Comparison of seven modelling algorithms for GABA-edited 1H-MRS

Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.

Damages of Neuromuscular System After Mechanical-Induced Limb Ischemia (Experimental Study)

Relevance. Traumatic and ischemic injury of the limbs is accompanied by damage of the skeletal muscles and peripheral nerves of the limbs. The dynamics and consequences of ischemic lesions remain poorly understood and need to be corrected. Objective: using quantitative morphological and sonographic methods, to study the dynamics of skeletal muscle damage of the limb after traumatically induced ischemia with and without the injection of platelet-rich plasma, bone marrow aspirate, and adipose tissue fraction. Materials and Methods. In 3 experiments, rabbits were modeled with 6-hour limb ischemia by applying an elastic tourniquet. After compartment syndrome detection, based on the assessment of subfascial pressure, cell suspensions were injected into the leg muscles. Sonographic and histological examination of the muscles was performed on days 5, 15, and 30. The results of sonography and morphometry were evaluated by statistical methods. Results. The developed model of ischemia consists of 6-hour immobilization of the limb, on тwhich medical elastic tourniquets were imposing. The action of the tourniquets causes high subfascial pressure and necrosis of the superficial muscle groups of the lower third of the thigh and lower leg. According to sonography, the δ-entropy of damaged tissues on day 5 is reduced relative to the intact limb, as in the case of administration of bone marrow aspirate cells. On days 15 and 30, sonography showed no difference between the comparison groups. The dynamics of morphological features of limb tissue damage consist of necrosis of superficial muscle groups, atrophy in the middle layers, and almost intact deep muscle groups. Necrosis was replaced by scar tissue, the density of which increases 11-14 times, and does not differ in the period 5-30 days. The administration of platelet plasma, bone marrow aspirate, and adipose tissue fraction did not change the dynamics of fibrotic changes in ischemic damaged muscles. Muscle atrophy is accompanied by activation of endogenous repair of single muscle fibers, which tended to intensify after injection of bone marrow aspirate. The sciatic nerve of the injured limb was not structurally damaged according to the deep topography, while the nerves of the tibia develop degenerative changes from the 15th day.

Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome

Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically.

Not all voxels are created equal: reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of neurites relating to their organisation and processing capacity that are essential for effective neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analysed in regions-of-interest (ROI) to identify brain behaviour associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional method induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. The tissue-weighted mean robustly identified disease-related differences in ROIs such as the fornix (p<0.05, Bonferroni corrected), some of which were absent using the conventional mean. The tissue-weighted mean may generate new insight into microstructural disease-related effects in regions typically confounded by partial volume, representing a promising tool for the study of microstructural correlates of aging and neurodegenerative diseases.

Fresh autologous stromal tissue fraction for the treatment of knee osteoarthritis related pain and disability

Knee osteoarthritis (KOA) is a very common condition with multifactorial etiology leading to severe pain and disability in the adult population. Although KOA is considered a non-inflammatory arthritis, upregulation of inflammatory and catabolic pathways with increased production of proinflammatory cytokines leading to cartilage degradation and extracellular matrix degeneration has been reported. Intra-articular injection of fresh fat derived stromal vascular fraction (SVF) fraction has been proposed as a valid and alternative treatment for symptomatic KOA that guarantees mechanical support through viscosupplementation, anti-inflammatory, and anabolic action. We retrospectively reviewed a case series of 84 consecutive adult patients with KOA who underwent intra-articular injection of fresh fat derived SVF. Significant improvement in pain levels (NRS score decrease 3.5±1.1, p<0.001), WOMAC pain (-7.02±3.45 score change, p<0.001), WOMAC stiffness (-1.97±1.02, p<0.001), and ROM improvement (+17.13±5.22°, p<0.001). The only complication noted was knee joint swelling lasting for less than 7 days after the injection in 7% of the patients.

In utero exposures to electronic-cigarette aerosols impair the Wnt signaling during mouse lung development

Currently, more than 9 million American adults, including women of childbearing age, use electronic-cigarettes (e-cigs). Further, the prevalence of maternal vaping now approaching 10% is similar to that of maternal smoking. Little, however, is known about the effects of fetal exposures to nicotine-rich e-cig aerosols on lung development. In this study, we assessed whether in utero exposures to e-cig aerosols compromised lung development in mice. A third-generation e-cig device was used to expose pregnant BALB/c mice by inhalation to 36 mg/mL of nicotine cinnamon-flavored e-cig aerosols for 14–31 days. This included exposures for either 12 days before mating plus during gestation (preconception groups) or only during gestation (prenatal groups). Respective control mice were exposed to filtered air. Subgroups of offspring were euthanized at birth or at 4 wk of age. Compared with respective air-exposed controls, both preconception and prenatal exposures to e-cig aerosols significantly decreased the offspring birth weight and body length. In the preconception group, 7 inflammation-related genes were downregulated, including 4 genes common to both dams and fetuses, denoting an e-cig immunosuppressive effect. Lung morphometry assessments of preconception e-cig-exposed offspring showed a significantly increased tissue fraction at birth. This result was supported by the downregulation of 75 lung genes involved in the Wnt signaling, which is essential to lung organogenesis. Thus, our data indicate that maternal vaping impairs pregnancy outcomes, alters fetal lung structure, and dysregulates the Wnt signaling. This study provides experimental evidence for future regulations of e-cig products for pregnant women and developmentally vulnerable populations.