Flow Cytometry Analysis of Microglial Phenotypes in the Murine Brain During Aging and Disease.

Microglia, the brain's primary resident immune cell, exists in various phenotypic states depending on intrinsic and extrinsic signaling. Distinguishing between these phenotypes can offer valuable biological insights into neurodevelopmental and neurodegenerative processes. Recent advances in single-cell transcriptomic profiling have allowed for increased granularity and better separation of distinct microglial states. While techniques such as immunofluorescence and single-cell RNA sequencing (scRNA-seq) are available to differentiate microglial phenotypes and functions, these methods present notable limitations, including challenging quantification methods, high cost, and advanced analytical techniques. This protocol addresses these limitations by presenting an optimized cell preparation procedure that prevents ex vivo activation and a flow cytometry panel to distinguish four distinct microglial states from murine brain tissue. Following cell preparation, fluorescent antibodies were applied to label 1) homeostatic, 2) disease-associated (DAM), 3) interferon response (IRM), and 4) lipid-droplet accumulating (LDAM) microglia, based on gene markers identified in previous scRNA-Seq studies. Stained cells were analyzed by flow cytometry to assess phenotypic distribution as a function of age and sex. A key advantage of this procedure is its adaptability, allowing the panel provided to be enhanced using additional markers with an appropriate cell analyzer (i.e., Cytek Aurora 5 laser spectral flow cytometer) and interrogating different brain regions or disease models. Additionally, this protocol does not require microglial cell sorting, resulting in a relatively quick and straightforward experiment. Ultimately, this protocol can compare the distribution of microglial phenotypic states between various experimental groups, such as disease state or age, with a lower cost and higher throughput than scRNA-seq. Key features • Analysis of microglial phenotypes from murine brain without the need for cell sorting, imaging, or scRNA-seq. • This protocol can distinguish between homeostatic, disease-associated (DAM), lipid-droplet accumulating (LDAM), and interferon response (IRM) microglia from any murine brain region and/or disease model of interest. • This protocol can be modified to incorporate additional markers of interest or dyes when using a cell analyzer capable of multiple color detections.

Microglial senescence contributes to female-biased neuroinflammation in the aging mouse hippocampus: implications for Alzheimer's disease.

BACKGROUND: Microglia, the brain's principal immune cells, have been implicated in the pathogenesis of Alzheimer's disease (AD), a condition shown to affect more females than males. Although sex differences in microglial function and transcriptomic programming have been described across development and in disease models of AD, no studies have comprehensively identified the sex divergences that emerge in the aging mouse hippocampus. Further, existing models of AD generally develop pathology (amyloid plaques and tau tangles) early in life and fail to recapitulate the aged brain environment associated with late-onset AD. Here, we examined and compared transcriptomic and translatomic sex effects in young and old murine hippocampal microglia. METHODS: Hippocampal tissue from C57BL6/N and microglial NuTRAP mice of both sexes were collected at young (5-6 month-old [mo]) and old (22-25 mo) ages. Cell sorting and affinity purification techniques were used to isolate the microglial transcriptome and translatome for RNA-sequencing and differential expression analyses. Flow cytometry, qPCR, and imaging approaches were used to confirm the transcriptomic and translatomic findings. RESULTS: There were marginal sex differences identified in the young hippocampal microglia, with most differentially expressed genes (DEGs) restricted to the sex chromosomes. Both sex chromosomally and autosomally encoded sex differences emerged with aging. These sex DEGs identified at old age were primarily female-biased and enriched in senescent and disease-associated microglial signatures. Normalized gene expression values can be accessed through a searchable web interface ( https://neuroepigenomics.omrf.org/ ). Pathway analyses identified upstream regulators induced to a greater extent in females than in males, including inflammatory mediators IFNG, TNF, and IL1B, as well as AD-risk genes TREM2 and APP. CONCLUSIONS: These data suggest that female microglia adopt disease-associated and senescent phenotypes in the aging mouse hippocampus, even in the absence of disease pathology, to a greater extent than males. This sexually divergent microglial phenotype may explain the difference in susceptibility and disease progression in the case of AD pathology. Future studies will need to explore sex differences in microglial heterogeneity in response to AD pathology and determine how sex-specific regulators (i.e., sex chromosomal or hormonal) elicit these sex effects.

Development and validation of a mobile application based on a machine learning model to aid in predicting dosage of vitamin K antagonists among Indian patients post mechanical heart valve replacement.

Patients who undergo heart valve replacements with mechanical valves need to take Vitamin K Antagonists (VKA) drugs (Warfarin, Nicoumalone) which has got a very narrow therapeutic range and needs very close monitoring using PT-INR. Accessibility to physicians to titrate drugs doses is a major problem in low-middle income countries (LMIC) like India. Our work was aimed at predicting the maintenance dosage of these drugs, using the de-identified medical data collected from patients attending an INR Clinic in South India. We used artificial intelligence (AI) - machine learning to develop the algorithm. A Support Vector Machine (SVM) regression model was built to predict the maintenance dosage of warfarin, who have stable INR values between 2.0 and 4.0. We developed a simple user friendly android mobile application for patients to use the algorithm to predict the doses. The algorithm generated drug doses in 1100 patients were compared to cardiologist prescribed doses and found to have an excellent correlation.

ThoraxNet: a 3D U-Net based two-stage framework for OAR segmentation on thoracic CT images.

An important phase of radiation treatment planning is the accurate contouring of the organs at risk (OAR), which is necessary for the dose distribution calculation. The manual contouring approach currently used in clinical practice is tedious, time-consuming, and prone to inter and intra-observer variation. Therefore, a deep learning-based auto contouring tool can solve these issues by accurately delineating OARs on the computed tomography (CT) images. This paper proposes a two-stage deep learning-based segmentation model with an attention mechanism that automatically delineates OARs in thoracic CT images. After preprocessing the input CT volume, a 3D U-Net architecture will locate each organ to generate cropped images for the segmentation network. Next, two differently configured U-Net-based networks will perform the segmentation of large organs-left lung, right lung, heart, and small organs-esophagus and spinal cord, respectively. A post-processing step integrates all the individually-segmented organs to generate the final result. The suggested model outperformed the state-of-the-art approaches in terms of dice similarity coefficient (DSC) values for the lungs and the heart. It is worth mentioning that the proposed model achieved a dice score of 0.941, which is 1.1% higher than the best previous dice score, in the case of the heart, an important organ in the human body. Moreover, the clinical acceptance of the results is verified using dosimetric analysis. To delineate all five organs on a CT scan of size [Formula: see text], our model takes only 8.61 s. The proposed open-source automatic contouring tool can generate accurate contours in minimal time, consequently speeding up the treatment time and reducing the treatment cost.

Correlation between radial and femoral arterial blood pressure during reperfusion in living donor liver transplantation.

BACKGROUND AND AIMS: Accurate blood pressure measurements are the mainstay for the efficient management of abrupt cardiovascular changes during reperfusion in liver transplant. We sought to compare the femoral and radial pressures during reperfusion and at T1:baseline, T2: 1 h in dissection: T3:portosystemic shunt, T4:reperfusion, T5: at bile duct anastomosis. METHODS: A retrospective study was performed amongst 102 adult patients who underwent R lobe living donor liver transplantation. Mean arterial pressure (MAP) and systolic arterial pressure (SAP) at 10 s intervals at reperfusion and at five fixed time points were compared by intraclass correlation coefficient (ICC) and limits of agreement by Bland-Altman statistics. RESULTS: MAP by both routes had a good correlation at all time points during reperfusion (overall ICC: 0.946 [0.938, 0.949]) in comparison with SAP (overall ICC: 0.650 [0.6128, 0.684]). At the lowest reperfusion pressure (reperfusion point), MAP showed high levels of agreements (ICC: 0.833 [0.761, 0.885]), whereas SAP showed only a poor level of agreement (ICC 0.343 [0.153, 0.508]). The Bland-Altman analysis for MAP showed a bias of 7.18 (5.94) mmHg and limits of agreement of - 4.5 mmHg to + 18.8 mmHg and for SAP a bias of 25.2 (22.04) mmHg and limits of agreement of - 18.0 mmHg to + 68.4 mmHg at the reperfusion point. The incidence of post-reperfusion syndrome (PRS) was 52.94% by femoral and 57.84% by radial routes. CONCLUSIONS: Radial MAP correlated well with femoral MAP during reperfusion and at predefined time points and can be used interchangeably for intraoperative monitoring. A high incidence of PRS was noted by our technique of measurement.

Efficacy of ultrasound guided quadratus lumborum block as postoperative analgesia in renal transplantation recipients: A randomised double blind clinical study.

BACKGROUND AND AIMS: Postoperative pain following renal transplantation is moderate to severe. Quadratus lumborum block (QLB) is a new block that can provide effective analgesia following abdominal and retroperitoneal surgeries. This study aimed to evaluate the analgesic efficacy of QLB for postoperative analgesia in patients undergoing renal transplantation. METHODS: Patients were randomised into two groups of 30 each. In group A (block group), 20 mL of 0.25% bupivacaine and group B (placebo group), 20 mLof normal saline were injected. In the postoperative room, an intravenous patient controlled analgesia (IVPCA) pump with fentanyl was started in both the group. The postoperatively recorded parameters were numerical rating scale (NRS) pain score at rest and on movement and coughing, total fentanyl consumption, sedation score, postoperative nausea vomiting, limb weakness, paralytic ileus, and any other block-related complication. Data were analysed using SPSS software version 22.0. Categorical data were analysed using the Chi-square method. Student t test or Mann-Whitney U test was applied for the continuous data. Numerical data with normal distribution were displayed as mean (standard deviation), abnormal distribution was displayed in the median (interquartile range) values, and as a percentage for categorical variables. RESULTS: Fentanyl consumption, numerical rating score, and sedation score were significantly less in group A when compared to group B at 1, 4, 8, 12, and 24 h (P < 0.001). CONCLUSION: Type-1 QLB significantly reduces fentanyl consumption and NRS pain score at 1,4,8,12, and 24 h in the postoperative period in renal transplant recipients.

Echo: A Large Display Interactive Visualization of ICU Data for Effective Care HandOffs.

One of the significant challenges of care transitions in Intensive Care Units (ICUs) is the lack of effective support tools for outgoing clinicians to find, filter, organize, and annotate information that can be effectively handed off to the incoming team. We present a large display interactive multivariate visual approach, aimed towards supporting clinicians during the transition of care. We first provide a characterization of the problem domain in terms of data and tasks, based on an observation session at the University of Illinois Hospital, and on interviews with several biomedical researchers and ICU clinicians. Informed by this experience, we design a scalable, interactive visual approach that supports both overview and detail views of ICU patient data, as well as anomaly detection, comparison, and annotation of the data. We demonstrate a large-display implementation of the visualization on an existing anonymized ICU dataset. Feedback from domain experts indicates this approach successfully meets the requirements of effective care transitions.