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Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Therapeutic options for PDAC are primarily restricted to surgery in the early stages of the disease or chemotherapy in advanced disease. Only a subset of patients with germline defects in BRCA1/2 genes can potentially benefit from personalized therapy, with the PARP inhibitor olaparib serving as a maintenance treatment for metastatic disease. Although the role of radiotherapy in PDAC remains controversial, the use of radiosensitizers offers hope for improving cancer management. Previously, we have shown that damage-specific DNA binding protein 2 (DDB2) is a potential prognostic and predictive biomarker for chemotherapy response in PDAC. In this study, we investigated the function of DDB2 in radiotherapy response, with and without radiosensitization by olaparib in PDAC cells. Our findings demonstrated DDB2 resistance to radiation effects, thereby improving cell survival and enhancing the repair of ionizing radiation-induced DNA double-strand breaks. We observed that DDB2 expression enhances the cell cycle arrest in the G2 phase by phosphorylating Chk1 and Chk2 cell cycle checkpoints. Additionally, we identified a novel link between DDB2 and PARP1 in the context of radiotherapy, which enhances the expression and activity of PARP1. Our findings highlight the potential of low-DDB2 expression to potentiate the radiosensitization effect of olaparib in PDAC cells. Collectively, this study provides novel insights into the impacts of DDB2 in the radiotherapy response in PDAC, enabling its employment as a potential biomarker to predict resistance to radiation. Furthermore, DDB2 represents a significant step forward in precision radiotherapy by widening the scope of patients who can be benefiting from olaparib as a radiosensitizer. Hence, this research has the potential to enrich the limited use of radiotherapy in the care of patients with PDAC.

Abstract The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [18F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [18F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [18F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [18F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. Additionally, BAT volumes computed from the segmentations distinguish the subjects with and without active BAT with an AUC of 0.8, compared to 0.6 for CT thresholding. These findings suggest CNNs can facilitate large-scale imaging studies more efficiently and cost-effectively using only CT.

Abstract Background Extreme Risk Protection Orders (ERPOs) are civil court orders that prohibit firearm purchase and possession when someone is behaving dangerously and is at risk of harming themselves and/or others. As of June 2024, ERPOs are available in 21 states and the District of Columbia to prevent firearm violence. This paper describes the design and protocol of a six-state study of ERPO use. Methods The six states included are California, Colorado, Connecticut, Florida, Maryland, and Washington. During the 3-year project period (2020–2023), ERPO case files were obtained through public records requests or through agreements with agencies with access to these data in each state. A team of over four dozen research assistants from seven institutions coded 6628 ERPO cases, abstracting 80 variables per case under domains related to respondent characteristics, events and behaviors leading to ERPO petitions, petitioner types, and court outcomes. Research assistants received didactic training through an online learning management system that included virtual training modules, quizzes, practice coding exercises, and two virtual synchronous sessions. A protocol for gaining strong interrater reliability was used. Research assistants also learned strategies for reducing the risk of experiencing secondary trauma through the coding process, identifying its occurrence, and obtaining help. Discussion Addressing firearm violence in the U.S. is a priority. Understanding ERPO use in these six states can inform implementation planning and ERPO uptake, including promising opportunities to enhance safety and prevent firearm-related injuries and deaths. By publishing this protocol, we offer detailed insight into the methods underlying the papers published from these data, and the process of managing data abstraction from ERPO case files across the multi-state and multi-institution teams involved. Such information may also inform future analyses of this data, and future replication efforts. Registration This protocol is registered on Open Science Framework ( https://osf.io/kv4fc/ ).

Abstract Cognitive behavioral therapy improves psychosocial outcomes in rheumatoid arthritis (RA), but access is limited. We conducted a randomized controlled trial to evaluate the efficacy of a cognitive-behavioral digital therapeutic, reclarit, on psychosocial outcomes in adult RA patients with impaired health-related quality of life. Participants were randomized to reclarit plus treatment as usual (TAU) or TAU plus educational and informational material (active control). The primary outcome was SF-36 mental (MCS) and physical (PCS) component summary scores at 3 months, with additional assessments at 6 months. reclarit significantly improved SF-36 MCS scores compared to control (mean difference 3.3 [95% CI 0.7, 5.9]; p = 0.014), with high user satisfaction and sustained improvements at 6 months. Depression, anxiety, fatigue, and social/work functioning also improved significantly, while SF-36 PCS, pain, and disability scores did not differ. In conclusion, reclarit offers immediate, effective, evidence-based and personalized psychological support for RA patients.

Abstract Most plants in natural ecosystems associate with arbuscular mycorrhizal (AM) fungi to survive soil nutrient limitations. To engage in symbiosis, AM fungi secrete effector molecules that, similar to pathogenic effectors, reprogram plant cells. Here we show that the Glomeromycotina-specific SP7 effector family impacts on the alternative splicing program of their hosts. SP7-like effectors localize at nuclear condensates and interact with the plant mRNA processing machinery, most prominently with the splicing factor SR45 and the core splicing proteins U1-70K and U2AF35. Ectopic expression of these effectors in the crop plant potato and in Arabidopsis induced developmental changes that paralleled to the alternative splicing modulation of a specific subset of genes. We propose that SP7-like proteins act as negative regulators of SR45 to modulate the fate of specific mRNAs in arbuscule-containing cells. Unraveling the communication mechanisms between symbiotic fungi and their host plants will help to identify targets to improve plant nutrition.

Abstract Background and aim Traffic-related ultrafine particle pollution near highways is associated with adverse health. Reducing exposure by use of portable air purifiers in homes is one approach to reducing this risk. However, the reaction of residents to having air purifiers in homes is not well studied. Methods Within the framework of our randomized crossover trial of air purifiers in homes near a major highway, we collected data about participants’ use and reactions to air purifiers using questionnaires at their 30-day and 90-day home visits, recorded electricity consumption using HOBO monitors, and conducted structured interviews with participants. Results Nearly all 150 participants reported running the air purifiers virtually 24 h every day in both their living room and their bedroom in the prior month. The units’ HOBO electricity use, from a subset of 45 participants, supported the participants’ responses from the questionnaire. Approximately 80% of participants reported setting their air purifier on the medium setting. Tolerance to air purifier noise increased significantly between the 30-day and 90-day home visits, with approximately two thirds reporting not being bothered at all by the noise. The qualitative interviews in a subset of 26 participants yielded consistent responses to those from the questionnaires. Size of unit, airflow, and energy consumption were additional concerns that emerged during the interviews. Conclusions Results from the questionnaires, HOBO data, and structured interviews all suggest participants had positive reactions towards the presence of in-home APs, and therefore may be receptive to using air purifiers in their homes on a regular basis. Trial registration ClinicalTrials.gov, NCT04279249 . Registered 09 October 2019.

Abstract Objective To understand how Long COVID is impacting the health and social conditions of the Black and Latinx communities. Background Emerging research on Long COVID has identified three distinct characteristics, including multi-organ damage, persistent symptoms, and post-hospitalization complications. Given Black and Latinx communities experienced significantly higher COVID rates in the first phase of the pandemic they may be disproportionately impacted by Long COVID. Methods Eleven focus groups were conducted in four languages with diverse Black and Latinx individuals (n = 99) experiencing prolonged symptoms of COVID-19 or caring for family members with prolonged COVID-19 symptoms. Data was analyzed thematically. Results Most participants in non-English language groups reported they were unfamiliar with the diagnosis of long COVID, despite experiencing symptoms. Long COVID impacts spanned financial and housing stability to physical and mental health impacts. Participants reported challenging encounters with health care providers, a lack of support managing symptoms and difficulty performing activities of daily living including work. Conclusions There is a need for multilingual, accessible information about Long COVID symptoms, improved outreach and healthcare delivery, and increased ease of enrollment in long-term disability and economic support programs.

Abstract AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D (“Skin Lesion Image Crops Extracted from 3D TBP”) dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection.

The implementation of the “power-to-gas” concept, where hydrogen and natural gas are blended and transported in the existing network, requires a quick, on-site method to monitor the content of hydrogen in the mixture. We evaluate a rapid characterisation of this mixture based on the measurement of the speed of sound, using micromachined ultrasonic transducers (MUTs). Two MUT-based prototypes were implemented to analyse a mixture of natural gas and hydrogen under controlled conditions. Changes in the hydrogen content below 2 mol % (in a mixture that was adjusted between 6 mol % and 16 mol %) were discriminated by both devices, including the uncertainty due to the temperature compensation and the time-delay estimation. The obtained values of the speed of sound were consistent with those calculated from independent, non-acoustic measurements performed with a gas chromatograph and a density sensor. An MUT-based flow meter is thus capable of reporting both gas intake and the molar fraction of hydrogen, provided that the source of natural gas is kept constant.

Abstract Despite its high prevalence, the determinants of smelling impairment in COVID-19 remain not fully understood. In this work, we aimed to examine the association between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment in a large-scale magnetic resonance imaging (MRI) analysis. Data of non-vaccinated COVID-19 convalescents recruited within the framework of the prospective Hamburg City Health Study COVID Program between March and December 2020 were analyzed. At baseline, 233 participants underwent MRI and neuropsychological testing as well as a structured questionnaire for olfactory function. Between March and April 2022, olfactory function was assessed at follow-up including quantitative olfactometric testing with Sniffin’ Sticks. This study included 233 individuals recovered from mainly mild to moderate SARS-CoV-2 infections. Longitudinal assessment demonstrated a declining prevalence of self-reported olfactory dysfunction from 67.1% at acute infection, 21.0% at baseline examination and 17.5% at follow-up. Participants with post-acute self-reported olfactory dysfunction had a significantly lower olfactory bulb volume at baseline than normally smelling individuals. Olfactory bulb volume at baseline predicted olfactometric scores at follow-up. Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. Our work demonstrates an association of long-term self-reported smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.

Abstract Background Diabetes mellitus (DM) can impair driving safety due to hypoglycemia, hyperglycemia, diabetic peripheral neuropathy, and diabetic eye diseases. However, few studies have examined the association between DM and driving safety in older adults based on naturalistic driving data. Methods Data for this study came from a multisite naturalistic driving study of drivers aged 65–79 years at baseline. Driving data for the study participants were recorded by in-vehicle recording devices for up to 44 months. We used multivariable negative binomial modeling to estimate adjusted incidence rate ratios (aIRRs) and 95% confidence intervals (CIs) of hard braking events (HBEs, defined as maneuvers with deceleration rates ≥ 0.4 g) associated with DM. Results Of the 2856 study participants eligible for this analysis, 482 (16.9%) reported having DM at baseline, including 354 (12.4%) insulin non-users and 128 (4.5%) insulin users. The incidence rates of HBEs per 1000 miles were 1.13 for drivers without DM, 1.15 for drivers with DM not using insulin, and 1.77 for drivers with DM using insulin. Compared to drivers without DM, the risk of HBEs was 48% higher for drivers with DM using insulin (aIRR 1.48; 95% CI: 1.43, 1.53). Conclusion Older adult drivers with DM using insulin appear to be at increased proneness to vehicular crashes. Driving safety should be taken into consideration in DM care and management.