

P-037 Deep learning for morphology classification of live stain-free human sperm

Abstract Study question Can a deep-learning algorithm classify live, stain-free human sperm cells based on the whole cell morphology in real-time? Summary answer Our model predicts sperm morphology with 94% precision and accuracy, maintaining performance despite over six-fold image quality reduction, demonstrating less than a 12% accuracy drop. What is known already Sperm morphology analysis plays a crucial role in infertility diagnosis and treatment. However, current clinical methods either use diagnostic staining, rendering sperm cells unsuitable for treatment or depend on subjective manual examination. While deep learning has presented new opportunities for morphology analysis, previous studies only evaluate sperm head morphology, neglecting other important morphological characteristics of sperm midpiece and tail. Additionally, these algorithms rely on fixed and stained images of sperm, preventing their applicability for the classification of live, unstained sperm cells for use in treatment cycles. Study design, size, duration Donor human semen samples (with a concentration range of 30-80 million/mL) comprising 8 technical replicates sourced from 6 biological replicates, were used to generate a dataset of 2254 stain-free single sperm images. Subsequently, three expert clinicians from two different fertility clinics labelled the images according to their morphological status (assessing head, midpiece, and tail morphology) as the ground truth for training our deep-learning model. Model development, training, testing, and analysis were then performed. Participants/materials, setting, methods Using the YOLOv3 algorithm, 2254 single sperm images were automatically extracted from 1000× magnification microscopy images. Three expert andrology scientists from Monash IVF classified and labelled these images based on the WHO morphological characteristics of the sperm head, midpiece, and tail. Then our optimized ensemble structure was trained using 80% of the images for morphology classification of the whole cell. Testing on the remaining unseen images was conducted to assess accuracy, precision, and processing time. Main results and the role of chance While our clinical labelling results highlighted the subjective nature of current manual morphology analysis practices in clinics, with only 16% agreement between experts, our model classifies human sperm images based on their whole morphology status with an accuracy and precision of 94% in real-time (less than a second per image). Testing our model across multiple image resolutions (800 × 800, 224 × 224, 192 × 192, and 128 × 128 pixels) revealed consistent performance, with less than a 12% variation. This underscores the robustness and compatibility of our model across various clinical imaging settings. Importantly, for more challenging images with 2-out-of-3 experts’ agreement (53% of the images) or images without an agreement between experts (25% of the images), our model consistently classified 96% and 97% of the images with at least 75% confidence, respectively. This demonstrates the performance of the model in classifying sperm images that pose difficulties for the experts to label. Consistency in operation and informing sperm selection with quantitative metrics through this platform offers promising opportunities to reduce day-to-day variability in fertility clinics when analysing or selecting sperm. Limitations, reasons for caution This study is confined to samples and clinics exclusively located in Australia, while a larger number of samples across multiple clinics could enhance the generalizability of findings. Additionally, real-time clinical implications and long-term performance evaluation are still needed. Wider implications of the findings This platform not only provides automated evaluation of live sperm as part of infertility diagnostics but also facilitates selection of the most morphologically normal sperm for treatment cycles, particularly in the context of Intracytoplasmic Sperm Injection (ICSI). Therefore, it can enhance positive long-term treatment outcomes by prioritizing normal sperm selection. Trial registration number 2017370

Phenotypic characterisation of bovine alveolar macrophages reveals two major subsets with differential expression of CD163

Bovine alveolar macrophages (AMs) defend the lungs against pathogens such as Mycobacterium bovis (M. bovis), the causative agent of bovine tuberculosis. However, little is known about the surface molecules expressed by bovine AMs and whether there is heterogeneity within the population. The purpose of this study was to characterise the bovine AM cell surface phenotype using flow cytometry. Bronchoalveolar lavage samples from four different calves were stained with a combination of antibodies against immune cell molecules prior to flow cytometric analysis. To assess the degree of expression, we considered the distribution and relative intensities of stained and unstained cells. We demonstrated that bovine AMs have high expression of CD172a, ADGRE1, CD206, and CD14, moderate expression of CD80, MHC II, CD1b, and CD40, low expression of CX3CR1 and CD86, and little or no expression of CD16 and CD26. Two distinct subsets of bovine AMs were identified based on CD163 expression. Subsequent analysis showed that the CD163+ subset had greater expression of other typical macrophage molecules compared to the CD163- subset, suggesting that these cells may perform different roles during infection. The characterisation of the uninfected bovine AM phenotype will provide a foundation for the examination of M. bovis-infected AMs.

Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra.

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as "autofluorescence signatures" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology “fingerprints” extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Single-shot differential phase contrast microscopy using ring-shaped polarisation multiplexing illumination.

We propose a differential phase contrast microscopy that enables single-shot phase imaging for unstained biological samples. The proposed approach employs a ring-shaped LED array for polarisation multiplexing illumination and a polarisation camera for image acquisition. As such, multiple images of different polarisation angles can be simultaneously captured with a single shot. Through polarisation demultiplexing, the sample phase can therefore be recovered from the single-shot measurement. Both simulations and experiments demonstrate the effectiveness of the approach. We also demonstrate that ring-shaped illumination enables higher contrast and lower-distortion imaging results than disk-shaped illumination does. The proposed single-shot approach potentially enables phase contrast imaging for live cell samples in vitro. Lay Description: We propose a microscopy that enables imaging of transparent samples, unstained cells, etc. We demonstrate that the proposed method enables higher contrast and lower-distortion imaging results than conventional methods, and significantly improves imaging efficiency. The proposed method potentially enables dynamic imaging for live cell samples in vitro.

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.

Effects of Rumen-Protected L-Tryptophan Supplementation on Productivity, Physiological Indicators, Blood Profiles, and Heat Shock Protein Gene Expression in Lactating Holstein Cows under Heat Stress Conditions.

In this study, we examined the effects of rumen-protected L-tryptophan supplementation on the productivity and physiological metabolic indicators in lactating Holstein cows under heat stress conditions. The study involved eight early lactating Holstein cows (days in milk = 40 ± 9 days; milk yield 30 ± 1.5 kg/day; parity 1.09 ± 0.05, p < 0.05), four cows per experiment, with environmentally controlled chambers. In each experiment, two distinct heat stress conditions were created: a low-temperature and low-humidity (LTLH) condition at 25 °C with 35-50% humidity and a high-temperature and high-humidity (HTHH) condition at 31 °C with 80-95% humidity. During the adaptation phase, the cows were subjected to LTLH and HTHH conditions for 3 days. This was followed by a 4-day heat stress phase and then by a 7-day phase of heat stress, which were complemented by supplementation with rumen-protected L-tryptophan (ACT). The findings revealed that supplementation with ACT increased dry matter intake as well as milk yield and protein and decreased water intake, heart rate, and rectal temperature in the HTHH group (p < 0.05). For plateletcrit (PCT, p = 0.0600), the eosinophil percentage (EOS, p = 0.0880) showed a tendency to be lower, while the monocyte (MONO) and large unstained cells (LUC) amounts were increased in both groups (p < 0.05). Albumin and glucose levels were lower in the HTHH group (p < 0.05). The gene expressions of heat shock proteins 70 and 90 in the peripheral blood mononuclear cells were higher in the ACT group (HTHH, p < 0.05). These results suggest that ACT supplementation improved productivity, physiological indicators, blood characteristics, and gene expression in the peripheral blood mononuclear cells of early lactating Holstein cows under heat-stress conditions. In particular, ACT supplementation objectively relieved stress in these animals, suggesting that L-tryptophan has potential as a viable solution for combating heat-stress-induced effects on the cattle in dairy farming.

Is large unstained cell percentage (%LUC) a novel diagnostic marker for periprosthetic joint infections?

BACKROUND: Periprosthetic joint infection (PJI) is a very important complication that occurs after total joint replacement. Diagnosing PJI is at least as difficult as this disease. There is no biomarker that alone can diagnose PJI. OBJECTIVE: This study aimed to evaluate the effectiveness of large unstained cell percentage (%LUC) in diagnosing PJI and deciding on re-implantation in two stage septic exchange arthroplasty. METHODS: Patients who underwent revision arthroplasty between 2019 and 2023 were screened retrospectively. These patients were grouped as those who underwent two-stage septic exchange and those who underwent aseptic exchange. Prosthesis removal from the patients who underwent two-stage septic exchange, before spacer application, and on the 3rd post-operative day after spacer application, Blood parameters were collected at the 1st month and before the second stage after the spacer application, and before the revision surgery from the patients who underwent aseptic exchange. White blood cell, neutrophil percentage, %LUC, albumin, sedim and CRP values were checked and recorded one by one from all patients. RESULTS: The data of a total of 233 patients, including 133 patients in the two stage septic exchange group and 100 patients in the aseptic exchange group, were included in the study. When the predictive value of %LUC in PJI was accepted as cut-off 1.75, the sensitivity was 69.2% and the specificity was 73%. The change in %LUC over time in patients who underwent two-stage septic exchange was statistically significant (p= 0.0001). A positive correlation was found between the value of .%LUC after spacer application and the value of CRP before prosthesis protrusion/spacer application surgery, and the Spearman correlation coefficient was found to be 0.005. CONCLUSION: %LUC value can be a promising biomarker for the diagnosis of PJI by considering both sensitivity and specificity rates. Apart from this, it is an easily accessible and effective biomarker for re-implantation decision making and evaluation of response to treatment, especially in two-stage septic exchange surgeries.

Enhancing extracellular electron transfer and power generation in microbial fuel cell using a ferrocene-based conjugated oligoelectrolyte

The anode compartment, as an electron generation station, determines the power performance and contaminant removal in microbial fuel cell (MFC). However, the extracellular electron transfer (EET) process limits bioelectricity generation. In this work, the role of conjugated oligoelectrolytes (COEs) on catalyzing bacterial activity toward bio-current production was investigated. In this regard, two COEs, namely [4-[1-(3-methylimidazolium chloride)]butyl] ferrocene (MIF+) and Bis [4-[1-(3-methylimidazolium chloride)]butyl] ferrocene (BMIF+) were synthesized and the increases in anodic current output were probed through MFC device. The electrochemical analyses revealed excellent performance in bio-current production for BMIF+ stained cells. The tolerance threshold of COEs stained cells for different concentrations of COEs was investigated using chronoamperometry (CA) test. Additionally, the power performance of MFC exhibited the highest value of 115.46 mW m−2 for the BMIF+ stained cells in comparison with MIF+ stained cells (73.12 mW m−2) and unstained cells (25.8 mW m−2). The results demonstrated that spiking cells' membranes with COEs is a meaningful strategy to facilitate the EET process and produce bioelectricity.

A-144 Three-wavelength Polarization Imaging Method for the Analysis of Individual Human Blood Cells With Dye-Enhanced Dispersion of Dilution Buffer

Abstract Background Performing basic hematology analysis in a near-patient setting enables more rapid clinical decision making in the process of diagnosis. Traditional blood analyzers in central labs use a complex combination of sensing principles and chemical modification of the sample to derive the necessary parameters for a complete blood count (CBC). To reduce cost and maintenance, varying approaches have been employed to determine a CBC with a single-step measurement technique. One common option is the use of optical microscopy to deliver highly reliable results, examples are fluorescence microscopy, digital holographic microscopy, or spatial light interference microscopy. However, most of these technologies lack a proper determination of cellular-level RBC parameters. Therefore, a non-interferometric, quantitative-phase measurement technique was developed to derive relevant CBC parameters from a single measurement, comparable to a standard lab analyzer. Methods The optical setup employed by this method enables the parallel acquisition of brightfield and differential interference contrast (DIC) images by polarization-sensitive cameras. By using three cameras with individual optical filters, quantitative phase and absorption information can be calculated, which enables the determination of mean cellular volume (MCV), mean cellular hemoglobin (MCH), and mean cellular hemoglobin concentration (MCHC) for red blood cells (RBCs). The whole-blood sample is diluted in a buffer containing a non-cell-penetrating dye to enhance optical refraction contrast and therefore precision in the dependent calculated quantities. The sample is then introduced to a glass flow cell, and the cells are allowed to sediment to the bottom for imaging. Machine learning is applied to the derived images to classify unstained native white blood cells (WBCs) to yield a 5-part differential, which distinguishes between neutrophil, monocyte, lymphocyte, eosinophil and basophil. The algorithm is trained by imaging FACS-validated, purified WBC subgroups. All blood samples are measured on an ADVIA® 2120 Hematology System for control. Results A one-step, optical CBC hematology system based on quantitative phase and absorption image analysis was developed. The correlations for MCV and MCHC compared to a standard lab CBC analyzer were 0.77 and 0.84. The accuracy for the 5-part differential WBC classification was approximately 0.8. To enable a visual inspection of the classification results, a pseudo-colored image of each cell was generated and uploaded in a review tool. Conclusion With a more-refined, less-expensive optical setup, a high-quality CBC analyzer suitable for use at the point of care can be derived with the presented method. A possible simplification could be the use of low-cost but high-resolution Fourier ptychography microscopy (FPM) technology. Imaging of blood samples on a single-cell level offers the potential for the analysis of even more morphological parameters, for example, disease-related changes in the cell membrane. Also, the adhesion behavior of the WBCs in the glass flow cell could indicate cell activation status and therefore give information about infection processes. *Trademark disclaimer: “ADVIA, and all associated marks are trademarks of Siemens Healthcare Diagnostics, Inc. or its affiliates. All other trademarks and brands are the property of their respective owners.”

Large unstained cells count is a useful predictor of coronary artery disease co-existence in patients with severe aortic stenosis.

Large unstained cells count is a useful predictor of coronary artery disease co-existence in patients with severe aortic stenosis.

Effects of monolaurin on intestinal barrier, blood biochemical profile, immunity and antioxidant function in porcine epidemic diarrhoea virus-infected piglets.

The effects of monolaurin (ML) on the health of piglets infected with porcine epidemic diarrhoea virus (PEDV) have not been fully understood. This study aimed to investigate its role in blood biochemical profile, intestinal barrier function, antioxidant function and the expression of antiviral genes in piglets infected with PEDV. Thirty-two piglets were randomly divided into four groups: control group, ML group, PEDV group and ML + PEDV group. Piglets were orally administrated with ML at a dose of 100 mg/kg·BW for 7 d before PEDV infection. Results showed that PEDV infection significantly decreased D-xylose content and increased intestinal fatty acid-binding protein content, indicating that PEDV infection destroyed intestinal barrier and absorption function. While it could be repaired by ML administration. Moreover, ML administration significantly decreased plasma blood urea nitrogen and total protein content upon PEDV infection. These results suggested ML may increase protein utilisation efficiency. ML administration significantly decreased the number of large unstained cells and Hb and increased the number of leucocytes and eosinophils in the blood of PEDV-infected piglets, indicating ML could improve the immune defense function of the body. In the presence of PEDV infection, ML administration significantly increased superoxide dismutase and catalase activities in blood and colon, respectively, indicating ML could improve antioxidant capacity. Besides, ML administration reversed the expression of ISG15, IFIT3 and IL-29 throughout the small intestine and Mx1 in jejunum and ileum, indicating the body was in recovery from PEDV infection. This study suggests that ML could be used as a kind of feed additive to promote swine health upon PEDV infection.

High molecular risk variants, severe thrombocytopenia and large unstained cells count affect the outcome in primary myelofibrosis

Apart from the driver mutations, high molecular risk (HMR) variants and other factors have been reported to influence the prognosis of primary myelofibrosis (PMF). The aim of our study was to investigate the impact of laboratory and molecular characteristics at the time of diagnosis (TOD) on the PMF outcome. The study group consisted of 82 patients recruited from three Polish university centers. Among the driver mutations, only CALR type 1 positively influenced the overall survival (OS). The risk of progression to accelerated or blastic disease phase (AP/BP) did not depend on the driver mutation type, but was closely associated with the presence of HMR variants (p = 0.0062). The risk of death (ROD) was higher in patients with HMR variants (OR[95%CI] = 4.33[1.52;12.34], p = 0.0044) and in patients with a platelet count at the TOD between 50–100 G/L (HR[95%CI] = 2.66[1.11;6.35]) and < 50 G/L (HR[95%CI] = 8.44[2.50;28.44]). Median survival time was 7.8, 2.2 and 1.4 years in patients with large unstained cells (LUC) count of [0.0–0.2], (0.2–0.4] and > 0.4 G/L at the TOD, respectively. We found an unexpected, hitherto undescribed, association between LUC count at the TOD and PMF prognosis. Our analysis led to the following conclusions: in PMF patients at the TOD 1) the presence of HMR variants, especially combined, is associated with an increased risk of progression to the AP and BP, and shorter OS, 2) severe thrombocytopenia confers worse prognosis than the moderate one, 3) LUC count is closely related with the disease phase, and associated with the ROD and OS.

Comparative study of SSNHL with and without tinnitus: audiologic and hematologic differences

Background and Objectives Tinnitus is one of the most common symptoms of sudden sensorineural hearing loss (SSNHL), with the incidence of tinnitus in patients with SSNHL ranging from 60% to 90%. Little is known, however, about the specific audiologic and hematologic factors that may be associated with the development of tinnitus. To better understand the relationship between tinnitus and SSNHL, the present study compared audiologic and hematologic factors in SSNHL patients with tinnitus and without tinnitus. Subjects and Method The present study compared 120 patients with SSNHL with tinnitus and 59 patients with SSNHL without tinnitus at their initial examination. Their audiology and hematologic test results were analyzed, and hearing recovery was determined by comparing the hearing thresholds before and after treatment. Results 120 patients with tinnitus showed longer III and V latency in auditory brainstem response (ABR) tests, lower signal-to-noise ratios (SNR) at 2 kHz in transient evoked otoacoustic emissions (TEOAE) tests, and lower response rates at 2 kHz in distortion product otoacoustic emissions (DPOAE) tests of the affected ear (p < 0.05 each) than the 59 patients without tinnitus. However, there were no significant between-group differences in the mean hearing threshold and hearing recovery rate of the affected ear. Patients with tinnitus had significantly worse mean hearing thresholds and hearing thresholds at 4 kHz in the nonaffected ear. The percentages of monocytes and large unstained cells (%LUCs) were higher in the group without tinnitus (p < 0.05), although there were no significant between-group differences in inflammatory markers, such as neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR) and platelet-to-lymphocyte ratio (PLR). Conclusion Tinnitus accompanying SSNHL may be associated with baseline hearing level, as well as being an indicator of damage to outer hair cells and auditory nerves. Additional studies are needed to evaluate hematologic data in SSNHL patients with and without tinnitus.

The usefulness of hematological parameters and cerebrospinal fluid indexes in the differential diagnosis of acute bacterial from viral meningitis

Central nervous system (CNS) infection is a medical emergency with an important cause of mortality worldwide. The 79 patients with confirmed acute CNS infection (48 bacterial and 31 viral meningitis) were evaluated. Bacterial meningitis score, cerebrospinal fluid (CSF)/serum glucose ratio, and CSF/serum albumin ratio had the highest area under the curves (0.873, 0.843, 0.810, respectively) for discriminating bacterial meningitis. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte ratio (PLR) and CSF lactate dehydrogenase have a good ability for the differential diagnosis of bacterial meningitis. CSF/serum glucose ratio, NLR (with a cut-off value> 8.87), large unstained cell, total protein, albumin, and procalcitonin levels were found to be predictors for mortality. NLR can be used as a biomarker to differentiate bacterial meningitis from viral meningitis and to predict the prognosis of CNS infection. CSF/serum albumin ratio and CSF lactate dehydrogenase can be used to predict bacterial meningitis as well as CSF/serum glucose ratio.

Adaptive phase contrast microscopy to compensate for the meniscus effect

Phase contrast is one of the most important microscopic methods for making visible transparent, unstained cells. Cell cultures are often cultivated in microtiter plates, consisting of several cylindrical wells. The surface tension of the culture medium forms a liquid lens within the well, causing phase contrast conditions to fail in the more curved edge areas, preventing cell observation. Adaptive phase contrast microscopy is a method to strongly increase the observable area by optically compensating for the meniscus effect. The microscope’s condenser annulus is replaced by a transmissive LCD to allow dynamic changes. A deformable, liquid-filled prism is placed in the illumination path. The prism’s surface angle is adaptively inclined to refract transmitted light so that the tangential angle of the liquid lens can be compensated. Besides the observation of the phase contrast image, a beam splitter allows to simultaneously view condenser annulus and phase ring displacement. Algorithms analyze the displacement to dynamically adjust the LCD and prism to guarantee phase contrast conditions. Experiments show a significant increase in observable area, especially for small well sizes. For 96-well-plates, more than twelve times the area can be examined under phase contrast conditions instead of standard phase contrast microscopy.

Laser transfer for circulating tumor cell isolation in liquid biopsy.

75Cancer research has found in the recent years a formidable ally in liquid biopsy, a noninvasive technique that allows the study of circulating tumor cells (CTCs) and biomolecules involved in the dynamics of cancer spread like cell-free nucleid acids or tumor-derived extracellular vesicles. However, single-cell isolation of CTCs with high viability for further genetic, phenotypic, and morphological characterization remains a challenge. We present a new approach for single CTC isolation in enriched blood samples using a liquid laser transfer (LLT) process, adapted from standard laser direct write techniques. In order to completely preserve the cells from direct laser irradiation, we used an ultraviolet laser to produce a blister-actuated laser-induced forward transfer process (BA-LIFT). Using a plasma-treated polyimide layer for blister generation, we completely shield the sample from the incident laser beam. The optical transparency of the polyimide allows direct cell targeting using a simplified optical setup, in which the laser irradiation module, standard imaging, and fluorescence imaging share a common optical path. Peripheral blood mononuclear cells (PBMCs) were identified by fluorescent markers, while target cancer cells remained unstained. As a proof of concept, we were able to isolate single MDA-MB-231 cancer cells using this negative selection process. Unstained target cells were isolated and culture while their DNA was sent for single-cell sequencing (SCS). Our approach appears to be an effective approach to isolate single CTCs, preserving cell characteristics in terms of cell viability and potential for further SCS.

Large unstained cell (LUC) count as a predictor of carotid artery occlusion.

Carotid artery stenosis is often considered a stable clinical condition, and the underlying atherosclerosis is thought to have an inflammatory background. The aim of the study was to assess the value of different parameters obtained from whole blood counts for the prediction of advanced carotid artery atherosclerosis, including vessel occlusion, irrespective of symptom occurrence. The study group comprised 290 patients (84 (29%) females and 206 (71%) males) with a mean age of 68 ±8 years, who were admitted to the Vascular Surgery Department due to significant carotid artery disease. Patients were retrospectively divided into 2 subgroups regarding the presence or absence of artery occlusion. The demographic, clinical and laboratory preoperative data were compared between both groups. We found significant differences in preoperative large unstained cell (LUC) counts between patients with and without carotid artery occlusion (p = 0.003), when analyzed with the Mann-Whitney test for independent samples. The receiver operating characteristic (ROC) curve showed that LUC count has prognostic properties for carotid artery occlusion, with an area under the curve (AUC) of 0.637 (p = 0.033), yielding a 69.70% sensitivity and a 51.75% specificity. Large unstained cells represent an acute inflammatory state related to artery occlusion. An LUC count below the cutoff value of 0.16×109/L may be a predictor of carotid artery occlusion. Therefore, carotid artery occlusion should not be regarded as a chronic state, but as a clinical challenge being promoted by active inflammatory processes.