Pregnant in a Pandemic: Mental Wellbeing and Associated Healthy Behaviors Among Pregnant People in California During COVID-19.

INTRODUCTION: Pregnancy is a time of increased vulnerability to mental health disorders. Additionally, the COVID-19 pandemic has increased the incidence of depression and anxiety. Thus, we aimed to assess mental health and associated healthy behaviors of pregnant people in California during the pandemic in order to contextualize prenatal well-being during the first pandemic of the twenty-first century. METHODS: We conducted an online cross-sectional study of 433 pregnant people from June 6 through July 29, 2020. We explored 3 hypotheses: (1) mental health would be worse during the pandemic than in general pregnant samples to date; (2) first-time pregnant people would have worse mental health; and (3) healthy behaviors would be positively related to mental health. RESULTS: Many of our participants (22%) reported clinically significant depressive symptoms and 31% reported clinically significant anxiety symptoms. Multiparous pregnant people were more likely to express worries about their own health and wellbeing and the process of childbirth than were primiparous pregnant people. Additionally, as pregnancy advanced, sleep and nutrition worsened, while physical activity increased. Lastly, anxious-depressive symptomology was significantly predictive of participant sleep behaviors, nutrition, and physical activity during the past week. DISCUSSION: Pregnant people had worse mental health during the pandemic, and this was associated with worse health-promoting behaviors. Given that the COVID-19 pandemic and associated risks are likely to persist due to low vaccination rates and the emergence of variants with high infection rates, care that promotes mental and physical well-being for the pregnant population should be a public health priority.

From hybrid to fully remote clinical trial amidst the COVID-19 pandemic: Strategies to promote recruitment, retention, and engagement in a randomized mHealth trial.

Clinical trials worldwide were disrupted when the COVID-19 pandemic began in early 2020. Most intervention trials moved to some form of remote implementation due to restrictions on in-person research activities. Although the proportion of remote trials is growing, they remain the vast minority of studies in part due to few successful examples. Our team transitioned Goals for Reaching Optimal Wellness (GROWell), an NIH-funded (R01NR017659) randomized control trial (RCT; ClinicalTrials.gov identifier NCT04449432) originally designed as a hybrid intervention, into a fully remote clinical trial. GROWell is a digital dietary intervention for people who enter pregnancy with overweight or obesity. Primary outcomes include gestational weight gain and six-month postpartum weight retention. Strategies that we have tested, refined, and deployed include: (a) use of a HIPAA-compliant, web-based participant recruitment and engagement platform; (b) use of a HIPAA-compliant digital health platform to disseminate GROWell and conduct study visits (c) interconnectivity of these two platforms for seamless recruitment, consent, enrollment, intervention delivery, follow-up, and study team blinding; (d) detailed SMS messages to address initial challenges with protocol adherence; (e) email notifications alerting the study team about missed participant surveys so they can follow-up; (f) remuneration using email gift cards with recipient choice of vendor; and (g) geotargeting social media campaigns to improve participation of Black Indigenous and People of Color Communities. These strategies have resulted in screen failure rates improving by 7%, study task adherence improving by an average of 20-30% across study visits, and study completion rates of 82%. Researchers may consider some or all of these approaches in future remote mHealth trials.

Measuring Evidence-Based Viral Respiratory Illness Mitigation Behaviors in Pregnant Populations: Development and Validation of a Short, Single-Factor Scale During the COVID-19 Pandemic.

OBJECTIVE: Researchers and public health professionals need to better understand individual engagement in coronavirus disease 2019 (COVID-19) mitigation behaviors to reduce the human and societal costs of the current pandemic and prepare for future respiratory pandemics. We suggest that developing measures of individual mitigation behaviors and testing them among high-risk individuals, including pregnant people, may help to reduce overall morbidity and mortality by quickly identifying targets for messaging around mitigation until sufficient vaccination uptake is reached. METHODS: We surveyed pregnant people in California over 2 waves of the COVID-19 pandemic to explore mitigation behaviors. We developed and validated a novel Viral Respiratory Illness Mitigation Scale (VRIMS). RESULTS: Seven measures loaded onto a single factor with good psychometric properties. The overall sample scale average was high over both waves, indicating that most pregnant Californians engaged in most of the strategies most of the time. Older participants, minoritized participants, those living in more urban contexts, and those surveyed during a surge reported engaging in these strategies most frequently. CONCLUSIONS: Clinicians and researchers should consider using reliable, validated measures like the VRIMS to identify individuals and communities that may benefit from additional education on reducing risk for COVID-19, future respiratory pandemics, or even seasonal flu.

Understanding COVID-19 vaccine uptake during pregnancy: 'Hesitance', knowledge, and evidence-based decision-making.

BACKGROUND: A key mitigation strategy to the COVID-19 pandemic has been the development and roll-out of vaccines. However, pregnant and lactating people were not included in initial vaccine trials and this population is hesitant to receive the vaccine, despite contrary recommendations from the American College of Obstetrics and Gynecology and the Centers for Disease Control and Prevention. Understanding the reasons behind this hesitancy is vital to promote vaccine uptake. METHODS: We surveyed pregnant people in California from December 2020 to January 2021 (n = 387) to describe cognitions and decision-making regarding COVID-19 vaccination. Using descriptive, regression-based analyses, we examined rates of planned uptake and reasoning among individuals who reported COVID-19 vaccine hesitancy. RESULTS: Overall, the pregnant Californians that we surveyed were aware of the COVID-19 vaccines. Of 387 participants, 43% reported planning to get the vaccine as soon as possible. The remaining 57% were hesitant: 27% responded that they would not receive the vaccine. Some demographic features did predict more COVID-19 vaccine hesitancy, particularly younger age (AOR = 0.95, p = 0.025) and living in a less urban context (AOR = 0.80, p = 0.041). Essential worker status also was associated with vaccine hesitancy. Having had, or intending to have, a flu vaccine was negatively associated with COVID-19 vaccine hesitancy (p < 0.001). The most commonly reported reason for COVID-19 vaccine hesitancy was "I don't know enough about the vaccine." Low levels of self-reported knowledge were highly predictive of hesitancy. CONCLUSIONS: Terms like "vaccine hesitance" and "anti-vax" do not adequately characterize decisions regarding delaying COVID-19 vaccination. Rather, these decisions are largely based on the lack of knowledge about the impacts of vaccination on pregnancy, fetal development, and later child wellbeing. This lack of knowledge should be countered by conversations between individual healthcare providers and their pregnant patients, and better inclusion of pregnant people and children in vaccine trials.

Goals for Reaching Optimal Wellness (GROWell): A clinical trial protocol of a digital dietary intervention for pregnant and postpartum people with prenatal overweight or obesity.

BACKGROUND: Excess gestational weight gain (EGWG) is associated with multiple pregnancy complications and health risks for birthing people and their infants. Likewise, postpartum weight retention (PPWR), or not losing all pregnancy weight, has long-term health consequences. EGWG among people who enter pregnancy with overweight or obesity have worse obstetric outcomes and increased PPWR compared to women who gain within Institute of Medicine guidelines. METHODS: This study protocol describes the details of a blinded, randomized clinical trial of GROWell: Goals for Reaching Optimal Wellness, a mHealth tool designed to improve diet quality among people who enter pregnancy with overweight or obese BMIs to help them achieve appropriate GWG and safe postpartum pregnancy weight loss. Individuals with overweight and obesity will be randomly assigned to an attention control or intervention arm. The intervention group will receive personalized, goal-oriented text messages regarding dietary choices, while the attention control group will receive text messages about healthy pregnancy, labor, delivery, and early infancy. Both groups will complete online surveys at baseline, follow up, 3 and 6 months postpartum. RESULTS AND DISCUSSION: Currently, 162 subjects have been enrolled. Outcomes associated with GWG and pregnancy are expected in late 2023, while outcomes on postpartum weight retention GROWell adherence are expected in late 2024. The results of this trial will support the use of an evidence-based mHealth tool to be integrated into clinical practice to reduce EGWG and PPWR among pregnant people with overweight and obese BMIs, a resource that is currently lacking. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04449432. Registered on June 26, 2020.

Perinatal health care access, childbirth concerns, and birthing decision-making among pregnant people in California during COVID-19.

BACKGROUND: During public health emergencies, including the COVID-19 pandemic, access to adequate healthcare is crucial for providing for the health and wellbeing of families. Pregnant and postpartum people are a particularly vulnerable subgroup to consider when studying healthcare access. Not only are perinatal people likely at higher risk for illness, mortality, and morbidity from COVID-19 infection, they are also at higher risk for negative outcomes due to delayed or inadequate access to routine care. METHODS: We surveyed 820 pregnant people in California over two waves of the COVID-19 pandemic: (1) a 'non-surge' wave (June 2020, n = 433), and (2) during a 'surge' in cases (December 2020, n = 387) to describe current access to perinatal healthcare, as well as concerns and decision-making regarding childbirth, over time. We also examined whether existing structural vulnerabilities - including acute financial insecurity and racial/ethnic minoritization - are associated with access, concerns, and decision-making over these two waves. RESULTS: Pregnant Californians generally enjoyed more access to, and fewer concerns about, perinatal healthcare during the winter of 2020-2021, despite surging COVID-19 cases and hospitalizations, as compared to those surveyed during the COVID-19 'lull' in the summer of 2020. However, across 'surge' and 'non-surge' pandemic circumstances, marginalized pregnant people continued to fare worse - especially those facing acute financial difficulty, and racially minoritized individuals identifying as Black or Indigenous. CONCLUSIONS: It is important for clinicians, researchers, and policymakers to understand whether and how shifting community transmission and infection rates may impact access to perinatal healthcare. Targeting minoritized and financially insecure communities for increased upstream perinatal healthcare supports are promising avenues to blunt the negative impacts of the COVID-19 pandemic on pregnant people in California.

Label-Free Visualization and Quantification of Biochemical Markers of Atherosclerotic Plaque Progression Using Intravascular Fluorescence Lifetime.

OBJECTIVES: This study aimed to systematically investigate whether plaque autofluorescence properties assessed with intravascular fluorescence lifetime imaging (FLIm) can provide qualitative and quantitative information about intimal composition and improve the characterization of atherosclerosis lesions. BACKGROUND: Despite advances in cardiovascular diagnostics, the analytic tools and imaging technologies currently available have limited capabilities for evaluating in situ biochemical changes associated with luminal surface features. Earlier studies of small number of samples have shown differences among the autofluorescence lifetime signature of well-defined lesions, but a systematic pixel-level evaluation of fluorescence signatures associated with various histological features is lacking and needed to better understand the origins of fluorescence contrast. METHODS: Human coronary artery segments (n = 32) were analyzed with a bimodal catheter system combining multispectral FLIm with intravascular ultrasonography compatible with in vivo coronary imaging. Various histological components present along the luminal surface (200-µm depth) were systematically tabulated (12 sectors) from each serial histological section (n = 204). Morphological information provided by ultrasonography allowed for the accurate registration of imaging data with histology data. The relationships between histological findings and FLIm parameters obtained from 3 spectral channels at each measurement location (n = 33,980) were characterized. RESULTS: Our findings indicate that fluorescence lifetime from different spectral bands can be used to quantitatively predict the superficial presence of macrophage foam cells (mFCs) (area under the receiver-operator characteristic curve: 0.94) and extracellular lipid content in advanced lesions (lifetime increase in 540-nm band), detect superficial calcium (lifetime decrease in 450-nm band area under the receiver-operator characteristic curve: 0.90), and possibly detect lesions consistent with active plaque formation such as pathological intimal thickening and healed thrombus regions (lifetime increase in 390-nm band). CONCLUSIONS: Our findings indicate that autofluorescence lifetime provides valuable information for characterizing atherosclerotic lesions in coronary arteries. Specifically, FLIm can be used to identify key phenomena linked with plaque progression (e.g., peroxidized-lipid-rich mFC accumulation and recent plaque formation).

Home-Based Primary Care Led-Outbreak Mitigation in Assisted Living Facilities in the First 100 Days of Coronavirus Disease 2019.

Residents of congregate care settings have been severely impacted by the current Coronavirus disease 2019 (COVID-19) pandemic. In this report, we describe the methods our home-based primary care practice has developed to mitigate the spread of COVID-19 in assisted living facilities (ALFs) and we present an initial evaluation of this innovation. Shortly after the first COVID-19 case was reported in the United States, our organization assembled an outbreak committee, designed to support the 1794 ALF residents and the 101 communities in which they reside. The committee led the development and deployment of a comprehensive COVID-19 prevention and suppression strategy. The average age of the cohort was 83 ± 11 years, and 74% were female. Seven individuals (0.4% of census) tested positive for SARS-CoV-2. The positive individuals were located in 3 ALFs, representing 3% of our total number of ALFs. There has been 1 death. Home-based primary care-led outbreak mitigation may be an enabler to suppress COVID-19 in ALFs.

Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning.

Tumor-free surgical margins are critical in breast-conserving surgery. In up to 38% of the cases, however, patients undergo a second surgery since malignant cells are found at the margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure clear margins at the time of surgery. The objective of this study was to evaluate a random forest classifier that makes use of parameters derived from point-scanning label-free fluorescence lifetime imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen. FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in this study. The supervised training was based on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides. A pathologist's histology annotations provide the ground truth to distinguish between adipose, fibrous, and tumor tissue. Current results demonstrate the ability of this approach to classify the tumor with 89% sensitivity and 93% specificity and to rapidly (∼ 20 frames per second) overlay the probabilistic classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore, we show an iterative imaging refinement that allows surgeons to switch between rapid scans with a customized, low spatial resolution to quickly cover the specimen and slower scans with enhanced resolution (400 µm per point measurement) in suspicious regions where more details are required. In summary, this technique provides high diagnostic prediction accuracy, rapid acquisition, adaptive resolution, nondestructive probing, and facile interpretation of images, thus holding potential for clinical breast imaging based on label-free FLIm.

An Outbreak Preparedness and Mitigation Approach in Home Health and Personal Home Care During the COVID-19 Pandemic.

The acute respiratory disease COVID-19, caused by the novel Coronavirus SARS-CoV-2, is a worldwide pandemic affecting millions of people. The methodology that organizations who provide home health and personal home care services are using to respond to this pandemic has not yet been characterized. In this report, we describe our approach to comprehensive outbreak suppression and report an initial case series of COVID-19 positive patients receiving home-based services. We implemented enhanced infection control procedures across our affiliates, and we communicated these protocols to our offices using multi-faceted methods. Using custom built software applications enabling us to track patient and employee cases and exposures, we leveraged current public health recommendations to identify cases and to suppress transmission. In the 100-day period between January 20, 2020 and April 30, 2020, our affiliates provided services to 67 COVID-19 positive patients (<0.3% of census). Twenty patients were referred to home health post hospitalization for COVID-19 related illness, whereas 47 were found to have COVID-19 while living in community settings. Of those who were found to have COVID-19 in the community, 17 (39%) required subsequent hospitalization. Hospitalized patients had an average age of 74.5 ± 18, and 53% were male. There were 13 deaths (76%) among those hospitalized from the community with COVID-19 related illness. A highly coordinated and frequently communicated approach to infection control, case identification and employee screening can be performed by home health and personal home care organizations. Studies that further assess risks and predictors of illness severity in home-based COVID-19 patients are needed.

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment.

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging.

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N = 20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

Simultaneous, label-free, multispectral fluorescence lifetime imaging and optical coherence tomography using a double-clad fiber.

We present a novel fiber-based imaging platform that allows simultaneous fluorescence lifetime imaging (FLIm) and optical coherence tomography (OCT) using a double-clad fiber. This platform acquires co-registered images showing structural and compositional contrast in unlabeled biological samples by scanning the fiber tip across the sample surface. In this Letter, we report a characterization of each modality and show examples of co-registered FLIm and OCT images acquired from a lemon segment and a section of human coronary artery. The close comparison between the combined FLIm and OCT images and a co-registered histology section provides a qualitative validation of the technique and highlights its potential for minimally invasive, multimodal imaging of tissue structure and composition.

In vivo label-free structural and biochemical imaging of coronary arteries using an integrated ultrasound and multispectral fluorescence lifetime catheter system.

Existing clinical intravascular imaging modalities are not capable of accurate detection of critical plaque pathophysiology in the coronary arteries. This study reports the first intravascular catheter combining intravascular ultrasound (IVUS) with multispectral fluorescence lifetime imaging (FLIm) that enables label-free simultaneous assessment of morphological and biochemical features of coronary vessels in vivo. A 3.7 Fr catheter with a fiber-optic channel was constructed based on a 40 MHz clinical IVUS catheter. The ability to safely acquire co-registered FLIm-IVUS data in vivo using Dextran40 solution flushing was demonstrated in swine coronary arteries. FLIm parameters from the arterial wall were consistent with the emission of fluorophores present in healthy arterial wall (collagen, elastin). Additionally, structural and biochemical features from atherosclerotic lesions were acquired in ex vivo human coronary samples and corroborated with histological findings. Current results show that FLIm parameters linked to the amount of structural proteins (e.g. collagen, elastin) and lipids (e.g. foam cells, extracellular lipids) in the first 200 µm of the intima provide important biochemical information that can supplement IVUS data for a comprehensive assessment of plaques pathophysiology. The unique FLIm-IVUS system evaluated here has the potential to provide a comprehensive insight into atherosclerotic lesion formation, diagnostics and response to therapy.

Lipid-lowering therapy stabilizes the complexity of non-culprit plaques in human coronary artery: a quantitative assessment using OCT bright spot algorithm.

To quantitatively evaluate the change of plaque complexity with cholesterol lowering therapy. A total of 44 non-culprit plaques from 30 patients who had serial image acquisition at baseline, 6-months, and 12-months by both optical coherence tomography (OCT) and intravascular ultrasound (IVUS) were included. Patients were treated with atorvastatin 60 mg (AT60, n = 16) or 20 mg (AT20, n = 14). We applied an OCT bright spot algorithm, which identifies a variety of plaque components including macrophages. The density of bright spot was measured within the superficial 250 µm of the vessel wall. Significant reduction of bright spot density was observed from baseline to 12-months [-0.49% (-0.95, -0.20), p < 0.001], particularly during the second 6 months [first 6 months: -0.01% (-0.57, 0.60), p = 0.939; second 6 months: -0.49% (-0.98, 0.14), p < 0.001]. Although there was no significant difference at 12 months in the reduction of bright spot density between plaques with acute coronary syndrome (ACS, n = 33) and those with stable angina (n = 11) [-0.49% (-0.93, -0.19) vs. -0.39% (-1.01, -0.21), p = 0.748], a significant reduction of bright spot density during the first 6 months was observed only in plaques with ACS. There was no significant difference in the change of bright spot density between the AT60 group (n = 22) and AT20 group (n = 22) [-0.61% (-0.93, -0.34) vs. -0.41% (-0.98, -0.19), p = 0.483]. Coronary plaque complexity evaluated by a quantitative OCT algorithm significantly decreased with 12 month atorvastatin therapy irrespective of the dose and initial clinical presentation.

Diagnosis of Thin-Capped Fibroatheromas in Intravascular Optical Coherence Tomography Images: Effects of Light Scattering.

BACKGROUND: Intravascular optical coherence tomography (IVOCT) images are recorded by detecting light backscattered within coronary arteries. We hypothesize that non-thin-capped fibroatheroma (TCFA) causes may scatter light to create the false appearance of IVOCT TCFA. METHODS AND RESULTS: Ten human cadaver hearts were imaged with IVOCT (n=14 coronary arteries). IVOCT and histological TCFA images were coregistered and compared. Of 21 IVOCT TCFAs (fibrous cap <65 µm, lipid arc >1 quadrant), only 8 were true histological TCFA. Foam cell infiltration was responsible for 70% of false IVOCT TCFA and caused both thick-capped fibroatheromas to appear as TCFA, and the appearance of TCFAs when no lipid core was present. Other false IVOCT TCFA causes included smooth muscle cell-rich fibrous tissue (12%) and loose connective tissue (9%). If the lipid arc >1 quadrant (obtuse) criterion was disregarded, 45 IVOCT TCFAs were identified, and sensitivity of IVOCT TCFA detection increased from 63% to 87%, and specificity remained high at 92%. CONCLUSIONS: We demonstrate that IVOCT can exhibit 87% (95% CI, 75%-93%) sensitivity and 92% specificity (95% CI, 86%-96%) to detect all lipid arcs (both obtuse and acute, <1 quadrant) TCFA, and we also propose new mechanisms involving light scattering that explain why other plaque components can masquerade as TCFA and cause low positive predictive value of IVOCT for TCFA detection (47% for obtuse lipid arcs). Disregarding the lipid arc >1 quadrant requirement enhances the ability of IVOCT to detect TCFA.

Comparing Raman and fluorescence lifetime spectroscopy from human atherosclerotic lesions using a bimodal probe.

Fluorescence lifetime imaging (FLIm) and Raman spectroscopy are two promising methods to support morphological intravascular imaging techniques with chemical contrast. Both approaches are complementary and may also be used in combination with OCT/IVUS to add chemical specificity to these morphologic intravascular imaging modalities. In this contribution, both modalities were simultaneously acquired from two human coronary specimens using a bimodal probe. A previously trained SVM model was used to interpret the fluorescence lifetime data; integrated band intensities displayed in RGB false color images were used to interpret the Raman data. Both modalities demonstrate unique strengths and weaknesses and these will be discussed in comparison to histologic analyses from the two coronary arteries imaged.

Differences in forward angular light scattering distributions between M1 and M2 macrophages.

The ability to distinguish macrophage subtypes noninvasively could have diagnostic potential in cancer, atherosclerosis, and diabetes, where polarized M1 and M2 macrophages play critical and often opposing roles. Current methods to distinguish macrophage subtypes rely on tissue biopsy. Optical imaging techniques based on light scattering are of interest as they can be translated into biopsy-free strategies. Because mitochondria are relatively strong subcellular light scattering centers, and M2 macrophages are known to have enhanced mitochondrial biogenesis compared to M1, we hypothesized that M1 and M2 macrophages may have different angular light scattering profiles. To test this, we developed an in vitro angle-resolved forward light scattering measurement system. We found that M1 and M2 macrophage monolayers scatter relatively unequal amounts of light in the forward direction between 1.6 deg and 3.2 deg with M2 forward scattering significantly more light than M1 at increasing angles. The ratio of forward scattering can be used to identify the polarization state of macrophage populations in culture.