Ruling out septic arthritis risk in a few minutes using mid-infrared spectroscopy in synovial fluids

Blood-based MASH diagnostic in candidates for bariatric surgery using mid-infrared spectroscopy: a European multicenter prospective study

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in individuals with obesity. Sexual dimorphism is present in MASLD. A noninvasive test to diagnose the severity of the disease, in particular the presence of Metabolic dysfunction-associated steatohepatitis (MASH), is lacking. This European multicenter prospective study uses a blood test based on mid-infrared (MIR) metabolic fingerprinting of individuals with severe or morbid obesity to diagnose MASH. Three hundred eighty-two individuals with severe or morbid obesity undergoing bariatric surgery were enrolled prospectively. Liver biopsies were obtained during surgery and assessed centrally. An algorithm was defined to calculate a score from the recorded MIR spectrum and to establish a diagnostic threshold to classify patients with MASH. Among the women (n = 217), MASH was diagnosed in 14.3% of cases. For women, the performance in terms of AUC were 0.83 and 0.82 in the calibration and validation groups, respectively. For a threshold of 0.1817, sensitivities were 86% and 70%, specificities were 81% and 75%, PPV were 43% and 32%, NPV were 97% and 94% and ACC were 82% and 74% for the calibration and validation groups, respectively. For men (n = 78; MASH: 33.3%), the performance of the spectral model was poor. The metabolic fingerprint obtained by MIR spectroscopy can rule out MASH in women with severe or morbid obesity. Its value in men needs new studies.Trial registration: ClinicalTrials.gov identifier: ClinicalTrials.gov identifier: NCT03978247 (04/06/2019).

Mid-Infrared Spectroscopy as a New Tool for Ruling Out Spontaneous Bacterial Peritonitis: A Proof-of-Concept Study

Background and aims: A highly sensitive and specific point-of-care method for diagnosing spontaneous bacterial peritonitis (SBP) is currently lacking. The objective of the present study is to evaluate the diagnostic value of a rapid, easy-to-use, mid-infrared fiber evanescent wave spectroscopy (MIR-FEWS) method for ruling out SBP. Patients and Methods: Cirrhotic patients (n = 256) at five centers in France were included for suspected SBP or for the scheduled evacuation of ascites fluid. The mid-infrared spectrum of 7 µL of an ascites fluid sample was recorded using a MIR-FEWS system. To define a model for the diagnosis of SBP, the patients were divided into a calibration group (n = 170) and a validation group (n = 86). Results: Most of the patients were male (71%). The mean age was 60.25 years. Alcohol-related liver disease was the most common cause of cirrhosis. SBP was observed in 18% of the patients. For the diagnosis of SBP in the calibration and validation groups, respectively, the model gave areas under the receiver operating characteristic curves of 0.87 and 0.89, sensitivities of 90% and 87%, specificities of 78% and 80%, positive predictive values of 48% and 50%, negative predictive values of 97% and 96%, positive likelihood ratio of 4.09 and 4.35, negative likelihood ratio of 0.13 and 0.16, Youden index of 0.68 and 0.67, and correct classification rates of 80% and 81%. Conclusion: The results of this proof-of-concept study show that MIR-FEWS is a highly sensitive diagnostic method for ruling out SBP. The method warrants further investigation.

Performance of a diagnostic score for gouty arthritis: results from a cohort of acute arthritis suspected of being septic

Septic arthritis (SA) and gout are the main suspected etiologies of acute monoarthritis. Differentiating them is essential because SA is an emergency. The performance of a gout diagnostic score developed by Janssens et al. was investigated in a cohort of patients with acute arthritis suspected of being septic. This was an ancillary study of a single-center cohort of patients with suspected SA. Patients were classified into three groups according to the final diagnosis (gout, SA or other diagnosis). We assessed the performance of the score (sensitivity [Se], specificity [Sp], positive and negative predictive value [PPV, NPV], area under the receiver operating characteristic [ROC] curve) for the diagnosis of gouty arthritis. In total, 138 patients were included: 28 (20.3%) had gout, 42 (30.4%) SA, and 68 (49.3%) another diagnosis. The median diagnostic score was 7.0 [4.5; 8.8] for patients with gout, 3.5 [2.5; 6.0] for those with SA and 3.0 [2.0-5.0] for those with another diagnosis. With a score threshold of ≥ 8, the Se for a diagnosis of gout was 28.6%, Sp 96.4%, PPV 66.7%, and NPV 84.1%. With a threshold of ≤ 4, the Se was 82.1%, Sp 64.5%, PPV 37.1%, and NPV 93.4%. The area under the ROC for the diagnostic score was 0.79. The performance of the clinico-biological score of Janssens et al. for a diagnosis of gout applied to a cohort of patients with acute arthritis and suspected of being septic was poor. Joint aspiration remains necessary to differentiate SA from another etiology.

Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas

The diffuse growth of glioma cells leads to gliomatosis, which has less cure rate and high mortality. As the severity deepens, the treatment difficulty and mortality of glioma patients gradually increase. Therefore, a rapid and non-invasive diagnostic technique is very important for glioma patients. The target of this study is to classify contract subjects and glioma patients by serum mid-infrared spectroscopy combined with an ensemble learning method. The spectra were normalized and smoothed, and principal component analysis (PCA) was utilized for dimensionality reduction. Particle swarm optimization-support vector machine (PSO-SVM), decision tree (DT), logistic regression (LR) as well as random forest (RF) were used as base classifiers, and AdaBoost integrated learning was introduced. AdaBoost-SVM, AdaBoost-LR, AdaBoost-RF and AdaBoost-DT models were established to discriminate glioma patients. The single classification accuracy of the four models for the test set was 87.14%, 90.00%, 92.00% and 90.86%, respectively. For the purpose of further improving the prediction accuracy, the four models were fused at decision level, and the final classification accuracy of the test set reached 94.29%. Experiments show that serum infrared spectroscopy combined with the ensemble learning method algorithm shows wonderful potential in non-invasive, fast and precise identification of glioma patients, and can also be used for reference in intelligent diagnosis of other diseases.

Novel strategies to diagnose prosthetic or native bone and joint infections

INTRODUCTION

Bone and Joint Infections (BJI) are medically important, costly and occur in native and prosthetic joints. Arthroplasties will increase significantly in absolute numbers over time as well as the incidence of Prosthetic Joint Infections (PJI). Diagnosis of BJI and PJI is sub-optimal. The available diagnostic tests have variable effectiveness, are often below standard in sensitivity and/or specificity, and carry significant contamination risks during the collection of clinical samples. Improvement of diagnostics is urgently needed.

AREAS COVERED

We provide a narrative review on current and future diagnostic microbiology technologies. Pathogen identification, antibiotic resistance detection, and assessment of the epidemiology of infections via bacterial typing are considered useful for improved patient management. We confirm the continuing importance of culture methods and successful introduction of molecular, mass spectrometry-mediated and next-generation genome sequencing technologies. The diagnostic algorithms for BJI must be better defined, especially in the context of diversity of both disease phenotypes and clinical specimens rendered available.

EXPERT OPINION

Whether interventions in BJI or PJI are surgical or chemo-therapeutic (antibiotics and bacteriophages included), prior sensitive and specific pathogen detection remains a therapy-substantiating necessity. Innovative tests for earlier and more sensitive and specific detection of bacterial pathogens in BJI are urgently needed.