Physics-informed deep generative learning for quantitative assessment of the retina.

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using Hierarchical Phase-Contrast Tomography (HiP-CT) as part of the Human Organ Atlas Project. HiP-CT, pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering resolution around 20µm/voxel, and enabling highly detailed localized zooms up to 1µm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice scores of 0.9523 and 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline Dice values ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydro-static pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP-CT imaging database.

Multidimensional Analysis of the Adult Human Heart in Health and Disease Using Hierarchical Phase-Contrast Tomography.

Background Current clinical imaging modalities such as CT and MRI provide resolution adequate to diagnose cardiovascular diseases but cannot depict detailed structural features in the heart across length scales. Hierarchical phase-contrast tomography (HiP-CT) uses fourth-generation synchrotron sources with improved x-ray brilliance and high energies to provide micron-resolution imaging of intact adult organs with unprecedented detail. Purpose To evaluate the capability of HiP-CT to depict the macro- to microanatomy of structurally normal and abnormal adult human hearts ex vivo. Materials and Methods Between February 2021 and September 2023, two adult human donor hearts were obtained, fixed in formalin, and prepared using a mixture of crushed agar in a 70% ethanol solution. One heart was from a 63-year-old White male without known cardiac disease, and the other was from an 87-year-old White female with a history of multiple known cardiovascular pathologies including ischemic heart disease, hypertension, and atrial fibrillation. Nondestructive ex vivo imaging of these hearts without exogenous contrast agent was performed using HiP-CT at the European Synchrotron Radiation Facility. Results HiP-CT demonstrated the capacity for high-spatial-resolution, multiscale cardiac imaging ex vivo, revealing histologic-level detail of the myocardium, valves, coronary arteries, and cardiac conduction system across length scales. Virtual sectioning of the cardiac conduction system provided information on fatty infiltration, vascular supply, and pathways between the cardiac nodes and adjacent structures. HiP-CT achieved resolutions ranging from gross (isotropic voxels of approximately 20 µm) to microscopic (approximately 6.4-µm voxel size) to cellular (approximately 2.3-µm voxel size) in scale. The potential for quantitative assessment of features in health and disease was demonstrated. Conclusion HiP-CT provided high-spatial-resolution, three-dimensional images of structurally normal and diseased ex vivo adult human hearts. Whole-heart image volumes were obtained with isotropic voxels of approximately 20 µm, and local regions of interest were obtained with resolution down to 2.3-6.4 µm without the need for sectioning, destructive techniques, or exogenous contrast agents. Published under a CC BY 4.0 license Supplemental material is available for this article. See also the editorial by Bluemke and Pourmorteza in this issue.

Incidence of diarrhea and risk factors among wastewater-irrigating urban farmers along Akaki Rivers in Addis Ababa.

The importance of diarrhea risk associated with wastewater exposure in agriculture is almost unknown. This study aimed to examine diarrheal infection and risk factors among farmers using wastewater for irrigation in Addis Ababa. A cross-sectional study was employed among 380 farmers (190 exposed and 190 unexposed farmers). Among these, a cohort of 200 adults (100 wastewater-exposed, 100 unexposed farmers) aged 25-69 years were selected and followed up biweekly for two successive vegetables growing seasons to examine the incidence of diarrhea. The incidence rate of reported diarrhea was 12 episodes per 1,000 person-weeks at risk and a maximum of 17 episodes per 1,000 person-weeks at risk. Strongly associated risk factors include washing body and clothes with wastewater [AOR: 4.2, 95%CI (1, 11.2)], using working clothes at home [AOR: 4.7, 95%CI (0.25, 87)], and wearing protective cloth [AOR: 5.6, 95 CI (0.3, 70)]. Moreover, hand washing after work, washing feet and boots, and on-site hand washing were significant determinants of farmers' diarrhea. Exposure to wastewater significantly contributes to the risk of diarrhea among adults. Significant determinants of diarrhea among farmers include washing the body and clothes with irrigation water, contamination with soil and irrigation water, and on-site hand washing. Increased risk factors include hand contamination, washing vegetables with irrigation water, eating raw vegetables, and using work clothes at home.

Contribution of wastewater irrigated vegetables to the prevalence of soil-transmitted helminth infection among female farmers in Addis Ababa, Ethiopia.

BACKGROUND: Untreated or inadequately treated wastewater carrying human feces can host helminth eggs and larvae, contaminating the soil and plants that are irrigated with it. In Addis Ababa, farmers use untreated wastewater to grow vegetables; however, there are little data currently available published on vegetables' contribution to the prevalence of helminth among female farmers along the Akaki River, in Addis Ababa, Ethiopia. METHODS: A cross-sectional study was conducted in Addis Ababa City in February 2022. A stratified random sampling method was used to sample farming households. The sample size for each district was determined by a proportional allocation to the total number of households in the area. Two hundred and fifty-two composite vegetable samples and 101 farmers' stool samples were collected and analyzed for helminth prevalence. Data on socio-demographics were collected by trained data collators using a structured questionnaire. Kato-Katz concentration was used to detect STH from a stool sample. Stata version 14.0 was used to process the data. Poisson regression was used to identify the association between STH prevalence in the vegetable and the farm's stool. RESULTS: Helminths were found in 67.5% of vegetables sampled and 20.8% of female farmers' stools. Ascaris lumbricoides eggs (vegetable 48.4% and stool 9.9%) were identified in all analyzed samples. Hookworm eggs (vegetable 13.1% and stool 8.9%) and Trichuris trichiura eggs (vegetable 5.9% and stool 2%) were also isolated. The total number of helminth eggs present in wastewater-irrigated vegetables and female farmers' stool had a positive association (p < 0.05) with a regression coefficient of 1.92 (95% CI = 1.56-2.28). CONCLUSIONS: The study found a significant prevalence of helminth infections, particularly Ascaris lumbricoides, in stool and vegetable samples irrigated with wastewater. A clear association was found between vegetable production and a higher prevalence of helminth infections among female farmers. Therefore, it is important to ensure that farmers are educated in the importance of food washing and sanitation/hygiene practices when using wastewater irrigation for vegetable crops.

A three-dimensional, discrete-continuum model of blood pressure in microvascular networks.

We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.

A closer look at high-energy X-ray-induced bubble formation during soft tissue imaging.

Improving the scalability of tissue imaging throughput with bright, coherent X-rays requires identifying and mitigating artifacts resulting from the interactions between X-rays and matter. At synchrotron sources, long-term imaging of soft tissues in solution can result in gas bubble formation or cavitation, which dramatically compromises image quality and integrity of the samples. By combining in-line phase-contrast imaging with gas chromatography in real time, we were able to track the onset and evolution of high-energy X-ray-induced gas bubbles in ethanol-embedded soft tissue samples for tens of minutes (two to three times the typical scan times). We demonstrate quantitatively that vacuum degassing of the sample during preparation can significantly delay bubble formation, offering up to a twofold improvement in dose tolerance, depending on the tissue type. However, once nucleated, bubble growth is faster in degassed than undegassed samples, indicating their distinct metastable states at bubble onset. Gas chromatography analysis shows increased solvent vaporization concurrent with bubble formation, yet the quantities of dissolved gasses remain unchanged. By coupling features extracted from the radiographs with computational analysis of bubble characteristics, we uncover dose-controlled kinetics and nucleation site-specific growth. These hallmark signatures provide quantitative constraints on the driving mechanisms of bubble formation and growth. Overall, the observations highlight bubble formation as a critical yet often overlooked hurdle in upscaling X-ray imaging for biological tissues and soft materials and we offer an empirical foundation for their understanding and imaging protocol optimization. More importantly, our approaches establish a top-down scheme to decipher the complex, multiscale radiation-matter interactions in these applications.

Age differences in patterns of cannabis use among an online US sample of adults who consume cannabis frequently.

Background: Cannabis use is increasing among middle-aged and older US adults, populations that are particularly vulnerable to the adverse effects of cannabis. Risks for adverse effects differ by cannabis use patterns, which have become increasingly heterogeneous. Nevertheless, little is known about age differences in such patterns.Objective: To investigate age differences in cannabis use patterns, comparing younger (age 18-49), middle-aged (age 50-64), and older adults (age ≥65).Methods: A total of 4,151 US adults with past 7-day cannabis consumption completed an online survey (35.1% male; 60.1% female; 4.8% identified as "other"). Regression models examined age differences in cannabis use patterns.Results: Compared to younger adults, middle-aged and older adults were more likely to consume cannabis during evening hours (50-64: adjusted odds ratio [aOR] = 2.98, 95% CI 2.24-3.96; ≥65: aOR = 4.23, 95 CI 2.82-6.35); by only one method (50-64: aOR = 1.67, 95% CI 1.34-2.09; ≥65: aOR = 3.38, 95 CI 2.24-5.09); primarily by smoking as the only method (50-64: aOR = 1.52, 95% CI 1.29-1.78; ≥65: aOR = 2.12, 95 CI 1.64-2.74); but less likely to consume concentrated cannabis products (concentrates) with extremely high %THC (50-64: aOR = 0.71, 95% CI 0.54-0.93; ≥65: aOR = 0.30, 95 CI 0.16-0.55). Age differences in cannabis use patterns were also observed between middle-aged and older adults.Conclusion: Findings suggest that middle-aged and older adults may engage in less risky cannabis use patterns compared to younger groups (e.g. lower likelihood of consuming highly potent concentrates). However, findings also underscore the importance of recognizing risks unique to these older demographics, such as smoking-related health events. Consequently, prevention strategies targeting such use patterns are needed.

Activated carbon amendment of sand in the base of a permeable pavement reduces total nitrogen and nitrate leaching.

Urban runoff from impermeable surfaces contains various pollutants. Stormwater samples were collected for one year from car parks on the campus of Newcastle University, located in northeast England, to monitor seasonal variation in stormwater properties and leachate quality following stormwater percolation through pilot-scale, outdoor permeable pavements. The pilot study compared an innovative 'pollution munching' permeable pavement with 2 % activated carbon (AC) amendment in the sand base with a conventional, un-amended sand base permeable pavement. Faecal coliforms were detected in stormwater at an average value of 3.75 ± 0.79 log10 CFUs per 100 mL. The permeable pavements without and with AC had mean log removal values of 0.81 ± 0.35 and 0.70 ± 0.35 for these faecal bacteria. The absence of genetic markers for human host associated Bacteroides (HF183) in eleven out of twelve stormwater samples showed that the faecal bacteria were mainly from animal sources. 16S rRNA gene sequencing results confirmed the presence of nitrifying bacteria from the genera Nitrosomonas, Nitrobacter, Nitrosococcus, Nitrospira, and Nitrosospira in stormwater. Nitrification and nitrate leaching was more notable for the conventional permeable pavement and may pose a groundwater pollution risk. Two percent AC amendment of the sand base reduced nitrate and total nitrogen leaching significantly compared with the conventional permeable pavement, by 57 ± 15 % and 40 ± 20 %, respectively. The AC amendment also resulted in significantly reduced Cu and DOC leaching, and lesser accumulation of PAHs by passive samplers embedded in the permeable pavement base. Hydraulic tests showed that the AC amended base layer still met the design specifications for permeable pavements, making it a promising proposition for pollution reduction in Sustainable Drainage Systems (SuDS).

HealthCall: Smartphone Enhancement of Brief Interventions to Improve HIV Medication Adherence Among Patients in HIV Care.

Heavy drinking among people living with HIV (PLWH) reduces ART adherence and worsens health outcomes. Lengthy interventions are not feasible in most HIV care settings, and patients infrequently follow referrals to outside treatment. Utilizing visual and video features of smartphone technology, we developed HealthCall as an electronic means of increasing patient involvement in a brief intervention to reduce drinking and improve ART adherence. The objective of the current study is to evaluate the efficacy of HealthCall to improve ART adherence among PLWH who drink heavily when paired with two brief interventions: the National Institute on Alcoholism and Alcohol Abuse (NIAAA) Clinician's Guide (CG) or Motivational Interviewing (MI). Therefore, we conducted a 1:1:1 randomized trial among 114 participants with alcohol dependence at a large urban HIV clinic. Participants were randomized to one of three groups: (1) CG only (n = 37), (2) CG and HealthCall (n = 38), or (3) MI and HealthCall (n = 39). Baseline interventions targeting drinking reduction and ART adherence were ~ 25 min, with brief (10-15 min) booster sessions at 30 and 60 days. The outcome was ART adherence assessed using unannounced phone pill-count method (possible adherence scores: 0-100%) at 30-day, 60-day, 3, 6, and 12 months. Analyses were conducted using generalized linear mixed models with pre-planned contrasts. Of the 114 enrolled patients, 58% were male, 75% identified as Black/African American, 28% were Hispanic, and 62% had less than a high school education. The mean age was 47.5 years (standard deviation [SD] 10 years) and the mean number of years since they were diagnosed with HIV was 18.6 (SD 7.6). Participants assigned to HealthCall to extend the CG had increased levels of ART adherence at 60-day and 6-month follow-up (compared to CG only), although there was no statistically significant difference by 12-month follow-up. Participants who were assigned to HealthCall to extend the MI never had statistically significant higher levels of ART adherence. These results suggest that the use of a smartphone app can be used to initially extend the reach of a brief drinking intervention to improve ART adherence over a short period of time; however, sustained long-term improvements in ART adherence after intervention activity ends remains a challenge.

Reconstructing microvascular network skeletons from 3D images: What is the ground truth?

Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimer's disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours such as blood flow or transport processes. Extraction of 3D networks from imaging data broadly consists of two image processing steps: segmentation followed by skeletonisation. Much research effort has been devoted to segmentation field, and there are standard and widely-applied methodologies for creating and assessing gold standards or ground truths produced by manual annotation or automated algorithms. The Skeletonisation field, however, lacks widely applied, simple to compute metrics for the validation or optimisation of the numerous algorithms that exist to extract skeletons from binary images. This is particularly problematic as 3D imaging datasets increase in size and visual inspection becomes an insufficient validation approach. In this work, we first demonstrate the extent of the problem by applying 4 widely-used skeletonisation algorithms to 3 different imaging datasets. In doing so we show significant variability between reconstructed skeletons of the same segmented imaging dataset. Moreover, we show that such a structural variability propagates to simulated metrics such as blood flow. To mitigate this variability we introduce a new, fast and easy to compute super metric that compares the volume, connectivity, medialness, bifurcation point identification and homology of the reconstructed skeletons to the original segmented data. We then show that such a metric can be used to select the best performing skeletonisation algorithm for a given dataset, as well as to optimise its parameters. Finally, we demonstrate that the super metric can also be used to quickly identify how a particular skeletonisation algorithm could be improved, becoming a powerful tool in understanding the complex implication of small structural changes in a network.

Use of Lysergic Acid Diethylamide by Major Depression Status.

Importance: Renewed interest in the clinical potential of hallucinogens may lead people with depression to a generally more positive view of the use of lysergic acid diethylamide (LSD). Therefore, past-year LSD use among people with depression may be increasing in prevalence. Objective: To assess time trends in the prevalence of past-year nonmedical LSD use by past-year major depression status and the variation in this association by sociodemographic characteristics. Design, Setting, and Participants: This survey study used pooled publicly available data from 478 492 adults aged 18 years or older who were administered the National Survey on Drug Use and Health from 2008 through 2019. Statistical analysis was conducted from December 2022 to June 2023. Main Outcome and Measures: Past-year major depression diagnoses per criteria from the DSM-IV were analyzed. Logistic regression models examined whether time trends in past-year nonmedical LSD use differed between adults with vs without past-year depression, adjusting for sociodemographic characteristics. Secondary analyses examined whether the trends in LSD use by depression status differed between sociodemographic subgroups. Results: The analytic sample included 478 492 adults, of whom 51.8% were female, 56.1% were younger than 50 years, 11.7% were Black, 15.1% were Hispanic, 65.8% were White, and 7.5% were another race. Weighted interview response rates ranged from 64.9% to 75.6% during the study time frame. From 2008 to 2019, past-year use of LSD increased significantly more among adults with major depression (2008 prevalence, 0.5%; 2019 prevalence, 1.8%; prevalence difference [PD], 1.3% [95% CI, 1.0%-1.6%]) compared with adults without major depression (2008 prevalence, 0.2%; 2019 prevalence, 0.8%; PD, 0.6% [95% CI, 0.5%-0.7%]) (difference in difference, 0.8% [95% CI, 0.5%-1.1%]). This difference was particularly pronounced among young adults aged 34 years or younger (PD among those aged 18-25 years with depression, 3.3% [95% CI, 2.5%-4.2%]; PD among those aged 26-34 years with depression, 2.7% [95% CI, 1.6%-3.8%]) and individuals with incomes less than $75 000 per year (PD among those with income <$20 000, 1.9% [95% CI, 1.3%-2.6%]; PD among those with income $20 000-$49 999, 1.5% [95% CI, 1.0%-2.1%]; PD among those with income $50 000-$74 999, 1.3% [95% CI, 0.7%-2.0%]). Conclusions and Relevance: This study suggests that, from 2008 to 2019, there was a disproportionate increase in the prevalence of past-year LSD use among US adults with past-year depression. Among those with depression, this increase was particularly strong among younger adults and those with lower household incomes. Among individuals with depression who also report LSD use, clinicians should discuss potential strategies for mitigating harm and maximizing benefits in medically unsupervised settings.

Evaluating age-related disparities in cannabis-related problems among LGBT+ versus non-LGBT+ adults.

LGBT+ adults demonstrate greater cannabis-related problems (e.g., Cannabis Use Disorder [CUD]) compared to non-LGBT+ counterparts. No study has explored age-related disparities in cannabis problems across the adult lifespan, nor have studies identified specific CUD criteria that contribute to elevated CUD among LGBT+ adults. The purpose of this study was to examine associations between LGBT+ identity and age with endorsement of CUD criteria in a sample of regular cannabis consumers. An online sample of N = 4334 (25.1% LGBT+) adults aged 18-64 residing in the U.S. completed an online survey about cannabis use behaviors and CUD diagnostic criteria. Bivariate contrasts revealed significantly greater CUD criteria endorsement among LGBT+ respondents, largely driven by differences at younger ages. However, this effect disappeared in the majority of adjusted logistic regression models. LGBT+ identity was associated with greater probability of use in larger amounts (adjOR = 2.10, 95% CI: 1.22-3.60) and use despite physical/mental health problems (adjOR = 2.51, 95% CI:1.23-5.03). No age*LGBT+ identity interactions were detected. Plotted trends depict more pronounced disparities in outcomes among LGBT+ adults under 35 years. Several potential risk and protective factors including employment, education, and reasons for use were identified. There were age-related differences in these characteristics among LGBT+ and non-LGBT+ respondents. Initial findings highlight the need for LGBT+ research examining trends in health outcomes and sociodemographic and cannabis characteristics across the lifespan. The study also provides a substantive contribution regarding specific cannabis-related problems that young LGBT+ cannabis consumers may be more likely to endorse than their non-LGBT+ counterparts.

Prevalence and risk factors of childhood diarrhea among wastewater irrigating urban farming households in Addis Ababa.

INTRODUCTION: Childhood diarrhea is one of the major contributors to the morbidity of under-five children in Ethiopia. Although researchers determine the risk factors varyingly, the exposure route to the pathogens is usually complicated. This study aims to investigate the prevalence and risk factors of diarrhea among children under the age of five among wastewater irrigation farming households in Addis Ababa, Ethiopia. METHODS: Cross-sectional study was conducted among 402 farming households from November 2021 to February 2022. Data was collected using a face-to-face interviewer-administered questionnaire. Stata version 14 software was used to analyze data. Factors associated with the prevalence of diarrhea was identified using binary logistic regression. Multivariable analysis was carried out to determine an adjusted odds ratio at a confidence level of 95% and level of significance at 0.05. RESULTS: The overall prevalence of under-five children diarrheal cases was 22.3%. The odds of diarrhea are associated with a multitude of variables. Major wastewater-related determinants associated with diarrhea are body washing with irrigation water [AOR: 37.7, 95%CI (3.1, 358)], contaminated cloth with irrigation water [AOR:10.8,95%CI(0.6, 205)], use of protective clothing during farm work [28.9,95%CI (3.9, 215)], use of farm work cloths at home [AOR: 31.7, 95%CI (4.4, 226)], and bringing unwashed farm tools to home [94 (5.7, 1575)]. CONCLUSION: The high prevalence of under-five children diarrheal disease among wastewater irrigation households was strongly associated with factors related to occupational exposure. Thus, to decrease childhood diarrheal among urban agriculture farmers, appropriate precautions need to be taken.

Distribution of helminth eggs in environmental and stool samples of farming households along Akaki River in Addis Ababa, Ethiopia.

BACKGROUND: Helminth infections are a public health issue in countries with poor sanitation facilities. However, there little information on the epidemiological association between helminths in wastewater and soil samples and rates of helminth infection among farming households along the Akaki River in Addis Ababa, Ethiopia. METHODS: A cross-sectional study was conducted between November 2021 and February 2022. A stratified random sampling technique was used to select farming households. The sample size for each district was determined by a proportionate allocation to the number of households. From wastewater-irrigated farms, 70 wastewater samples, 28 soil samples, and 86 farmers' stool samples were collected and analyzed for helminths. A questionnaire was used to gather ethnographic data, about farming households, whereas wastewater and soil sample analysis was used to generate quantitative data on helminth loads. The data were systematically analysed by developing themes, and bias evaluated using triangulation validation methodologies. Potential pathways to helminth infection were evaluated by measuring. Total number of helminth eggs in wastewater, soil samples and farmer's stools was investigated using Poisson regression. RESULTS: In this study, 82.9% of wastewater samples, 57.1% of soil samples, and 18.6% of farmers' stool samples contained helminth eggs. The most prevalent helminth was Ascaris lumbricoides in all samples (wastewater 67%, soil 25%, and stool 10.5%), followed by hookworm (wastewater 10%, soil 21.4%, and stool 6.9%) and Trichuris trichiura eggs (wastewater 5.7%, soil 10.7%, and stool 1.2%). There was a positive association between the total number of helminth eggs in wastewater and soil samples with counts in farmers' stool. The Poisson regression coefficients for wastewater and soil were, 1.63 (95% CI = 1.34-1.92) and 1.70 (95% CI = 1.39-2.01), (p < 0.05). CONCLUSIONS: This research has shown a clear association between the total helminth eggs in wastewater and soil samples and farmer stools along the Akaki River. Therefore, an integrated approach is essential to address the issue in this area and prevent the spread of further helminth infections.

Exploring survey methods for measuring consumption quantities of cannabis flower and concentrate products.

Background: Researchers need accurate measurements of cannabis consumption quantities to assess risks and benefits. Survey methods for measuring cannabis flower and concentrate quantities remain underdeveloped.Objective: We examined "grams" and "hits" units for measuring flower and concentrate quantities, and calculating milligrams of THC (mgTHC).Methods: Online survey participants (n = 2,381) reported preferred unit (hits or grams), past-week hits and grams for each product, and product %THC. Quantile regression compared mgTHC between unit-preference subgroups. Hits-based mgTHC calculations assumed a universal grams-per-hit ratio (GPHR). To examine individualized GPHRs, we tested a "two-item approach," which divided total grams by total hits, and "one-item approach," which divided 0.5 grams by responses to the question: "How many total hits would it take you to finish 1/2 g of your [product] by [administration method]?"Results: Participants were primarily daily consumers (77%), 50% female sex, mean age 39.0 (SD 16.4), 85% White, 49% employed full-time. Compared to those who preferred the hits unit, those who preferred the grams unit reported consuming more hits and grams, higher %THC products, and consequently, larger median mgTHC (flower-hits mgTHC: 32 vs. 91 (95%CI: 52-67); flower-grams mgTHC: 27 vs. 113 (95%CI: 73-95); concentrate-hits mgTHC: 29 vs. 59 (95%CI: 15-43); concentrate-grams mgTHC: 61 vs. 129 (95%CI: 43-94)). "Two-item" and "one-item" approach GPHRs were similar and frequently 50% larger or smaller than the universal GPHR.Conclusion: Allowing respondents to choose "hits" or "grams" when reporting cannabis quantities does not compromise mgTHC estimates. A low-burden, one-item approach yields individualized "hit sizes" that may improve mgTHC estimates.

Extending Gender- and Sex-Based Analyses in Cannabis Research: Findings from an Online Sample of Gender Diverse Young Adult Consumers.

Background: Gender and sex can influence cannabis behaviors and consequences (Cannabis Use Disorder [CUD]). Research typically examines sex and gender independently. Gender analyses often exclude transgender and gender diverse (TGD) populations. The objectives of this study were to (a) replicate less frequent cannabis use among TGD young adults compared to cisgender counterparts (b) compare severity of CUD, and (c) examine the role of sex on cannabis outcomes. Method: Online survey participants between 18 and 34 (N=1213) from the United States who reported past-week cannabis consumption provided information on cannabis practices and CUD from February to April 2022. Bivariate analyses explored gender differences across frequency (daily frequency across routes of administration [ROAs]; daily use of 2+ ROAs, use throughout the day) and CUD. Adjusted regression models provided model-estimated marginal probabilities and means to examine differences across four gender-by-sex categories (cisgender men: n=385; cisgender women: n=681; male-at-birth TGD: n=26; female-at-birth TGD: n=121). Benjamini-Hochberg adjustments (10% false discovery rate) were applied. Results: Among past-week consumers, female-at-birth TGD participants demonstrated lower probability of daily flower smoking compared to cisgender men (0.54 vs. 0.67). Cisgender men reported greater probability of daily concentrate vaping (0.55) compared to cisgender women (0.45) and female-at-birth TGD participants (0.27); they were also more likely to report daily use of 2+ ROAs (cisgender men: 0.51 vs. cisgender women: 0.39 and female at-birth TGD: 0.27). TGD participants reported greater CUD severity compared to cisgender counterparts, t(1096)=-3.69, p=0.002. Model-estimated means found lower severity among cisgender women compared to cisgender men and female-at-birth TGD participants. Stratified regression models support positive associations between daily cannabis use and CUD in both TGD in cisgender groups. Among cisgender participants, greater severity was predicted by male sex, younger age, and younger age of onset. Conclusions: The present study replicates and extends a prior finding that among past-week cannabis consumers, TGD young adults report less frequent use than cisgender counterparts. Despite this, TGD participants demonstrated greater severity of CUD. While analyses were limited by the small sample of male-at-birth TGD participants, the article highlights the importance of expanding sex- and gender-focused analyses. Future work is expanding efforts to target hard-to-reach consumers.

Fecal Contamination in the Wastewater Irrigation System and its Health Threat to Wastewater-Based Farming Households in Addis Ababa, Ethiopia.

Due to rapidly growing demand, the production of vegetables is increasing along the Akaki Rivers. The objective of this study was to examine the degree of fecal contamination and levels of fecal contamination and dissemination throughout the wastewater irrigation system. Irrigation water, irrigated soil, and leafy vegetables were collected twice during 2 vegetable growing seasons, at the maturity period of the growing season, from 19 sampling points along the 2 Akaki Rivers. Composite samples were taken from all sampling points and E.coli was enumerated. The mean E.coli load in wastewater and non-wastewater sources were 1.16±5.53 CFU/100 ml and 2.232±1.292 CFU/100 ml respectively. All counts of E. coli in the wastewater exceeded the WHO's standards indicating that the irrigation water quality was unacceptable. In the wastewater-irrigated and non-wastewater-irrigated soil, the mean E.coli were 3.62 ±1.582 CFU/g and 1.322±87.1 CFU/g respectively. Meanwhile, the mean E.coli counts on the lettuce and Swiss chard were 78 ± 2 CFU/g and 44 ±3CFU/g respectively. The E.coli count on the leafy vegetables was found to be associated with the E.coli in the wastewater and soil. The production of leafy vegetables using wastewater with unacceptably high levels of E.coli and high occupational exposure introduces high levels of risk to the farming communities and to the consumers. Leafy, low-growing raw edible vegetables need careful treatment during food production and harvesting procedures or activities.