A sample-preparation-free, automated, sample-to-answer system for cell counting in human body fluids

Evaluation of the Microscanner C3 for Automated Cell Counting in Cerebrospinal Fluid Analysis

Background: Cerebrospinal fluid (CSF) analysis is essential for diagnosing various disorders affecting the central nervous system (CNS). Traditionally, CSF cell count analysis is performed manually using a Neubauer chamber hemocytometer, which is labor-intensive and prone to subjective interpretation. Methods: In this study, we evaluated the analytical and clinical performance of the Microscanner C3, an automated cell counting system, for CSF analysis using artificially prepared samples and 150 clinical CSF samples. Results: The lowest detectable white blood cell (WBC) count was 3.33 cells/µL, and the lowest detectable red blood cell (RBC) count was 3.67 cells/µL. The coefficients of variation (CV%) for the Microscanner C3 were lower than those for the Neubauer chamber at all cell concentrations. The correlation coefficients (R) between the Microscanner C3 and conventional methods were high: 0.9377 for WBCs and 0.9952 for RBCs when compared with the Neubauer chamber, and 0.8782 for WBCs and 0.9759 for RBCs when compared with the flow cytometer. Additionally, the Microscanner C3 showed good agreement with both the Neubauer chamber and flow cytometer in the Passing-Bablok regression analysis and Bland-Altman analysis for WBC count at all concentrations and RBC count at concentrations of 0-1000 cells/µL. Conclusions: The Microscanner C3 proved to be more sensitive, precise, and consistent compared to the conventional hemocytometer. The new system is also compact, convenient, and cost-effective, making it a valuable option for clinical laboratories.

A smartphone-based diagnostic analyzer for point-of-care milk somatic cell counting

BACKGROUND

Mastitis, a pervasive and detrimental disease in dairy farming, poses a significant challenge to the global dairy industry. Monitoring the milk somatic cell count (SCC) is vital for assessing the incidence of mastitis and the quality of raw cow's milk. However, existing SCC detection methods typically require large-scale instruments and specialized operators, limiting their application in resource-constrained settings such as dairy farms and small-scale labs. To address these limitations, this study introduces a novel, smartphone-based, on-site SCC testing method that leverages smartphone capabilities for milk somatic cell identification and enumeration, offering a portable and user-friendly testing platform.

RESULTS

The central findings of our study demonstrate the effectiveness of the proposed method for counting milk somatic cells. Its on-site applicability, facilitated by the microfluidic chip, optical system, and smartphone integration, heralds a paradigm shift in point-of-care testing (POCT) for dairy farms and smaller laboratories. This approach bypasses complex processing and presents a user-friendly solution for real-time SCC monitoring in resource-limited settings. This device boasts several unique features: small size, low cost (<$1,000 total manufacturing cost and <$1 per test), and high accuracy. Remarkably, it delivers test results within just 2 min. Actual-sample testing confirmed its consistency with results from the commercial Bentley FTS/FCM cytometer, affirming the reliability of the proposed method. Overall, these results underscore the potential for transformative change in dairy farm management and laboratory testing practices.

SIGNIFICANCE

In summary, this study concludes that the proposed smartphone-based method significantly contributes to the accessibility and ease of SCC testing in resource-limited environments. By fostering the use of POCT technology in food safety control, particularly in the dairy industry, this innovative approach has the potential to revolutionize the monitoring and management of mastitis, ultimately benefiting the global dairy sector.

A sample-preparation-free, point-of-care testing system for in situ detection of bovine mastitis

We present a highly integrated point-of-care testing (POCT) device capable of immediately and accurately screening bovine mastitis infection based on somatic cell counting (SCC). The system primarily consists of a homemade cell-counting chamber and a miniature fluorescent microscope. The cell-counting chamber is pre-embedded with acridine orange (AO) in advance, which is simple and practical. And then SCC is directly identified by microscopic imaging analysis to evaluate the bovine mastitis infection. Only 4 μL of raw bovine milk is required for a simple sample testing and accurate SCC. The entire assay process from sampling to result in presentation is completed quickly within 6 min, enabling instant "sample-in and answer-out." Under laboratory conditions, we mixed bovine leukocyte suspension with whole milk and achieved a detection limit as low as 2.12 × 104 cells/mL on the system, which is capable of screening various types of clinical standards of bovine milk. The fitting degrees of the proposed POCT system with manual fluorescence microscopy were generally consistent (R2 > 0.99). As a proof of concept, four fresh milk samples were used in the test. The average accuracy of somatic cell counts was 98.0%, which was able to successfully differentiate diseased cows from healthy ones. The POCT system is user-friendly and low-cost, making it a potential tool for on-site diagnosis of bovine mastitis in resource-limited areas.

The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks

The evaluation of megakaryocytes is an important part of the work up on bone marrow smear examination. It has significance in the differential diagnosis, therapeutic efficacy assessment, and predication of prognosis of many hematologic diseases. The process of manual identification of megakaryocytes are tedious and lack of reproducibility; therefore, a reliable method of automated megakaryocytic identification is urgently needed. Three hundred and thirty-three bone marrow aspirate smears were digitized by Morphogo system. Pathologists annotated megakaryocytes on the digital images of marrow smears are applied to construct a large dataset for testing the system's predictive performance. Subsequently, we obtained megakaryocyte count and classification for each sample by different methods (system-automated analysis, system-assisted analysis, and microscopic examination) to study the correlation between different counting and classification methods. Morphogo system localized cells likely to be megakaryocytes on digital smears, which were later annotated by pathologists and the system, respectively. The system showed outstanding performance in identifying megakaryocytes in bone marrow smears with high sensitivity (96.57%) and specificity (89.71%). The overall correlation between the different methods was confirmed the high consistency (r ≥ 0.7218, R² ≥ 0.5211) with microscopic examination in classifying megakaryocytes. Morphogo system was proved as a reliable screen tool for analyzing megakaryocytes. The application of Morphogo system shows promises to advance the automation and standardization of bone marrow smear examination.

A Drop-in, Focus-Extending Phase Mask Simplifies Microscopic and Microfluidic Imaging Systems for Cost-Effective Point-of-Care Diagnostics

Microscopic imaging and imaging flow cytometry have wide potential in point-of-care assays; however, their narrow depth of focus necessitates precise mechanical or fluidic focus control of a sample in order to acquire high-quality images that can be used for downstream analysis, increasing the cost and complexity of the imaging system. This complexity represents a barrier to miniaturization and translation of point-of-care assays based on microscopic imaging or imaging flow cytometry. To address this challenge, we present a simple drop-in phase mask with a physics-informed, circularly symmetric asphere phase profile that extends the depth of focus by >5-fold while largely preserving the image quality compared to other depth extending methods. We show that such a focus-extended system overcomes manufacturing tolerances in low-cost sample chambers, enlarges the useable field-of-view of low-cost objectives, and permits increased throughput and precision in flow imaging systems without the need for complex flow-focusing. As the image quality is preserved without the need for postacquisition image restoration, our solution is also highly appropriate for on-line applications such as cell sorting.