Rapid haemoglobin A and S quantification using Tosoh HLC-723G8 in variant mode for patients with sickle cell disease.

The management of life-threatening complications in patients with sickle cell disease (SCD) requires an accurate and reproducible quantification of haemoglobin A (HbA) and S (HbS) with a short turnaround time and 24-7 availability. We propose a novel method for quantifying HbA and HbS using the glycated haemoglobin (HbA1c) assay on a Tosoh HLC-723G8 (G8) analyser in variant mode. HbA and HbS results obtained using our method highly correlated with results obtained using a reference method (r > 0.99 for 124 samples of patients with SCD or sickle cell trait). Our method met laboratory requirements for linearity (coefficient of variation [CV] and bias <5%), between-run and within-run reproducibility (CV <10%) and carryover (<0.5%) over the range of HbS and HbA values expected in a therapeutic context. Using the G8 analyser in variant mode is viable for monitoring HbA and HbS concentrations in dire situations. This method is easy to use, quick (1.6 min per sample), and automatable and produces highly reproducible results without significant bias. Finally, it does not require modifications to the analytical pipeline recommended by the supplier, enabling a 24-7 availability without disrupting routine monitoring of HbA1c in the laboratory.

Combined Metabolipidomic and Machine Learning Approach in a Rat Model of Stroke Reveals a Deleterious Impact of Brain Injury on Heart Metabolism.

Cardiac complications are frequently found following a stroke in humans whose pathophysiological mechanism remains poorly understood. We used machine learning to analyse a large set of data from a metabolipidomic study assaying 630 metabolites in a rat stroke model to investigate metabolic changes affecting the heart within 72 h after a stroke. Twelve rats undergoing a stroke and 28 rats undergoing the sham procedure were investigated. A plasmatic signature consistent with the literature with notable lipid metabolism remodelling was identified. The post-stroke heart showed a discriminant metabolic signature, in comparison to the sham controls, involving increased collagen turnover, increased arginase activity with decreased nitric oxide synthase activity as well as an altered amino acid metabolism (including serine, asparagine, lysine and glycine). In conclusion, these results demonstrate that brain injury induces a metabolic remodelling in the heart potentially involved in the pathophysiology of stroke heart syndrome.

Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears.

OBJECTIVES: VEXAS syndrome is a newly described autoinflammatory disease associated with UBA1 somatic mutations and vacuolization of myeloid precursors. This disease possesses an increasingly broad spectrum, leading to an increase in the number of suspected cases. Its diagnosis via bone-marrow aspiration and UBA1-gene sequencing is time-consuming and expensive. This study aimed at analyzing peripheral leukocytes using deep learning approaches to predict VEXAS syndrome in comparison to differential diagnoses. METHODS: We compared leukocyte images from blood smears of three groups: participants with VEXAS syndrome (identified UBA1 mutation) (VEXAS); participants with features strongly suggestive of VEXAS syndrome but without UBA1 mutation (UBA1-WT); participants with a myelodysplastic syndrome and without clinical suspicion of VEXAS syndrome (MDS). To compare images of circulating leukocytes, we applied a two-step procedure. First, we used self-supervised contrastive learning to train convolutional neural networks to translate leukocyte images into lower-dimensional encodings. Then, we employed support vector machine to predict patients' condition based on those leukocyte encodings. RESULTS: The VEXAS, UBA1-WT, and MDS groups included 3, 3, and 6 patients respectively. Analysis of 33,757 images of neutrophils and monocytes enabled us to distinguish VEXAS patients from both UBA1-WT and MDS patients, with mean ROC-AUCs ranging from 0.87 to 0.95. CONCLUSIONS: Image analysis of blood smears via deep learning accurately distinguished neutrophils and monocytes drawn from patients with VEXAS syndrome from those of patients with similar clinical and/or biological features but without UBA1 mutation. Our findings offer a promising pathway to better screening for this disease.

In Reply to Performance of Deep Learning in the Interpretation of Serum Protein Electrophoresis.

We thank He et al. for their comments on our article (1), which gives us the opportunity to clarify some methodological points. 1. Detection of abnormal patterns: mechanics.

Clinically Symptomatic Resistance to Thyroid Hormone ß Syndrome Because of THRB Gene Mosaicism.

CONTEXT: Resistance to thyroid hormone ß syndrome (RTHß) is caused by pathogenic variants in the THRB gene, but such variants are found in only 85% of cases. We report the case of a patient with RTHß phenotype but for whom we found a pathogenic variant of the THRB gene in a mosaic state. CASE DESCRIPTION: The patient is a 52-year-old woman with clinical and biological signs of RTHß. Symptoms included asthenia, cardiac palpitations, and diarrhea. Repeated thyroid function tests showed an elevated serum TSH, elevated serum free T4, and variably normal or slightly elevated serum fT3. Pituitary magnetic resonance imaging was normal, and the thyrotropin-releasing hormone test result was compatible with the diagnosis of RTHß syndrome. Initial Sanger sequencing on blood samples could not highlight the presence of a mosaic variant because of insufficient sensitivity. When next-generation sequencing became accessible, blood samples were retested and we found a known pathogenic variant: c.949G > A; p.(ala317Thr), with an allelic frequency of 12%. Other samples from tissues of different embryological origin were also tested and found an allelic frequency of 5.7%, 17.9%, 9.9%, 6.4%, and 0% on urine tests, oral swab, nasal mucosa swab, skin biopsy, and conjunctival swab, respectively. Cloning confirmed the allelic frequency observed. CONCLUSIONS: We highlight that a pathogenic variant in a mosaic state in the THRB gene may be the cause of an authentic RTHß syndrome. High-throughput sequencing of multiple tissues eases the detection of pathogenic variant in a mosaic state and allows the correct diagnosis of patients with true RTHß, thus avoiding patient mismanagement.

Post-infarct cardiac remodeling predictions with machine learning.

BACKGROUND: We sought to improve the risk prediction of 3-month left ventricular remodeling (LVR) occurrence after myocardial infarction (MI), using a machine learning approach. METHODS: Patients were included from a prospective cohort study analyzing the incidence of LVR in ST-elevation MI in 443 patients that were monitored at Angers University Hospital, France. Clinical, biological and cardiac magnetic resonance (CMR) imaging data from the first week post MI were collected, and LVR was assessed with CMR at 3 month. Data were processed with a machine learning pipeline using multiple feature selection algorithms to identify the most informative variables. RESULTS: We retrieved 133 clinical, biological and CMR imaging variables, from 379 patients with ST-elevation MI. A baseline logistic regression model using previously known variables achieved an AUC of 0.71 on the test set, with 67% sensitivity and 64% specificity. In comparison, our best predictive model was a neural network using seven variables (in order of importance): creatine kinase, mean corpuscular volume, baseline left atrial surface, history of diabetes, history of hypertension, red blood cell distribution width, and creatinine. This model achieved an AUC of 0.78 on the test set, reaching a sensitivity of 92% and a specificity of 55%, outperforming the baseline model. CONCLUSION: These preliminary results show the value of using an unbiased data-driven machine learning approach. We reached a higher level of sensitivity compared to traditional methods for the prediction of a 3-month post-MI LVR.

The Long Non-Coding RNA SAMMSON Is a Regulator of Chemosensitivity and Metabolic Orientation in MCF-7 Doxorubicin-Resistant Breast Cancer Cells.

Despite improvements in therapeutic strategies for treating breast cancers, tumor relapse and chemoresistance remain major issues in patient outcomes. Indeed, cancer cells display a metabolic plasticity allowing a quick adaptation to the tumoral microenvironment and to cellular stresses induced by chemotherapy. Recently, long non-coding RNA molecules (lncRNAs) have emerged as important regulators of cellular metabolic orientation. In the present study, we addressed the role of the long non-coding RNA molecule (lncRNA) SAMMSON on the metabolic reprogramming and chemoresistance of MCF-7 breast cancer cells resistant to doxorubicin (MCF-7dox). Our results showed an overexpression of SAMMSON in MCF-7dox compared to doxorubicin-sensitive cells (MCF-7). Silencing of SAMMSON expression by siRNA in MCF-7dox cells resulted in a metabolic rewiring with improvement of oxidative metabolism, decreased mitochondrial ROS production, increased mitochondrial replication, transcription and translation and an attenuation of chemoresistance. These results highlight the role of SAMMSON in the metabolic adaptations leading to the development of chemoresistance in breast cancer cells. Thus, targeting SAMMSON expression levels represents a promising therapeutic route to circumvent doxorubicin resistance in breast cancers.

Late-onset argininosuccinic aciduria in a 72-year-old man presenting with fatal hyperammonemia.

Argininosuccinate lyase deficiency (ASLD, MIM #207900) is an inherited urea cycle disorder. There are mainly two clinical forms, an acute neonatal form which manifests as life-threatening hyperammonemia, and a late-onset form characterised by polymorphic neuro-cognitive or psychiatric presentation with transient hyperammonemia episodes. Here, we report a late-onset case of ASLD in a 72-year-old man carrying a homozygous pathogenic variant in the exon 16 of the ASL gene, presenting for the first time with fatal hyperammonemic coma. This case report shows the need to systematically carry out an ammonia assay when faced with an unexplained coma.

Achieving Expert-Level Interpretation of Serum Protein Electrophoresis through Deep Learning Driven by Human Reasoning.

BACKGROUND: Serum protein electrophoresis (SPE) is a common clinical laboratory test, mainly indicated for the diagnosis and follow-up of monoclonal gammopathies. A time-consuming and potentially subjective human expertise is required for SPE analysis to detect possible pitfalls and to provide a clinically relevant interpretation. METHODS: An expert-annotated SPE dataset of 159 969 entries was used to develop SPECTR (serum protein electrophoresis computer-assisted recognition), a deep learning-based artificial intelligence, which analyzes and interprets raw SPE curves produced by an analytical system into text comments that can be used by practitioners. It was designed following academic recommendations for SPE interpretation, using a transparent architecture avoiding the "black box" effect. SPECTR was validated on an external, independent cohort of 70 362 SPEs and challenged by a panel of 9 independent experts from other hospital centers. RESULTS: SPECTR was able to identify accurately both quantitative abnormalities (r ≥ 0.98 for fractions quantification) and qualitative abnormalities [receiver operating characteristic-area under curve (ROC-AUC) ≥ 0.90 for M-spikes, restricted heterogeneity of immunoglobulins, and beta-gamma bridging]. Furthermore, it showed highly accurate at both detecting (ROC-AUC ≥ 0.99) and quantifying (r = 0.99) M-spikes. It proved highly reproducible and resilient to minor variations and its agreement with human experts was higher (κ = 0.632) than experts between each other (κ = 0.624). CONCLUSIONS: SPECTR is an algorithm based on artificial intelligence suitable to high-throughput SPEs analyses and interpretation. It aims at improving SPE reproducibility and reliability. It is freely available in open access through an online tool providing fully editable validation assistance for SPE.

Risk of infection according to the gamma globulin level in the 100 days following allogeneic stem cell transplantations.

BACKGROUND: Immunoglobulin replacement therapy is recommended in case of severe hypogammaglobulinemia after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, the supposed increased risk of infection in case of hypogammaglobulinemia has not been confirmed in allo-HSCT. In this study, we assessed the relationship between the gamma globulin level and the risk of infection during the 100 days following the allo-HSCT. METHODS: We gathered the weekly laboratory tests from day 7 to day 100 of 76 allograft patients, giving a total of 1 044 tests. 130 infections were documented clinically, by imaging, or microbiologically. RESULTS: Average gamma globulin levels between D-7 and D100 did not differ between patients with or without infection (642 ± 232 and 671 ± 246 mg/dL, respectively, P = .65). Gamma globulin level <400 mg/dl was not associated with the occurrence of infection between the test studied and the next one (aOR 1.33 [0.84-2.15], P = .24). The gamma globulin level was not predictive of bacterial or fungal infections (AUC 0.54 [95%CI: 0.47-0.61]) nor of viral reactivations (AUC 0.51 [95%CI: 0.43-0.60]). CONCLUSIONS: This confirmed that the humoral deficiency is a minor part of the immune deficiency in the 100 days post-transplant. This questions the relevance of the indications of immunoglobulin substitution during this period.

Metabolomic Sexual Dimorphism of the Mouse Brain is Predominantly Abolished by Gonadectomy with a Higher Impact on Females.

The importance of sexual dimorphism of the mouse brain metabolome was recently highlighted, in addition to a high regional specificity found between the frontal cortex, the cerebellum, and the brain stem. To address the origin of this dimorphism, we performed gonadectomy on both sexes, followed by a metabolomic study targeting 188 metabolites in the three brain regions. While sham controls, which underwent the same surgical procedure without gonadectomy, reproduced the regional sexual dimorphism of the metabolome previously identified, no sex difference was identifiable after gonadectomy, through both univariate and multivariate analyses. These experiments also made it possible to identify which sex was responsible for the dimorphism for 35 metabolites. The female sex contributed to the difference for more than 80% of them. Our results show that gonads are the main contributors to the brain sexual dimorphism previously observed, especially in females.

Preliminary Metabolomic Profiling of the Vitreous Humor from Hypothermia Fatalities.

The postmortem diagnosis of hypothermia fatalities is often complex due to the absence of pathognomonic lesions and biomarkers. In this study, potential novel biomarkers of hypothermia fatalities were searched in the vitreous humor of known cases of hypothermia fatalities (n = 20) compared to control cases (n = 16), using a targeted metabolomics approach allowing quantitative detection of 188 metabolites. A robust discriminant model with good predictivity was obtained with the supervised OPLS-DA multivariate analysis, showing a distinct separation between the hypothermia and control groups. This signature was characterized by the decreased concentrations of five metabolites (methionine sulfoxide, tryptophan, phenylalanine, alanine, and ornithine) and the increased concentration of 28 metabolites (21 phosphatidylcholines, 3 sphingomyelins, spermine, citrulline, acetylcarnitine, and hydroxybutyrylcarnitine) in hypothermia fatalities compared to controls. The signature shows similarities with already identified features in serum such as the altered concentrations of tryptophan, acylcarnitines, and unsaturated phosphatidylcholines, revealing a highly significant increased activity of methionine sulfoxide reductase, attested by a low methionine sulfoxide-to-methionine ratio. Our results show a preliminary metabolomics signature of hypothermia fatalities in the vitreous humor, highlighting an increased methionine sulfoxide reductase activity.

Deep learning shows no morphological abnormalities in neutrophils in Alzheimer's disease.

INTRODUCTION: Several studies have provided evidence of the key role of neutrophils in the pathophysiology of Alzheimer's disease (AD). Yet, no study to date has investigated the potential link between AD and morphologically abnormal neutrophils on blood smears. METHODS: Due to the complexity and subjectivity of the task by human analysis, deep learning models were trained to predict AD from neutrophil images. Control models were trained for a known feasible task (leukocyte subtype classification) and for detecting potential biases of overfitting (patient prediction). RESULTS: Deep learning models achieved state-of-the-art results for leukocyte subtype classification but could not accurately predict AD. DISCUSSION: We found no evidence of morphological abnormalities of neutrophils in AD. Our results show that a solid deep learning pipeline with positive and bias control models with visualization techniques are helpful to support deep learning model results.

Familial Dysalbuminemic Hyperthyroxinemia: An Underdiagnosed Entity.

Resistance to thyroid hormone (RTH) is a syndrome characterized by impaired sensitivity of tissues to thyroid hormone (TH). The alteration of TH-binding proteins, such as in Familial Dysalbuminemic Hyperthyroxinemia (FDH), can mimic the abnormal serum thyroid tests typical of RTH. We aimed to characterize a population referred to our center with suspected RTH and estimate the proportion of patients with FDH. For 303 different families, we collected clinical and hormonal data and sequenced the thyroid hormone receptor ß gene (THRB) and exon 7 of the albumin gene (ALB). We found 56 THRB variants (i.e., 38% of the 303 index cases, called RTHß group). Among the samples screened for FDH variants, 18% had the variant R218H in ALB (FDH group); in addition, 71% of the cases had neither variant (non-FDH/RTHß group). Patients with FDH had significantly lower free T3 (fT3) and free T4 (fT4) levels and more often an isolated elevation of fT4 than RTHß patients. Clinically, patients with FDH had fewer symptoms than patients with RTHß. Our study suggests that FDH should be systematically considered when examining patients suspected of having RTH. In most cases, they present no clinical symptoms, and their biochemical alterations show an elevation of fT4 levels, while fT3 levels are 1.11 times below the upper limit of the assay.

Tryptophane-kynurenine pathway in the remote ischemic conditioning mechanism.

The actual protective mechanisms underlying cardioprotection with remote ischemic conditioning (RIC) remain unclear. Recent data suggest that RIC induces kynurenine (KYN) and kynurenic acid synthesis, two metabolites derived from tryptophan (TRP), yet a causal relation between TRP pathway and RIC remains to be established. We sought to study the impact of RIC on the levels of TRP and its main metabolites within tissues, and to assess whether blocking kynurenine (KYN) synthesis from TRP would inhibit RIC-induced cardioprotection. In rats exposed to 40-min coronary occlusion and 2-h reperfusion, infarct size was significantly smaller in RIC-treated animals (35.7 ± 3.0% vs. 46.5 ± 2.2%, p = 0.01). This protection was lost in rats that received 1-methyl-tryptophan (1-MT) pretreatment, an inhibitor of KYN synthesis from TRP (infarct size = 46.2 ± 5.0%). Levels of TRP and nine compounds spanning its metabolism through the serotonin and KYN pathways were measured by reversed-phase liquid chromatography-tandem mass spectrometry in the liver, heart, and limb skeletal muscle, either exposed or not to RIC. In the liver, RIC induced a significant increase in xanthurenic acid, nicotinic acid, and TRP. Likewise, RIC increased NAD-dependent deacetylase sirtuin activity in the liver. Pretreatment with 1-MT suppressed the RIC-induced increases in NAD-dependent deacetylase sirtuin activity. Altogether, these findings indicate that RIC mechanism is dependent on TRP-KYN pathway activation.

Metabolomics reveals highly regional specificity of cerebral sexual dimorphism in mice.

The development of personalized medicine according to gender calls for the integration of sexual dimorphism in pre-clinical models of diseases. Although sexual dimorphism in the brain of the mouse has been the subject of several behavioral, neuroimaging and experimental studies, very few have characterized the bases of sexual dimorphism in the brain on the omics scale. In particular, physiological variations in metabolomic and lipidomic terms related to gender have not been mapped in the brain. We carried out a metabolomic analysis, targeting 188 metabolites representative of various cellular structures and metabolisms, in three brain regions: frontal cortex, brain stem and cerebellum, in 3-month-old C57BL-6 J male (n = 20) vs. female (n = 20) mice. Our results demonstrate the existence of sexual dimorphism in the whole brain as well as in separate brain regions. Half of the 129 accurately measured metabolites were involved in the sexual dimorphism of the murine brain, but only 8% of those (hydroxyproline, creatinine, hexoses, tryptophan, threonine and lysoPC.a.C18.2) were involved in common in the three cerebral regions, while 71%, including phosphatidylcholines, lysophosphatidylcholines, sphingomyelins, acylcarnitines, amino acids, biogenic amines, and polyamines, were specific to only one region of the brain, underscoring the highly regional specificity of cerebral sexual dimorphism in mice.