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Background: The response to severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) vaccination is severely impaired in patients on maintenance immunosuppression after kidney transplantation., Methods: We conducted a prospective cohort study of 194 kidney transplant recipients (KTR) who exhibited no response to SARS-CoV-2 vaccinations (i.e., SARS-CoV-2 spike protein antibodies ≤264 U/mL) and had no prior documented infection. Patients received 300 mg of cilgavimab/tixagevimab as SARS-CoV-2 pre-exposure prophylaxis (PrEP) between March 4, 2022, and May 3, 2022 and were contrasted to a matched cohort of 186 KTRs also without immunization again defined as SARS-CoV-2 spike protein antibodies ≤264 U/mL and no documented prior infection. The primary outcome was the serum kinetics of cilgavimab/tixagevimab, the secondary endpoints were time to SARS-CoV-2 breakthrough infection, severity of disease and variant specific live viral in vitro neutralization tests of patient sera., Findings: Longitudinal serum level monitoring showed a half-life of 91 days for both antibodies (95% CI 86-95 days for cilgavimab and 85-96 days for tixagevimab) in KTRs. In vitro neutralization tests showed effectiveness against the BA.2 omicron subvariant but not BA.5. The cumulative incidence of SARS-CoV-2 infections until May 15, 2022, (BA.2 dominance) was 15/194 vs 36/186 in the PrEP and control group respectively (OR = 0.35, 95% CI 0.18-0.66) but was not different thereafter (BA.4/5 dominance). The number of severe infections during the BA.2 period was lower in the prophylaxis than in the control group (OR = 0.37, 95% CI 0.17-0.79)., Interpretation: This study showed that SARS-CoV-2 PrEP with cilgavimab/tixagevimab demonstrated clinical effectiveness against variants that are neutralised (BA.2) but not against BA.4/5., Funding: This study was funded by the Medical University of Vienna and an unrestricted grant from AstraZeneca (ESR-21-21585)., Competing Interests: Declaration of interests “The authors declare no competing interests for this manuscript”, (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Lung cancer is the leading cause of cancer death worldwide, with poor survival despite recent therapeutic advances. A better understanding of the complexity of the tumor microenvironment is needed to improve patients' outcome., Methods: We applied a computational immunology approach (involving immune cell proportion estimation by deconvolution, transcription factor activity inference, pathways and immune scores estimations) in order to characterize bulk transcriptomics of 62 primary lung adenocarcinoma (LUAD) samples from patients across disease stages. Focusing specifically on early stage samples, we validated our findings using an independent LUAD cohort with 70 bulk RNAseq and 15 scRNAseq datasets and on TCGA datasets., Results: Through our methodology and feature integration pipeline, we identified groups of immune cells related to disease stage as well as potential immune response or evasion and survival. More specifically, we reported a duality in the behavior of immune cells, notably natural killer (NK) cells, which was shown to be associated with survival and could be relevant for immune response or evasion. These distinct NK cell populations were further characterized using scRNAseq data, showing potential differences in their cytotoxic activity., Conclusion: The dual profile of several immune cells, most notably T-cell populations, have been discussed in the context of diseases such as cancer. Here, we report the duality of NK cells which should be taken into account in conjunction with other immune cell populations and behaviors in predicting prognosis, immune response or evasion., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Hurtado, Khajavi, Essabbar, Kammer, Xie, Coullomb, Pradines, Casanova, Kruczynski, Gouin, Clermont, Boutillet, Senosain, Zou, Zhao, Burq, Mahfoudi, Besse, Launay, Passioukov, Chetaille, Favre, Maldonado, Cruzalegui, Delfour, Mazières and Pancaldi.)

Background: Measurement of tumor markers from peripheral venous blood is an emerging tool to assist in the early diagnosis of lung cancer. Samples from the pulmonary artery and pulmonary artery wedge position (trans-pulmonary samples) are accessible via right-heart catheterization and, by virtue of their proximity to lung tumors, may increase diagnostic yield., Case Presentation: We report a case of a 64 year-old woman from whom trans-pulmonary samples were obtained and who was diagnosed 16 months later with recurrent metastatic small cell lung cancer. Carcinoembryonic antigen, cytokeratin fragment 21 - 1 (CYFRA), and human epididymis protein 4 (HE4) levels demonstrated increasing concentrations across the pulmonary circulation. These gradients exceeded the assays' coefficient of variation by several-fold. For CYFRA and HE4, pulmonary artery wedge concentrations exceeded peripheral venous levels by more than 10% and peripheral arterial levels were up to 8% higher than peripheral venous levels., Conclusions: Evaluating the feasibility and utility of trans-pulmonary tumor markers for lung cancer diagnosis in a larger cohort should be considered. The addition of a peripheral arterial sample to standard peripheral venous samples may be a more practical alternative., (© 2024. The Author(s).)

Background: Induction of donor-specific tolerance is a promising approach to achieve long-term graft patency in transplantation with little to no maintenance immunosuppression. Changes to the recipient's T cell receptor (TCR) repertoire are understood to play a pivotal role in the establishment of a robust state of tolerance in chimerism-based transplantation protocols., Methods: We investigated changes to the TCR repertoires of patients participating in an ongoing prospective, controlled, phase I/IIa trial designed to evaluate the safety and efficacy of combination cell therapy in living donor kidney transplantation. Using high-throughput sequencing, we characterized the repertoires of six kidney recipients who also received bone marrow from the same donor (CKBMT), together with an infusion of polyclonal autologous Treg cells instead of myelosuppression., Findings: Patients undergoing combination cell therapy exhibited partial clonal deletion of donor-reactive CD4 + T cells at one, three, and six months post-transplant, compared to control patients receiving the same immunosuppression regimen but no cell therapy (p = 0.024). The clonality, R20 and turnover rates of the CD4 + and CD8 + TCR repertoires were comparable in both groups, showing our protocol caused no excessive repertoire shift or loss of diversity. Treg clonality was lower in the case group than in control (p = 0.033), suggesting combination cell therapy helps to preserve Treg diversity., Interpretation: Overall, our data indicate that combining Treg cell therapy with CKBMT dampens the alloimmune response to transplanted kidneys in humans in the absence of myelosuppression., Funding: This study was funded by the Vienna Science and Technology Fund (WWTF)., Competing Interests: Declaration of interests The authors have declared that no conflict of interest exists., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: The results of this study describe the relationship between the body condition of dairy cows and selected metabolic parameters during the peri- and post-partum period with special consideration of 3 local dairy cow breed in Upper Bavaria and the Allgau., Material and Methods: Three local dairy cattle breeds (Swiss Brown (BV), Simmental (FL), Holstein Friesian (HF)) were examined on 68 farms in southern Germany for 7 consecutive weeks. In dry cows as well as lactating cows (5.-65. day in milk), following blood parameters were investigated: beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), creatinine, aspartate aminotransferase, gamma-glutamyltransferase, glutamate dehydrogenase, total protein, albumin, creatine kinase. In addition, body condition (body condition score [BCS] and back fat thickness [BFT]) were recorded. Exploratory and descriptive statistics were used for data analysis., Results: Concerning the difference in condition before and after calving, the FL showed the smallest difference in RFD. For FL and BV a trend towards higher BFT values could be seen in first lactating cows. For FL and HF, the NEFA values of the later lactating cows were below those of the first lactating cows. The higher lactating cows of BV and FL had higher BHB values. The correlation between BFT and BCS showed the highest R2 (0.53) in the HF cows. BV and FL were below at 0.42 and 0.37. BCS and BFT could not be predicted by the variables NEFA, BHB and liver enzymes. BHB levels of all 3 breeds increased at weeks 2-4 post-partum. The NEFA values for all 3 breeds increased primarily in the 1st-3rd week p.p. in parallel to when the BFT p.p. decreased. NEFA values were highest when body condition declined and therefore when fat mobilization peaked. In BV and HF, there was a constant increase in GLDH when the p.p. BCS difference was there., Conclusion and Clinical Relevance: Body condition assessment (BCS at herd and animals` level, BFT at animal level) is an important tool for animal health monitoring. Due to the recognizable breed specificity, the dairy herds can be dealt with more explicitly. The aim is to optimally influence the energy balance of the cow during early lactation in order maintain the health of the animal and its organ systems., Competing Interests: Die Autor*innen bestätigen, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Background: We recently found that epiplakin 1 (EPPK1) alterations were present in 12% of lung adenocarcinoma (LUAD) cases and were associated with a poor prognosis in early-stage LUAD when combined with other molecular alterations. This study aimed to identify a probable crucial role for EPPK1 in cancer development., Methods: EPPK1 mRNA and protein expression was analyzed with clinical variables. Normal bronchial epithelial cell lines were exposed to cigarette smoke for 16 weeks to determine whether EPPK1 protein expression was altered after exposure. Further, we used CRISPR-Cas9 to knock out (KO) EPPK1 in LUAD cell lines and observed how the cancer cells were altered functionally and genetically., Results: EPPK1 protein expression was associated with smoking and poor prognosis in early-stage LUAD. Moreover, a consequential mesenchymal-to-epithelial transition was observed, subsequently resulting in diminished cell proliferation and invasion after EPPK1 KO. RNA sequencing revealed that EPPK1 KO induced downregulation of 11 oncogenes, 75 anti-apoptosis, and 22 angiogenesis genes while upregulating 8 tumor suppressors and 12 anti-cell growth genes. We also observed the downregulation of MYC and upregulation of p53 expression at both protein and RNA levels following EPPK1 KO. Gene ontology enrichment analysis of molecular functions highlighted the correlation of EPPK1 with the regulation of mesenchymal cell proliferation, mesenchymal differentiation, angiogenesis, and cell growth after EPPK1 KO., Conclusions: Our data suggest that EPPK1 is linked to smoking, epithelial to mesenchymal transition, and the regulation of cancer progression, indicating its potential as a therapeutic target for LUAD., (© 2024. The Author(s).)

Introduction: Diabetic kidney disease (DKD) is a common cause of chronic kidney disease with around 25-40% of patients with diabetes being affected. The course of DKD is variable, and estimated glomerular filtration rate (eGFR) and albuminuria, the currently used clinical markers, are not able to accurately predict the individual disease trajectory, in particular in early stages of the disease. The aim of this study was to assess the association of urine levels of selected protein biomarkers with the progression of DKD at an early stage of disease., Methods: We measured 22 protein biomarkers using the Mesoscale Discovery platform in 461 urine samples of the PROVALID cohort, an observational study of patients with type 2 diabetes mellitus followed at the primary health care level for a minimum of 4 years. Odds ratios (ORs) were estimated for the effect of marker values above median on fast progression using unadjusted and adjusted logistic regression models. RNA expression at the single-cell level in kidney biopsy samples obtained from a cohort of young persons with type 2 diabetes mellitus was in addition determined for markers showing significant associations with disease progression., Results: Increased urinary levels of epidermal growth factor (EGF) were linked to lower odds of fast progression (defined as annual eGFR decline greater than 2.58 mL/min per 1.73 m2) with an OR of 0.60 (95% CI: 0.46, 0.78). The association with outcome was even stronger when adjusting for a set of 14 baseline clinical parameters including age, biological sex, eGFR, body mass index, albuminuria, and HbA1c. Elevated urinary levels of fatty acid-binding protein 3 (FABP3) and vascular cell adhesion molecule 1 (VCAM1) were each significantly associated with fast progression with an OR of 1.44 (95% CI: 1.11, 1.87) and an OR of 1.41 (95% CI: 1.08, 1.83), respectively. Enriched expression of EGF and FABP3 was observed in distal convoluted tubular cells and VCAM1 in parietal epithelial cells at single-cell level from biopsies of patients with early DKD., Conclusion: In summary, we show that lower urinary levels of EGF and higher urinary levels of FABP3 and VCAM1 are significantly associated with DKD progression in early-stage disease., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Background: Kidney transplantation is the preferred treatment for eligible patients with kidney failure who need renal replacement therapy. However, it remains unclear whether the anticipated survival benefit from kidney transplantation is different for women and men., Methods: We included all dialysis patients recorded in the Austrian Dialysis and Transplant Registry who were waitlisted for their first kidney transplant between 2000 and 2018. In order to estimate the causal effect of kidney transplantation on 10-year restricted mean survival time, we mimicked a series of controlled clinical trials and applied inverse probability of treatment and censoring weighted sequential Cox models., Results: This study included 4408 patients (33% female) with a mean age of 52 years. Glomerulonephritis was the most common primary renal disease both in women (27%) and men (28%). Kidney transplantation led to a gain of 2.22 years (95% CI 1.88 to 2.49) compared with dialysis over a 10-year follow-up. The effect was smaller in women (1.95 years, 95% CI 1.38 to 2.41) than in men (2.35 years, 95% CI 1.92 to 2.70) due to a better survival on dialysis. Across ages the survival benefit of transplantation over a follow-up of 10 years was smaller in younger women and men and increased with age, showing a peak for both women and men aged about 60 years., Conclusions: There were few differences in survival benefit by transplantation between females and males. Females had better survival than males on the waitlist receiving dialysis and similar survival to males after transplantation., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Objective: The aim of the present study was to investigate relationships between elevated haptoglobin concentrations in milk and clinical as well as laboratory parameters in early lactating dairy cows. Furthermore, cut-off values should be identified for the differentiation of healthy and affected animals., Material and Methods: 1462 dairy cows between 5.-65. days in milk were examined on 68 Bavarian farms. Milk and blood samples were taken once a week for a 7-week period per farm and body-condition-scoring, backfat thickness measurement and Metricheck examination, to evaluate uterine health, were performed. Milk samples were analysed for milk fat, milk protein, lactose, urea, ß-hydroxybutyrate and non-esterified fatty acids (indirect measurement, based on IR spectra), cell count, and milk haptoglobin. Blood samples were analysed for creatinine, aspartate aminotransferase, gamma-glutamyl transferase, glutamate dehydrogenase, total protein, albumin, creatine kinase, ß-hydroxybutyrate, non-esterified fatty acids, and blood haptoglobin.Cluster analyses were performed to determine cut-off values for haptoglobin., Results: Besides milk haptoglobin (µg/ml) and blood haptoglobin (µg/ml), cell count (cells/ml milk), milk fat (%), milk protein (%), non-esterified fatty acids in blood (mmol/l), lactation number, days in milk, breed, season, and milk yield (kg) were included as significant input variables (p<0.005) in the cluster analyses. Cluster analysis, using k-means resp. k-prototypes algorithms, resulted in 5 (clusters 1-5 M 1 ) resp. 4 different clusters (clusters 0-3 M 2 and 0-3 B).A cut-off value of 0.5 µg/ml haptoglobin in milk was determined for the differentiation of healthy and affected animals., Conclusion and Clinical Relevance: As milk is an easily available substrate, routine determination of haptoglobin in milk might be a suitable parameter for animal health monitoring. Using the detected cut-off value, apparently healthy animals with subclinical inflammatory diseases can be identified more quickly., Competing Interests: Die Autoren bestätigen, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015-2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock.

Placental membranes have been widely studied and used clinically for wound care applications, but there is limited published information on the benefits of using the chorion membrane. The chorion membrane represents a promising source of placental-derived tissue to support wound healing, with its native composition of extracellular matrix (ECM) proteins and key regulatory proteins. This study examined the impact of hypothermic storage on the structure of chorion membrane, ECM content, and response to degradation in vitro. Hypothermically stored chorion membrane (HSCM) was further characterized for its proteomic content, and for its functionality as a scaffold for cell attachment and proliferation in vitro. HSCM retained the native ECM structure, composition, and integrity of native unprocessed chorion membrane and showed no differences in response to degradation in an in vitro wound model. HSCM retained key regulatory proteins previously shown to be present in placental membranes and promoted the attachment and proliferation of fibroblasts in vitro. These data support the fact that hypothermic storage does not significantly impact the structure and characteristics of the chorion membrane compared to unprocessed tissue or its functionality as a scaffold to support tissue growth.

The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers. Code available at https://github.com/MASILab/lmsignatures.

Importance: Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking., Objective: To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts., Design, Setting, and Participants: This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023., Main Outcomes and Measures: Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c [mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated., Results: Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2 ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5., Conclusions and Relevance: In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.

Background: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory., Methods: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation., Results: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline., Conclusions: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway., (© 2023. The Author(s).)

Background: Photodynamic therapy (PDT) is an effective method for treating actinic keratosis (AK) with pain during illumination representing the major side effect. The efficacy of two different cooling methods for pain relief in PDT of AK in the head region was compared., Methods: Randomized, assessor-blinded, half side comparison study in 20 patients with symmetrically distributed AK on the head. Conventional PDT was performed on both halves of the scalp or face by applying 20% aminolevulinic acid cream (ALA) and subsequent illumination with incoherent red light. During illumination one side was cooled with a cold air blower (CAB) and the other with a standard fan (FAN) in a randomized fashion. Pain and skin temperature were recorded during and after PDT. The phototoxic skin reaction was evaluated up to seven days after PDT. The clearance rate of AK was assessed at 3 and 6 months after PDT., Results: Mean pain (VASmean), maximum pain intensity (VASmax) and the mean skin temperature during PDT were significantly lower with CAB as compared to FAN (VASmean: 2.7 ± 1.4 vs. 3.7 ± 2.1, p = 0.003; VASmax: 3.8 ± 2.0 vs. 4.8 ± 2.5, p = 0.002; 26.8 ± 2.0 °C vs. 32.1 ± 1.7 °C; p=<0.001). The severity of the phototoxic skin reaction and the clearance rate of AK did not differ between the two cooling methods., Conclusion: Cooling with CAB during PDT has a greater analgesic effect than cooling with FAN. Patients with a lower skin temperature during illumination tended to experience less pain, however, this effect did not reach the level of statistical significance., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier B.V.)

Objective: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the pretest probability of lung cancer. In this study, we apply a deep learning network to integrate multi-modal data from CT images and clinical data (including blood-based biomarkers) to improve lung cancer diagnosis. Our goal is to reduce uncertainty and to avoid morbidity, mortality, over- and undertreatment of patients with IPNs., Method: We use a retrospective study design with cross-validation and external-validation from four different sites. We introduce a deep learning framework with a two-path structure to learn from CT images and clinical data. The proposed model can learn and predict with single modality if the multi-modal data is not complete. We use 1284 patients in the learning cohort for model development. Three external sites (with 155, 136 and 96 patients, respectively) provided patient data for external validation. We compare our model to widely applied clinical prediction models (Mayo and Brock models) and image-only methods (e.g., Liao et al. model)., Results: Our co-learning model improves upon the performance of clinical-factor-only (Mayo and Brock models) and image-only (Liao et al.) models in both cross-validation of learning cohort (e.g., , Auc: 0.787 (ours) vs. 0.707-0.719 (baselines), results reported in validation fold and external-validation using three datasets from University of Pittsburgh Medical Center (e.g., 0.918 (ours) vs. 0.828-0.886 (baselines)), Detection of Early Cancer Among Military Personnel (e.g., 0.712 (ours) vs. 0.576-0.709 (baselines)), and University of Colorado Denver (e.g., 0.847 (ours) vs. 0.679-0.746 (baselines)). In addition, our model achieves better re-classification performance (cNRI 0.04 to 0.20) in all cross- and external-validation sets compared to the Mayo model., Conclusions: Lung cancer risk estimation in patients with IPNs can benefit from the co-learning of CT image and clinical data. Learning from more subjects, even though those only have a single modality, can improve the prediction accuracy. An integrated deep learning model can achieve reasonable discrimination and re-classification performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Importance: Kidney transplant is considered beneficial in terms of survival compared with continued dialysis for patients with kidney failure. However, randomized clinical trials are infeasible, and available evidence from cohort studies is at high risk of bias., Objective: To compare restricted mean survival times (RMSTs) between patients who underwent transplant and patients continuing dialysis across transplant candidate ages and depending on waiting time, applying target trial emulation methods., Design, Setting, and Participants: In this retrospective cohort study, patients aged 18 years or older appearing on the wait list for their first single-organ deceased donor kidney transplant between January 1, 2000, and December 31, 2018, in Austria were evaluated. Available data were obtained from the Austrian Dialysis and Transplant Registry and Eurotransplant and included repeated updates on wait-listing status and relevant covariates. Data were analyzed between August 1, 2019, and December 23, 2021., Exposures: A target trial was emulated in which patients were randomized to either receive the transplant immediately (treatment group) or to continue dialysis and never receive a transplant (control group) at each time an organ became available., Main Outcomes and Measures: The primary outcome was time from transplant allocation to death. Effect sizes in terms of RMSTs were obtained using a sequential Cox approach., Results: Among the 4445 included patients (2974 men [66.9%]; mean [SD] age, 52.2 [13.2] years), transplant was associated with increased survival time across all considered ages compared with continuing dialysis and remaining on the wait list within a 10-year follow-up. The estimated RMST differences were 0.57 years (95% CI, -0.14 to 1.84 years) at age 20 years, 3.01 years (95% CI, 2.50 to 3.54 years) at age 60 years, and 2.48 years (95% CI, 1.88 to 3.04 years) at age 70 years. The survival benefit for patients who underwent transplant across ages was independent of waiting time., Conclusions and Relevance: The findings of this study suggest that kidney transplant prolongs the survival time of persons with kidney failure across all candidate ages and waiting times.

Background: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer., Methods: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index., Results: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy., Conclusions: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management., (Copyright © 2022. Published by Elsevier B.V.)

Background: Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues., Methods: We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability., Results: Frequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates. The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results., Conclusions: Despite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications., (© 2022. The Author(s).)

Secondary hyperparathyroidism in chronic kidney disease poses a major risk factor for vascular calcification and high bone turnover, leading to mineralization defects. The aim was to analyze the effect of active vitamin D and calcimimetic treatment on fibroblast growth factor 23 (FGF23), serum calcification propensity (T50), a surrogate marker of calcification stress and bone specific alkaline phosphatase (BAP) in hemodialysis. This is a subanalysis of a randomized trial comparing etelcalcetide vs. alfacalcidol in 62 hemodialysis patients for 1 year. We compared the change of BAP and serum calcification propensity between the two medications and assessed the influence of FGF23 change over time. We found no significant differences in the change of BAP or serum calcification propensity (T50) levels from baseline to study end between treatment arms (difference in change of marker between treatment with etelcalcetide vs. alfacalcidol: BAP : 2.0 ng/ml [95% CI-1.5,5.4], p = 0.3; T50: -15 min [95% CI -49,19], p = 0.4). Using FGF23 change over time, we could show that BAP levels at study end were associated with FGF23 change (-0.14 [95% CI -0.21, -0.08], p < 0.001). We did not observe the same association between FGF23 change and T50 (effect of FGF23 change on T50: 3.7 [95% CI -5.1, 12], p = 0.4; R 2 = 0.07 vs. R 2 = 0.06). No significant difference was found in serum calcification propensity (T50) values between treatment arms. FGF23 was not associated with serum calcification propensity (T50), but was negatively correlated with BAP underlying its role in the bone metabolism., Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT03182699]., Competing Interests: RO reports grants from Amgen during the conduct of the study. In addition, RO, KD, and RR-S have a patent “Methods of treating left ventricle hypertrophy” pending. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dörr, Hödlmoser, Kammer, Reindl-Schwaighofer, Lorenz, Reiskopf, Jagoditsch, Marculescu and Oberbauer.)

PytuTester is an open-source ventilator tester developed to help bio-engineers in the design and verification of new ventilator prototypes. A ventilator tester allows measuring the flow, pressure, volume, and oxygen concentration provided to the patient. During the global pandemic COVID-19, several open-source ventilators prototypes were developed; however, due to high cost and demand testers, they were not available. In this context, a low-cost tester was developed using a Raspberry Pi and medical-grade sensors for the test ventilators prototypes. This paper presents the design files, software interface, and validations tests. Our results indicate that the tester has good accuracy to evaluate the efficacy and performance of new prototypes. When tested on two ventilator designs developed in Paraguay, PytuTester reported flow profiles that were concordant with the industry-standard VT650 Gas Flow Analyzer. PytuTester was then field deployed to test several DIY ventilator designs in low-resource areas., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

Fibroblast growth factor 23 (FGF23) is elevated in patients with chronic kidney disease and contributes to left ventricular hypertrophy (LVH). The aim of the analysis was to determine whether this effect is mediated by the renin-angiotensin-aldosterone system (RAAS) in hemodialysis. Serum samples from 62 randomized hemodialysis patients with LVH were analyzed for plasma renin activity (PRA-S), angiotensin II (AngII), and metabolites, angiotensin-converting enzyme-2 (ACE2) and aldosterone using a high throughput mass spectrometry assay. Compared to healthy individuals, levels of the RAAS parameters PRA-S, AngII and aldosterone were generally lower [median (IQR) PRA-S 130 (46-269) vs. 196 (98, 238) pmol/L; AngII 70 (28-157) vs. 137 (76, 201) pmol/L; Aldosterone 130 (54, 278) vs. 196 (98, 238) pmol/L]. We did not find an indication that the effect of FGF23 on LVH was mediated by RAAS parameters, with all estimated indirect effects virtually zero. Furthermore, FGF23 was not associated with RAAS parameter levels throughout the study. While there was a clear association between FGF23 levels and left ventricular mass index (LVMI) at the end of the study and in the FGF23 fold change and LVMI change analysis, no association between RAAS and LVMI was observed. Serum concentrations of PRA-S, AngII, and aldosterone were below the ranges measured in healthy controls suggesting that RAAS is not systemically activated in hemodialysis patients. The effect of FGF23 on LVMI was not mediated by systemic RAAS activity. These findings challenge the current paradigm of LVH progression and treatment with RAAS blockers in dialysis., Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/NCT03182699], identifier [NCT03182699]., Competing Interests: RO reported grants from Amgen during the conduct of the study. In addition, RO, KD, and RR-S had a patent “Methods of treating left ventricle hypertrophy” pending. MP was employed by Attoquant Diagnostics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dörr, Kammer, Reindl-Schwaighofer, Lorenz, Marculescu, Poglitsch, Beitzke and Oberbauer.)