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Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly efficient in increasing the accuracy values of the AI model output for predicting CSCs from phase-contrast images. An AI model of conditional generative adversarial network (CGAN) image translation for CSC identification predicted CSCs with various accuracy levels, and convolutional neural network classification of CSC phase-contrast images showed variation in the images. The accuracy of the AI model of CGAN image translation was increased by the AI model built by deep learning of selected CSC images with high accuracy previously calculated by another AI model. The workflow of building an AI model based on CGAN image translation could be useful for the AI prediction of CSCs.

The last decade has led to rapid developments and increased usage of computational tools at the single-cell level. However, our knowledge remains limited in how extracellular cues alter quantitative macrophage morphology and how such morphological changes can be used to predict macrophage phenotype as well as cytokine content at the single-cell level. Using an artificial intelligence (AI) based approach, this study determined whether (i) accurate macrophage classification and (ii) prediction of intracellular IL-10 at the single-cell level was possible, using only morphological features as predictors for AI. Using a quantitative panel of shape descriptors, our study assessed image-based original and synthetic single-cell data in two different datasets in which CD14+ monocyte-derived macrophages generated from human peripheral blood monocytes were initially primed with GM-CSF or M-CSF followed by polarization with specific stimuli in the presence/absence of continuous GM-CSF or M-CSF. Specifically, M0, M1 (GM-CSF-M1, TNFα/IFNγ-M1, GM-CSF/TNFα/IFNγ-M1) and M2 (M-CSF-M2, IL-4-M2a, M-CSF/IL-4-M2a, IL-10-M2c, M-CSF/IL-10-M2c) macrophages were examined. Phenotypes were confirmed by ELISA and immunostaining of CD markers. Variations of polarization techniques significantly changed multiple macrophage morphological features, demonstrating that macrophage morphology is a highly sensitive, dynamic marker of phenotype. Using original and synthetic single-cell data, cell morphology alone yielded an accuracy of 93% for the classification of 6 different human macrophage phenotypes (with continuous GM-CSF or M-CSF). A similarly high phenotype classification accuracy of 95% was reached with data generated with different stimuli (discontinuous GM-CSF or M-CSF) and measured at a different time point. These comparably high accuracies clearly validated the here chosen AI-based approach. Quantitative morphology also allowed prediction of intracellular IL-10 with 95% accuracy using only original data. Thus, image-based machine learning using morphology-based features not only (i) classified M0, M1 and M2 macrophages but also (ii) classified M2a and M2c subtypes and (iii) predicted intracellular IL-10 at the single-cell level among six phenotypes. This simple approach can be used as a general strategy not only for macrophage phenotyping but also for prediction of IL-10 content of any IL-10 producing cell, which can help improve our understanding of cytokine biology at the single-cell level.

Polyhydroxyalkanoates (PHAs) can be produced by microorganisms from renewable resources and are regarded as promising bioplastics to replace petroleum-based plastics. A medium-chain-length PHAs (mcl-PHA)-producing strain Pseudomonas mendocina NK-01 was isolated previously by our lab and its whole-genome sequence is currently available. Morphology engineering of manipulating cell morphology–related genes has been applied for enhanced accumulation of the intracellular biopolymer short-chain-length PHAs (scl-PHA). However, it has not yet been reported to improve the yield of mcl-PHA by morphology engineering so far. In this work, several well-characterized cell morphology–related genes, including the cell fission ring (Z-ring) location genes minCD , peptidoglycan degradation gene nlpD , actin-like cytoskeleton protein gene mreB , Z-ring formation gene ftsZ , and FtsZ inhibitor gene sulA , were intensively investigated for their impacts on the cell morphology and mcl-PHA accumulation by gene knockout and overexpression in P . mendocina NKU, a upp knockout mutant of P . mendocina NK-01. For a minCD knockout mutant P . mendocina NKU-∆ minCD , the average cell length was obviously increased and the mcl-PHA production was improved. However, the nlpD knockout mutant had a shorter cell length and lower mcl-PHA yield compared with P . mendocina NKU. Overexpression of mreB in P . mendocina NKU resulted in spherical cells. When ftsZ was overexpressed in P . mendocina NKU, the cell division was accelerated and the mcl-PHA titer was improved. Furthermore, mreB , ftsZ , or sulA was overexpressed in P . mendocina NKU-∆ minCD . Consequently, the mcl-PHA titers were all increased compared with P . mendocina NKU-∆ minCD carrying the empty vector. The multiple fission pattern was finally achieved in ftsZ -overexpressing NKU-∆ minCD . In this work, improved production of mcl-PHA in P. mendocina NK-01 has been achieved by morphology engineering. This work provides an alternative strategy to enhance mcl-PHA accumulation in mcl-PHA-producing strains.

Background/Objectives: Mycosis fungoides (MF) and Sézary syndrome (SS) are cutaneous T-cell lymphomas (CTCLs) with variable clinical outcomes. Peripheral blood (PB) involvement in MF/SS is an independent predictor of prognosis. Accurate laboratory determination of PB involvement by MF/SS cells, however, is an ongoing challenge. Both flow cytometry (FC) and morphology-based quantification are limited by the overlap of CTCL cells and reactive T-cells. This study looks at the optimization over time of CTCL blood burden evaluation. Methods: This retrospective study reviews CTCL blood assessment at Northwestern Memorial Hospital from 2012 to 2021. Test ordering and reporting practices for morphology-based Sézary cell counts and FC were evaluated. For each assay, quantitative and qualitative results were analyzed and compared including percentages and absolute counts of abnormal T-cell populations and pathologist interpretations. Results: A total of 514 patients were evaluated, with increasing numbers of both tests ordered over time. FC quantitative metrics showed a moderate to high correlation with morphology metrics, especially for absolute CD4+/CD7− counts (correlation coefficient = 0.901, p-value < 0.001). Qualitative pathologist interpretations had moderate agreement between methods (kappa = 0.58). The recent addition of TRBC1 clonality assessment to our FC assay further optimizes the evaluation for CTCL blood burden. Conclusions: Flow cytometry offers a reliable approach for blood staging in MF/SS, and morphologic assessment may be redundant. This study provides a foundation for designing a new FC approach with TRBC1. This comprehensive review of the evolution of our laboratory practices may serve as a guide for other institutions with similar clinical needs.

Currently, synthetic biology approaches have been developed for constructing microbial cell factories capable of efficient synthesis of high value-added products. Most studies have focused on the construction of novel biosynthetic pathways and their regulatory processes. Morphology engineering has recently been proposed as a novel strategy for constructing efficient microbial cell factories, which aims at controlling cell shape and cell division pattern by manipulating the cell morphology-related genes. Morphology engineering strategies have been exploited for improving bacterial growth rate, enlarging cell volume and simplifying downstream separation. This mini-review summarizes cell morphology-related proteins and their function, current advances in manipulation tools and strategies of morphology engineering, and practical applications of morphology engineering for enhanced production of intracellular product polyhydroxyalkanoate and extracellular products. Furthermore, current limitations and the future development direction using morphology engineering are proposed.

PurposeThe investigation of a semi-quantitative index in the pelvis to assess for diffuse bone marrow (BM) [18F]-FDG uptake and the investigation of PET skeletal patterns in multiple myeloma (MM) patients, in accordance with prognostic markers, clonal plasma cell (cPC) morphology, and response to therapy.MethodsWe prospectively analyzed [18F]-FDG PET/CT in 90 MM patients (newly diagnosed, 60; relapsed/refractory, 30). Among other PET/CT parameters, we calculated the ratio SUVmax pelvis/liver and examined for correlations with known MM prognostic parameters, cPC morphology (good vs. low/intermediate differentiation), and response to therapy.ResultsSUVmax pelvis/liver ratio was significantly lower for the group of good differentiation vs. intermediate/low differentiation cPCs (p < 0.001) and showed a positive correlation with BM infiltration rate, β2 microglobulin, serum ferritin, international staging system (ISS), and revised ISS; no significant correlation was found with hemoglobin. A cutoff value of 1.1 showed an excellent specificity (99%) and high sensitivity (76%) for diffuse BM involvement (AUC 0.94; p < 0.001). Mixed pattern and appendicular involvement correlated with poor prognostic features while normal pattern, found in 30% of patients, correlated with good prognostic features. Presence of ≥ 10 focal lesions negatively predicted for overall response (p < 0.05; OR 4.8). The CT component improved the diagnostic performance of PET.ConclusionThis study showed, for the first time, that cPC morphology and markers related with MM biology, correlate with SUVmax pelvis/liver index, which could be used as a surrogate marker for BM assessment and disease prognosis; PET patterns correlate with MM prognostic features and response rates.

Background The morphology and content of stratum corneum (SC) cells provide information on the physiological condition of the skin. Although the morphological and biochemical properties of the SC are known, no method is available to fully access and interpret this information. This study aimed to develop a method to comprehensively decode the physiological information of the skin, based on the SC. Therefore, we established a novel image analysis technique based on artificial intelligence (AI) and multivariate analysis to predict skin conditions. Materials and Methods SC samples were collected from participants, imaged, and annotated. Nine biomarkers were measured in the samples using enzyme‐linked immunosorbent assay. The data were then used to teach machine‐learning models to recognize individual SC cell regions and estimate the levels of the nine biomarkers from the images. Skin physiological indicators (e.g., skin barrier function, facial analysis, and questionnaires) were measured or obtained from the participants. Multivariate analysis, including biomarker levels ​​and structural parameters of the SC as variables, was used to estimate these physiological indicators. Results We established two machine‐learning models. The accuracy of recognition was assessed according to the average intersection over union (0.613), precision (0.953), recall (0.640), and F‐value (0.766). The predicted biomarker levels significantly correlated with the measured levels. Skin physiological indicators and questionnaire answers were predicted with strong correlations and correct answer rates. Conclusion Various physiological skin conditions can be predicted from images of the SC using AI models and multivariate analysis. Our method is expected to be useful for dermatological treatment optimization.

Cell morphology has been widely investigated for its influence on the functions of normal cells. However, the influence of cell morphology on cancer cell resistance to anti-cancer drugs remains unclear. In this study, micropatterned surfaces were prepared and used to control the spreading area and elongation of human breast cancer cell line. The influences of cell adhesion area and elongation on resistance to doxorubicin were investigated. The percentage of apoptotic breast cancer cells decreased with cell spreading area, while did not change with cell elongation. Large breast cancer cells had higher resistance to doxorubicin, better assembled actin filaments, higher DNA synthesis activity and higher expression of P-glycoprotein than small breast cancer cells. The results suggested that the morphology of breast cancer cells could affect their resistance to doxorubicin. The influence was correlated with cytoskeletal organization, DNA synthesis activity and P-glycoprotein expression.

Background The clinical value of procalcitonin (PCT) in infection diagnosis and antibiotic stewardship is still unclear. This study aimed to investigate the association between serum PCT and different clinical conditions as well as other infectious/inflammatory parameters in different septic patients in order to elucidate the value of PCT detection in infection management. Methods Chemiluminescence immunoassay was used for serum PCT analysis. Hematology analysis was used for complete blood cell count. Digital automated cell morphology analysis was used for blood cell morphology examination. Blood, urine, and stool cultures were performed according to routine clinical laboratory standard operating procedures. C‐reactive protein (CRP) was analyzed by immunoturbidimetry. Erythrocyte sedimentation rate test was performed using natural sedimentation methods. Results Outpatients, ICU patients, and patients under 2 years of age with respiratory infections had higher serum PCT levels. Septic patients had the highest‐serum PCT levels and other infection indexes. PCT levels in the blood, urine, and stool culture‐positive patients were significantly higher than in culture‐negative patients. The neutrophil granulation and reactive lymphocytes were observed together with the PCT‐level increments in different septic patients, and these alterations were lessened after treatment. There was no significant change in monocyte morphology between pre‐ and posttreatment septic patients. Conclusions Serum PCT is associated with neutrophil cytotoxicity and lymphocyte morphology changes in sepsis; thus, the combination of neutrophil and lymphocyte digital cell morphology evaluations with PCT detection may be a useful examination for guiding the clinical management of sepsis. Serum procalcitonin (PCT), hematology parameters, and cell morphology in different clinical conditions and sepsis; the association of serum PCT levels and neutrophil cytotoxicity and lymphocyte morphology changes using the digital automated cell morphology technology and other inflammatory/infection parameters.

—Blood cells of harbor seals, Phoca vitulina Linnaeus, 1758, from the Commander Islands population have been characterized in detail for the first time. Data on the composition, size, and ratio of red blood cells (RBCs), white blood cells (WBCs), and platelets in the peripheral blood of immature and mature seals are provided. A micrograph library for all types of peripheral blood cells of harbor seal is presented. The blood cell morphology of this seal generally corresponds to that of other pinniped species; however, neutrophils with Döhle bodies, reactive lymphocytes, and hypersegmented neutrophils have been found in blood films. Increased numbers of eosinophils (10–25%) have been recorded from the WBC differential counts of all examined seals. Mature seals had higher numbers of eosinophils (p = 0.04) and large lymphocytes (p = 0.005); immature seals had higher numbers of monocytes (p = 0.04) and Howell–Jolly bodies in RBCs (p = 0.03). Microfilariae and nucleated RBCs were observed in blood films of two mature seals.