Nicotinamide for Skin-Cancer Chemoprevention in Transplant Recipients.

BACKGROUND: Immunosuppressed organ-transplant recipients have an increased incidence of, and mortality from, skin cancer. Nicotinamide (vitamin B3) enhances the repair of ultraviolet (UV) radiation-induced DNA damage, reduces the cutaneous immunosuppressive effects of UV radiation, and reduces the incidence of keratinocyte cancers (including squamous-cell and basal-cell carcinomas) and actinic keratoses among high-risk immunocompetent patients. Whether oral nicotinamide is useful for skin-cancer chemoprevention in organ-transplant recipients is unclear. METHODS: In this phase 3 trial, we randomly assigned, in a 1:1 ratio, organ-transplant recipients who had had at least two keratinocyte cancers in the past 5 years to receive 500 mg of nicotinamide or placebo twice daily for 12 months. Participants were examined for skin lesions by dermatologists at 3-month intervals for 12 months. The primary end point was the number of new keratinocyte cancers during the 12-month intervention period. Secondary end points included the numbers of squamous-cell and basal-cell carcinomas during the 12-month intervention period, the number of actinic keratoses until 6 months after randomization, safety, and quality of life. RESULTS: A total of 158 participants were enrolled, with 79 assigned to the nicotinamide group and 79 to the placebo group. The trial was stopped early owing to poor recruitment. At 12 months, there were 207 new keratinocyte cancers in the nicotinamide group and 210 in the placebo group (rate ratio, 1.0; 95% confidence interval, 0.8 to 1.3; P = 0.96). No significant between-group differences in squamous-cell and basal-cell carcinoma counts, actinic keratosis counts, or quality-of-life scores were observed. Adverse events and changes in blood or urine laboratory variables were similar in the two groups. CONCLUSIONS: In this 12-month, placebo-controlled trial, oral nicotinamide therapy did not lead to lower numbers of keratinocyte cancers or actinic keratoses in immunosuppressed solid-organ transplant recipients. (Funded by the National Health and Medical Research Council; ONTRANS Australian New Zealand Clinical Trials Registry number, ACTRN12617000599370.).

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

Similarity-based multimodal regression.

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

Population and contact tracer uptake of New Zealand's QR-code-based digital contact tracing app for COVID-19.

This study aimed to understand the population and contact tracer uptake of the quick response (QR)-code-based function of the New Zealand COVID Tracer App (NZCTA) used for digital contact tracing (DCT). We used a retrospective cohort of all COVID-19 cases between August 2020 and February 2022. Cases of Asian and other ethnicities were 2.6 times (adjusted relative risk (aRR) 2.58, 99 per cent confidence interval (95% CI) 2.18, 3.05) and 1.8 times (aRR 1.81, 95% CI 1.58, 2.06) more likely than Maori cases to generate a token during the Delta period, and this persisted during the Omicron period. Contact tracing organization also influenced location token generation with cases handled by National Case Investigation Service (NCIS) staff being 2.03 (95% CI 1.79, 2.30) times more likely to generate a token than cases managed by clinical staff at local Public Health Units (PHUs). Public uptake and participation in the location-based system independent of contact tracer uptake were estimated at 45%. The positive predictive value (PPV) of the QR code system was estimated to be close to nil for detecting close contacts but close to 100% for detecting casual contacts. Our paper shows that the QR-code-based function of the NZCTA likely made a negligible impact on the COVID-19 response in New Zealand (NZ) in relation to isolating potential close contacts of cases but likely was effective at identifying and notifying casual contacts.

Pevonedistat, a first-in-class NEDD8-activating enzyme inhibitor, sensitizes cancer cells to VSVΔ51 oncolytic virotherapy.

The clinical efficacy of VSVΔ51 oncolytic virotherapy has been limited by tumor resistance to viral infection, so strategies to transiently repress antiviral defenses are warranted. Pevonedistat is a first-in-class NEDD8-activating enzyme (NAE) inhibitor currently being tested in clinical trials for its antitumor potential. In this study, we demonstrate that pevonedistat sensitizes human and murine cancer cells to increase oncolytic VSVΔ51 infection, increase tumor cell death, and improve therapeutic outcomes in resistant syngeneic murine cancer models. Increased VSVΔ51 infectivity was also observed in clinical human tumor samples. We further identify the mechanism of this effect to operate via blockade of the type 1 interferon (IFN-1) response through neddylation-dependent interferon-stimulated growth factor 3 (ISGF3) repression and neddylation-independent inhibition of NF-κB nuclear translocation. Together, our results identify a role for neddylation in regulating the innate immune response and demonstrate that pevonedistat can improve the therapeutic outcomes of strategies using oncolytic virotherapy.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Perioperative intravenous dexamethasone for patients undergoing colorectal surgery: a systematic review and meta-analysis.

PURPOSE: Dexamethasone is a glucocorticoid that is often administered intraoperatively as prophylaxis for postoperative nausea and vomiting (PONV). Several randomized controlled trials (RCTs) have examined its use in colorectal surgery. This systematic review aims to assess the postoperative impacts of dexamethasone use in colorectal surgery. METHODS: MEDLINE, Embase, and CENTRAL were searched from database inception to January 2023. Articles were included if they compared perioperative intravenous dexamethasone to a control group in patients undergoing elective colorectal surgery in terms of postoperative morbidity. The primary outcomes were prolonged postoperative ileus (PPOI) and PONV. Secondary outcomes included postoperative infectious morbidity and return of bowel function. A pair-wise meta-analysis and GRADE assessment of the quality of evidence were performed. RESULTS: After reviewing 3476 relevant citations, seven articles (five RCTs, two retrospective cohorts) met the inclusion criteria. Overall, 1568 patients received perioperative dexamethasone and 1459 patients received a control. Patients receiving perioperative dexamethasone experienced significantly less PPOI based on moderate-quality evidence (three studies, OR 0.46, 95%CI 0.28-0.74, p < 0.01). Time to first flatus was significantly reduced with intravenous dexamethasone. There was no difference between groups in terms of PONV (four studies, OR 0.90, 95%CI 0.64-1.27, p = 0.55), postoperative morbidity (OR 0.93, 95%CI 0.63-1.39, p = 0.74), or rate of postoperative infectious complications (seven studies, OR 0.74, 95%CI 0.55-1.01, p = 0.06). CONCLUSION: This review presents moderate-quality evidence that perioperative intravenous dexamethasone may reduce PPOI and enhance the return of bowel function following elective colorectal surgery. There was no significant observed effect on PONV or postoperative infectious complications.

Age-Dependent Electroencephalogram Features in Infants Under Spinal Anesthesia Appear to Mirror Physiologic Sleep in the Developing Brain: A Prospective Observational Study.

BACKGROUND: Infants under spinal anesthesia appear to be sedated despite the absence of systemic sedative medications. In this prospective observational study, we investigated the electroencephalogram (EEG) of infants under spinal anesthesia and hypothesized that we would observe EEG features similar to those seen during sleep. METHODS: We computed the EEG power spectra and spectrograms of 34 infants undergoing infraumbilical surgeries under spinal anesthesia (median age 11.5 weeks postmenstrual age, range 38-65 weeks postmenstrual age). Spectrograms were visually scored for episodes of EEG discontinuity or spindle activity. We characterized the relationship between EEG discontinuity or spindles and gestational age, postmenstrual age, or chronological age using logistic regression analyses. RESULTS: The predominant EEG patterns observed in infants under spinal anesthesia were slow oscillations, spindles, and EEG discontinuities. The presence of spindles, observed starting at about 49 weeks postmenstrual age, was best described by postmenstrual age ( P =.002) and was more likely with increasing postmenstrual age. The presence of EEG discontinuities, best described by gestational age ( P = .015), was more likely with decreasing gestational age. These age-related changes in the presence of spindles and EEG discontinuities in infants under spinal anesthesia generally corresponded to developmental changes in the sleep EEG. CONCLUSIONS: This work illustrates 2 separate key age-dependent transitions in EEG dynamics during infant spinal anesthesia that may reflect the maturation of underlying brain circuits: (1) diminishing discontinuities with increasing gestational age and (2) the appearance of spindles with increasing postmenstrual age. The similarity of these age-dependent transitions under spinal anesthesia with transitions in the developing brain during physiological sleep supports a sleep-related mechanism for the apparent sedation observed during infant spinal anesthesia.

Identification of FDA-approved bifonazole as a SARS-CoV-2 blocking agent following a bioreporter drug screen.

We established a split nanoluciferase complementation assay to rapidly screen for inhibitors that interfere with binding of the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein with its target receptor, angiotensin-converting enzyme 2 (ACE2). After a screen of 1,200 US Food and Drug Administration (FDA)-approved compounds, we identified bifonazole, an imidazole-based antifungal agent, as a competitive inhibitor of RBD-ACE2 binding. Mechanistically, bifonazole binds ACE2 around residue K353, which prevents association with the RBD, affecting entry and replication of spike-pseudotyped viruses as well as native SARS-CoV-2 and its variants of concern (VOCs). Intranasal administration of bifonazole reduces lethality in K18-hACE2 mice challenged with vesicular stomatitis virus (VSV)-spike by 40%, with a similar benefit after live SARS-CoV-2 challenge. Our screen identified an antiviral agent that is effective against SARS-CoV-2 and VOCs such as Omicron that employ the same receptor to infect cells and therefore has high potential to be repurposed to control, treat, or prevent coronavirus disease 2019 (COVID-19).

Clinical observation of tear film breakup time with a novel smartphone-attachable technology.

PURPOSE: To demonstrate the practicability of a portable instrument in assessing tear film breakup time (TFBUT): a lens attachment for smartphones (LAS). METHODS: By applying LAS in combination with the iPhone 12 pro, and a recordable slit-lamp microscope, we obtained TFBUT videos from 58 volunteers. The comparison between the conventional slit-lamp microscope and LAS by an experienced ophthalmologist. Moreover, we also invited an ophthalmic postgraduate student and an undergraduate student with no clinical experience to assess TFBUT in those videos. The inter-observer reliability was assessed using intraclass correlation coefficients (ICC). RESULTS: The TFBUT of 116 eyes in total was recorded. Reliability indexes were adequate. The Spearman's correlation analysis and the intraclass correlation coefficient suggested a strong correlation between the 2 modalities (Right eye: Spearman's r = 0.929, 95% confidence interval (CI) = 0.847-0.963, ICC = 0.978, p < 0.001; Left eye: Spearman's r = 0.931, 95% CI = 0.866-0.964, ICC = 0.985, p < 0.001;). Between instruments, the majority of TFBUT measurements showed good agreement on Bland Altman plot. A high concordance was observed in TFBUT, when assessed by an ophthalmologist and an ophthalmic postgraduate student (Left eye: LAS ICC = 0.951, p < 0.001; Left eye: slit-lamp microscope ICC = 0.944, p < 0.001). CONCLUSIONS: Compared with the conventional slit-lamp microscope, the LAS has sufficient validity for evaluating TFBUT in clinics or at home.

Modeling of Ultrasound Stimulation of Adolescent Pancreas for Insulin Release Therapy.

OBJECTIVES: Our previous published studies have focused on safety and effectiveness of using therapeutic ultrasound (TUS) for treatment of type 2 diabetes mellitus (T2DM) in preclinical models. Here we present a set of simulation studies to explore potential ultrasound application schemes that would be feasible in a clinical setting. METHODS: Using the multiphysics modeling tool OnScale, we created two-dimensional (2D) models of the human abdomen from CT images captured from one normal weight adolescent patient, and one obese adolescent patient. Based on our previous studies, the frequency of our TUS was 1 MHz delivered from a planar unfocused transducer. We tested five different insonation angles, as well as four ultrasound intensities combined with four different duty factors and five durations of application to explore how these variables effect the peak pressure and temperature delivered to the pancreas as well as surrounding tissue in the model. RESULTS: We determined that ultrasound applied directly from the anterior of the patient abdomen at 5 W/cm2 delivered consistent acoustic pressures to the pancreas at the levels which we have previously found to be effective at inducing an insulin release from preclinical models. CONCLUSIONS: Our modeling work indicates that it may be feasible to non-invasively apply TUS in clinical treatment of T2DM.

Epistatic interactions between PHOTOPERIOD1, CONSTANS1 and CONSTANS2 modulate the photoperiodic response in wheat.

In Arabidopsis, CONSTANS (CO) integrates light and circadian clock signals to promote flowering under long days (LD). In the grasses, a duplication generated two paralogs designated as CONSTANS1 (CO1) and CONSTANS2 (CO2). Here we show that in tetraploid wheat plants grown under LD, combined loss-of-function mutations in the A and B-genome homeologs of CO1 and CO2 (co1 co2) result in a small (3 d) but significant (P<0.0001) acceleration of heading time both in PHOTOPERIOD1 (PPD1) sensitive (Ppd-A1b, functional ancestral allele) and insensitive (Ppd-A1a, functional dominant allele) backgrounds. Under short days (SD), co1 co2 mutants headed 13 d earlier than the wild type (P<0.0001) in the presence of Ppd-A1a. However, in the presence of Ppd-A1b, spikes from both genotypes failed to emerge by 180 d. These results indicate that CO1 and CO2 operate mainly as weak heading time repressors in both LD and SD. By contrast, in ppd1 mutants with loss-of-function mutations in both PPD1 homeologs, the wild type Co1 allele accelerated heading time >60 d relative to the co1 mutant allele under LD. We detected significant genetic interactions among CO1, CO2 and PPD1 genes on heading time, which were reflected in complex interactions at the transcriptional and protein levels. Loss-of-function mutations in PPD1 delayed heading more than combined co1 co2 mutations and, more importantly, PPD1 was able to perceive and respond to differences in photoperiod in the absence of functional CO1 and CO2 genes. Similarly, CO1 was able to accelerate heading time in response to LD in the absence of a functional PPD1. Taken together, these results indicate that PPD1 and CO1 are able to respond to photoperiod in the absence of each other, and that interactions between these two photoperiod pathways at the transcriptional and protein levels are important to fine-tune the flowering response in wheat.

Privacy-preserving harmonization via distributed ComBat.

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

Harmonizing functional connectivity reduces scanner effects in community detection.

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

γ C-H Functionalization of Amines via Triple H-Atom Transfer of a Vinyl Sulfonyl Radical Chaperone.

A selective, remote desaturation has been developed to rapidly access homoallyl amines from their aliphatic precursors. The strategy employs a triple H-atom transfer (HAT) cascade, entailing (i) cobalt-catalyzed metal-HAT (MHAT), (ii) carbon-to-carbon 1,6-HAT, and (iii) Co-H regeneration via MHAT. A new class of sulfonyl radical chaperone (to rapidly access and direct remote, radical reactivity) enables remote desaturation of diverse amines, amino acids, and peptides with excellent site-, chemo-, and regioselectivity. The key, enabling C-to-C HAT step in this cascade was computationally designed to satisfy both thermodynamic (bond strength) and kinetic (polarity) requirements, and it has been probed via regioselectivity, isomerization, and competition experiments. We have also interrupted this radical transfer dehydrogenation to achieve γ-selective C-Cl, C-CN, and C-N bond formations.

Mitigating site effects in covariance for machine learning in neuroimaging data.

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.

Wheat VRN1, FUL2 and FUL3 play critical and redundant roles in spikelet development and spike determinacy.

The spikelet is the basic unit of the grass inflorescence. In this study, we show that wheat MADS-box genes VRN1, FUL2 and FUL3 play critical and redundant roles in spikelet and spike development, and also affect flowering time and plant height. In the vrn1ful2ful3-null triple mutant, the inflorescence meristem formed a normal double-ridge structure, but then the lateral meristems generated vegetative tillers subtended by leaves instead of spikelets. These results suggest an essential role of these three genes in the fate of the upper spikelet ridge and the suppression of the lower leaf ridge. Inflorescence meristems of vrn1ful2ful3-null and vrn1ful2-null remained indeterminate and single vrn1-null and ful2-null mutants showed delayed formation of the terminal spikelet and increased number of spikelets per spike. Moreover, the ful2-null mutant showed more florets per spikelet, which together with a higher number of spikelets, resulted in a significant increase in the number of grains per spike in the field. Our results suggest that a better understanding of the mechanisms underlying wheat spikelet and spike development can inform future strategies to improve grain yield in wheat.

Defining Properly Collected Urine: Thresholds to Improve the Accuracy of Urinalysis for Microscopic Hematuria Evaluation in Women.

PURPOSE: Microscopic hematuria is one of the most common office consults for urologists. While revised guidelines have risk-stratified patients to reduce unnecessary screening, they do not provide guidance concerning specimen quality. We sought to define "properly collected" specimens using catheterized urine samples as a reference to improve the utility of hematuria screening in women. MATERIALS AND METHODS: We prospectively acquired same-visit voided and catheterized urine samples from 46 women referred for microscopic hematuria from September 2016 to March 2020. Characteristics of pre-referral urinalysis were compared to the matched specimens. True microscopic hematuria was defined as ≥3 red blood cells per high power field on catheterization. RESULTS: Catheterized urinalyses had significantly fewer red blood and squamous epithelial cells in comparison to both referral urinalyses (p=0.006, p=0.001, respectively) and same-day void urinalyses (p=0.02, p=0.04, respectively). As no catheterized sample had >2 squamous epithelial cells, we applied this squamous epithelial cell threshold to referral urinalyses for analysis. Addition of this criterion for "properly collected specimen" increased the positive predictive value of referral urinalyses from 46.1% to 68.8% for true microscopic hematuria. Fewer than 2 squamous epithelial cells with elevated RBC was a significant predictor for true microscopic hematuria (p=0.003). CONCLUSIONS: Voided specimens in the urology clinic had significantly lower red blood cells than referral samples, indicating improved collection technique may reduce false positive urinalyses. Matched collection suggested that repeat collection by catheterization in women who present with >2 squamous epithelial cells per high power field on referral urinalysis may prevent unnecessary future work-up.