The non-ELR CXC chemokine encoded by human cytomegalovirus UL146 genotype 5 contains a C-terminal ß-hairpin and induces neutrophil migration as a selective CXCR2 agonist.

Human cytomegalovirus (HCMV) is a major pathogen in immunocompromised patients. The UL146 gene exists as 14 diverse genotypes among clinical isolates, which encode 14 different CXC chemokines. One genotype (vCXCL1GT1) is a known agonist for CXCR1 and CXCR2, while two others (vCXCL1GT5 and vCXCL1GT6) lack the ELR motif considered crucial for CXCR1 and CXCR2 binding, thus suggesting another receptor targeting profile. To determine the receptor target for vCXCL1GT5, the chemokine was probed in a G protein signaling assay on all 18 classical human chemokine receptors, where CXCR2 was the only receptor being activated. In addition, vCXCL1GT5 recruited ß-arrestin in a BRET-based assay and induced migration in a chemotaxis assay through CXCR2, but not CXCR1. In contrast, vCXCL1GT1 stimulated G protein signaling, recruited ß-arrestin and induced migration through both CXCR1 and CXCR2. Both vCXCL1GT1 and vCXCL1GT5 induced equally potent and efficacious migration of neutrophils, and ELR vCXCL1GT4 and non-ELR vCXCL1GT6 activated only CXCR2. In contrast to most human chemokines, the 14 UL146 genotypes have remarkably long C-termini. Comparative modeling using Rosetta showed that each genotype could adopt the classic chemokine core structure, and predicted that the extended C-terminal tail of several genotypes (including vCXCL1GT1, vCXCL1GT4, vCXCL1GT5, and vCXCL1GT6) forms a novel ß-hairpin not found in human chemokines. Secondary NMR shift and TALOS+ analysis of vCXCL1GT1 supported the existence of two stable ß-strands. C-terminal deletion of vCXCL1GT1 resulted in a non-functional protein and in a shift to solvent exposure for tryptophan residues likely due to destabilization of the chemokine fold. The results demonstrate that non-ELR chemokines can activate CXCR2 and suggest that the UL146 chemokines have unique C-terminal structures that stabilize the chemokine fold. Increased knowledge of the structure and interaction partners of the chemokine variants encoded by UL146 is key to understanding why circulating HCMV strains sustain 14 stable genotypes.

Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness.

BACKGROUND: Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness. METHODS: The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts. RESULTS: The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%). CONCLUSION: A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart's continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.

Combined endoscopic and laparoscopic surgery (CELS) for early colon cancer in high-risk patients.

BACKGROUND: Local excision of early colon cancers could be an option in selected patients with high risk of complications and no sign of lymph node metastasis (LNM). The primary aim was to assess feasibility in high-risk patients with early colon cancer treated with Combined Endoscopic and Laparoscopic Surgery (CELS). METHODS: A non-randomized prospective feasibility study including 25 patients with Performance Status score ≥ 1 and/or American Society of Anesthesiologists score ≥ 3, and clinical Union of International Cancer Control stage-1 colon cancer suitable for CELS resection. The primary outcome was failure of CELS resection, defined as either: Incomplete resection (R1/R2), local recurrence within 3 months, complication related to CELS within 30 days (Clavien-Dindo grade ≥ 3), death within 30 days or death within 90 days due to complications to surgery. RESULTS: Fifteen patients with clinical T1 (cT1) and ten with clinical T2 (cT2) colon cancer and without suspicion of metastases were included. Failure occurred in two patients due to incomplete resections. Histopathological examination classified seven patients as having pT1, nine as pT2, six as pT3 adenocarcinomas, and three as non-invasive tumors. In three patients, the surgical strategy was changed intraoperatively to conventional colectomy due to tumor location or size. Median length of stay was 1 day. Seven patients had completion colectomy performed due to histological high-risk factors. None had LNM. CONCLUSIONS: In selected patients, CELS resection was feasible, and could spare some patients large bowel resection.

Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation.

Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human-machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimation. Yet rapid and irregular motion patterns, varying image contrast and domain shifts in imaging devices pose a severe challenge to conventional realtime registration approaches. While learning-based registration networks have the promise of abstracting relevant features and delivering very fast inference times, they come at the potential risk of limited generalisation and robustness for unseen data; in particular, when trained with limited supervision. In this work, we demonstrate that these issues can be overcome by using end-to-end differentiable displacement optimisation. Our method involves a trainable feature backbone, a correlation layer that evaluates a large range of displacement options simultaneously and a differentiable regularisation module that ensures smooth and plausible deformation. In extensive experiments on public and private ultrasound datasets with very sparse ground truth annotation the method showed better generalisation abilities and overall accuracy than a VoxelMorph network with the same feature backbone, while being two times faster at inference.

Lexical stability of psychiatric clinical notes from electronic health records over a decade.

OBJECTIVE: Natural language processing (NLP) methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice - as well as the systems and databases in which clinical notes are recorded and stored - change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models. Despite its importance, the stability of clinical notes over time has rarely been tested. METHODS: The lexical stability of clinical notes from the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011, to November 22, 2021 (a total of 14,811,551 clinical notes describing 129,570 patients) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics. RESULTS: We find lexical stability of the clinical notes over time, with minor deviations during the COVID-19 pandemic. Out of 2988 data points, 17 possible changepoints (corresponding to 0.6%) were detected. The majority of these were related to the discontinuation of a specific note type. CONCLUSION: We find lexical and syntactic stability of clinical notes from psychiatric services over time, which bodes well for the use of NLP for predictive modelling in clinical psychiatry.

Circulating Concentrations of C-Type Natriuretic Peptides Increase with Sacubitril/Valsartan Treatment in Healthy Young Men.

BACKGROUND: C-type natriuretic peptide (CNP) is a cardioprotective peptide with high affinity for the ectoenzyme neutral endopeptidase (neprilysin). We aimed to determine whether angiotensin receptor-neprilysin inhibitor treatment acutely affects circulating concentrations of bioactive CNP and its molecular amino-terminal precursor (NT-proCNP). METHODS: We included 9 and 10 healthy young men in 2 randomized crossover trials with sacubitril/valsartan vs control (Trial 1) and sacubitril/valsartan and sitagliptin vs sitagliptin (Trial 2). The participants were randomized to a single dose of sacubitril/valsartan (194/206 mg) or control at the first visit 30 min prior to a standardized meal intake. We obtained blood samples at 12 time points over 5 h and measured plasma concentrations of NT-proCNP in both trials and CNP in Trial 2. RESULTS: NT-proCNP concentrations increased 3.5 h after sacubitril/valsartan treatment, and at 4.5 h concentrations were 42% and 65% higher compared with control in Trial 1 and Trial 2, respectively. The total area under the curve (tAUC)15-270 min was 22% higher (P = 0.007) in Trial 1 and 17% higher with treatment (P = 0.017) in Trial 2. Concentrations of bioactive CNP followed a similar temporal pattern with an increase of 93% at 4.5 h and a 31% higher tAUC15-270 min compared with control (P = 0.001) in Trial 2. CONCLUSIONS: Sacubitril/valsartan augments circulating concentrations of both bioactive CNP and NT-proCNP in healthy young men. The increase in bioactive CNP is most likely caused by de novo synthesis and secretion rather than diminished breakdown through neprilysin inhibition.ClinicalTrials.gov registration number NCT03717688.

Acute effects on glucose tolerance by neprilysin inhibition in patients with type 2 diabetes.

AIMS: Sacubitril/valsartan is a neprilysin-inhibitor/angiotensin II receptor blocker used for the treatment of heart failure. Recently, a post-hoc analysis of a 3-year randomized controlled trial showed improved glycaemic control with sacubitril/valsartan in patients with heart failure and type 2 diabetes. We previously reported that sacubitril/valsartan combined with a dipeptidyl peptidase-4 inhibitor increases active glucagon-like peptide-1 (GLP-1) in healthy individuals. We now hypothesized that administration of sacubitril/valsartan with or without a dipeptidyl peptidase-4 inhibitor would lower postprandial glucose concentrations (primary outcome) in patients with type 2 diabetes via increased active GLP-1. METHODS: We performed a crossover trial in 12 patients with obesity and type 2 diabetes. A mixed meal was ingested following five respective interventions: (a) a single dose of sacubitril/valsartan; (b) sitagliptin; (c) sacubitril/valsartan + sitagliptin; (d) control (no treatment); and (e) valsartan alone. Glucose, gut and pancreatic hormone responses were measured. RESULTS: Postprandial plasma glucose increased by 57% (incremental area under the curve 0-240 min) (p = .0003) and increased peak plasma glucose by 1.7 mM (95% CI: 0.6-2.9) (p = .003) after sacubitril/valsartan compared with control, whereas postprandial glucose levels did not change significantly after sacubitril/valsartan + sitagliptin. Glucagon, GLP-1 and C-peptide concentrations increased after sacubitril/valsartan, but insulin and glucose-dependent insulinotropic polypeptide did not change. CONCLUSIONS: The glucose-lowering effects of long-term sacubitril/valsartan treatment reported in patients with heart failure and type 2 diabetes may not depend on changes in entero-pancreatic hormones. Neprilysin inhibition results in hyperglucagonaemia and this may explain the worsen glucose tolerance observed in this study. CLINICALTRIALS: gov (NCT03893526).

Stability of diagnostic coding of psychiatric outpatient visits across the transition from the second to the third version of the Danish National Patient Registry.

OBJECTIVE: In Denmark, data on hospital contacts are reported to the Danish National Patient Registry (DNPR). The ICD-10 main diagnoses from the DNPR are often used as proxies for mental disorders in psychiatric research. With the transition from the second version of the DNPR (DNPR2) to the third (DNPR3) in February-March 2019, the way main diagnoses are coded in relation to outpatient treatment changed substantially. Specifically, in the DNPR2, each outpatient treatment course was labelled with only one main diagnosis. In the DNPR3, however, each visit during an outpatient treatment course is labelled with a main diagnosis. We assessed whether this change led to a break in the diagnostic time-series represented by the DNPR, which would pose a threat to the research relying on this source. METHODS: All main diagnoses from outpatients attending the Psychiatric Services of the Central Denmark Region from 2013 to 2021 (n = 100,501 unique patients) were included in the analyses. The stability of the DNPR diagnostic time-series at the ICD-10 subchapter level was examined by comparing means across the transition from the DNPR2 to the DNPR3. RESULTS: While the proportion of psychiatric outpatients with diagnoses from some ICD-10 subchapters changed statistically significantly from the DNPR2 to the DNPR3, the changes were small in absolute terms (e.g., +0.6% for F2-psychotic disorders and +0.6% for F3-mood disorders). CONCLUSION: The change from the DNPR2 to the DNPR3 is unlikely to pose a substantial threat to the validity of most psychiatric research at the diagnostic subchapter level.

A generalizable speech emotion recognition model reveals depression and remission.

OBJECTIVE: Affective disorders are associated with atypical voice patterns; however, automated voice analyses suffer from small sample sizes and untested generalizability on external data. We investigated a generalizable approach to aid clinical evaluation of depression and remission from voice using transfer learning: We train machine learning models on easily accessible non-clinical datasets and test them on novel clinical data in a different language. METHODS: A Mixture of Experts machine learning model was trained to infer happy/sad emotional state using three publicly available emotional speech corpora in German and US English. We examined the model's predictive ability to classify the presence of depression on Danish speaking healthy controls (N = 42), patients with first-episode major depressive disorder (MDD) (N = 40), and the subset of the same patients who entered remission (N = 25) based on recorded clinical interviews. The model was evaluated on raw, de-noised, and speaker-diarized data. RESULTS: The model showed separation between healthy controls and depressed patients at the first visit, obtaining an AUC of 0.71. Further, speech from patients in remission was indistinguishable from that of the control group. Model predictions were stable throughout the interview, suggesting that 20-30 s of speech might be enough to accurately screen a patient. Background noise (but not speaker diarization) heavily impacted predictions. CONCLUSION: A generalizable speech emotion recognition model can effectively reveal changes in speaker depressive states before and after remission in patients with MDD. Data collection settings and data cleaning are crucial when considering automated voice analysis for clinical purposes.

Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints.

Deep learning based medical image registration remains very difficult and often fails to improve over its classical counterparts where comprehensive supervision is not available, in particular for large transformations-including rigid alignment. The use of unsupervised, metric-based registration networks has become popular, but so far no universally applicable similarity metric is available for multimodal medical registration, requiring a trade-off between local contrast-invariant edge features or more global statistical metrics. In this work, we aim to improve over the use of handcrafted metric-based losses. We propose to use synthetic three-way (triangular) cycles that for each pair of images comprise two multimodal transformations to be estimated and one known synthetic monomodal transform. Additionally, we present a robust method for estimating large rigid transformations that is differentiable in end-to-end learning. By minimising the cycle discrepancy and adapting the synthetic transformation to be close to the real geometric difference of the image pairs during training, we successfully tackle intra-patient abdominal CT-MRI registration and reach performance on par with state-of-the-art metric-supervision and classic methods. Cyclic constraints enable the learning of cross-modality features that excel at accurate anatomical alignment of abdominal CT and MRI scans.

Influence of FGF23 and Klotho on male reproduction: Systemic vs direct effects.

Currently, no treatment exists to improve semen quality in most infertile men. Here, we demonstrate systemic and direct effects of Fibroblast growth factor 23 (FGF23) and Klotho, which normally regulate vitamin D and mineral homeostasis, on testicular function. Direct effects are plausible because KLOTHO is expressed in both germ cells and spermatozoa and forms with FGFR1 a specific receptor for the bone-derived hormone FGF23. Treatment with FGF23 increased testicular weight in wild-type mice, while mice with global loss of either FGF23 or Klotho had low testicular weight, reduced sperm count, and sperm motility. Mice with germ cell-specific Klotho (gcKL) deficiency neither had a change in sperm count nor sperm motility. However, a tendency toward fewer pregnancies was detected, and significantly fewer Klotho heterozygous pups originated from gcKL knockdown mice than would be expected by mendelian inheritance. Moreover, gcKL mice had a molecular phenotype with higher testicular expression of Slc34a2 and Trpv5 than wild-type littermates, which suggests a regulatory role for testicular phosphate and calcium homeostasis. KLOTHO and FGFR1 were also expressed in human germ cells and spermatozoa, and FGF23 treatment augmented the calcium response to progesterone in human spermatozoa. Moreover, cross-sectional data revealed that infertile men with the highest serum Klotho levels had significantly higher serum Inhibin B and total sperm count than men with the lowest serum Klotho concentrations. In conclusion, this translational study suggests that FGF23 and Klotho influence gonadal function and testicular mineral ion homeostasis both directly and indirectly through systemic changes in vitamin D and mineral homeostasis.

The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders.

BACKGROUND: The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years. METHODS: PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation. DISCUSSIONS: We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

Discovery of O-glycans on atrial natriuretic peptide (ANP) that affect both its proteolytic degradation and potency at its cognate receptor.

Atrial natriuretic peptide (ANP) is a peptide hormone that in response to atrial stretch is secreted from atrial myocytes into the circulation, where it stimulates vasodilatation and natriuresis. ANP is an important biomarker of heart failure where low plasma concentrations exclude cardiac dysfunction. ANP is a member of the natriuretic peptide (NP) family, which also includes the B-type natriuretic peptide (BNP) and the C-type natriuretic peptide. The proforms of these hormones undergo processing to mature peptides, and for proBNP, this process has previously been demonstrated to be regulated by O-glycosylation. It has been suggested that proANP also may undergo post-translational modifications. Here, we conducted a targeted O-glycoproteomics approach to characterize O-glycans on NPs and demonstrate that all NP members can carry O-glycans. We identified four O-glycosites in proANP in the porcine heart, and surprisingly, two of these were located on the mature bioactive ANP itself. We found that one of these glycans is located within a conserved sequence motif of the receptor-binding region, suggesting that O-glycans may serve a function beyond intracellular processing and maturation. We also identified an O-glycoform of proANP naturally occurring in human circulation. We demonstrated that site-specific O-glycosylation shields bioactive ANP from proteolytic degradation and modifies potency at its cognate receptor in vitro Furthermore, we showed that ANP O-glycosylation attenuates acute renal and cardiovascular ANP actions in vivo The discovery of novel glycosylated ANP proteoforms reported here significantly improves our understanding of cardiac endocrinology and provides important insight into the etiology of heart failure.

Site-specific O-Glycosylation by Polypeptide N-Acetylgalactosaminyltransferase 2 (GalNAc-transferase T2) Co-regulates ß1-Adrenergic Receptor N-terminal Cleavage.

The ß1-adrenergic receptor (ß1AR) is a G protein-coupled receptor (GPCR) and the predominant adrenergic receptor subtype in the heart, where it mediates cardiac contractility and the force of contraction. Although it is the most important target for ß-adrenergic antagonists, such as ß-blockers, relatively little is yet known about its regulation. We have shown previously that ß1AR undergoes constitutive and regulated N-terminal cleavage participating in receptor down-regulation and, moreover, that the receptor is modified by O-glycosylation. Here we demonstrate that the polypeptide GalNAc-transferase 2 (GalNAc-T2) specifically O-glycosylates ß1AR at five residues in the extracellular N terminus, including the Ser-49 residue at the location of the common S49G single-nucleotide polymorphism. Using in vitro O-glycosylation and proteolytic cleavage assays, a cell line deficient in O-glycosylation, GalNAc-T-edited cell line model systems, and a GalNAc-T2 knock-out rat model, we show that GalNAc-T2 co-regulates the metalloproteinase-mediated limited proteolysis of ß1AR. Furthermore, we demonstrate that impaired O-glycosylation and enhanced proteolysis lead to attenuated receptor signaling, because the maximal response elicited by the ßAR agonist isoproterenol and its potency in a cAMP accumulation assay were decreased in HEK293 cells lacking GalNAc-T2. Our findings reveal, for the first time, a GPCR as a target for co-regulatory functions of site-specific O-glycosylation mediated by a unique GalNAc-T isoform. The results provide a new level of ß1AR regulation that may open up possibilities for new therapeutic strategies for cardiovascular diseases.

Exploring the potential of high-resolution LC-MS in combination with ion mobility separation and surrogate minimal depth for enhanced almond origin authentication.

Almonds (Prunus dulcisMill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis.