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Nevin Manimala Statistics

Non-calcified active atherosclerosis plaque detection with 18F-NaF and 18F-FDG PET/CT dynamic imaging

Phys Eng Sci Med. 2023 Jan 30. doi: 10.1007/s13246-023-01218-7. Online ahead of print.

ABSTRACT

Arterial inflammation is an indicator of atheromatous plaque vulnerability to detach and to obstruct blood vessels in the heart or in the brain thus causing heart attack or stroke. To date, it is difficult to predict the plaque vulnerability. This study was aimed to assess the behavior of 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG) uptake in the aorta and iliac arteries as a function of plaque density on CT images. We report metabolically active artery plaques associated to inflammation in the absence of calcification. 18 elderly volunteers were recruited and imaged with computed tomography (CT) and positron emission tomography (PET) with 18F-NaF and 18F-FDG. A total of 1338 arterial segments were analyzed, 766 were non-calcified and 572 had calcifications. For both 18F-NaF and 18F-FDG, the mean SUV values were found statistically significantly different between non-calcified and calcified artery segments. Clustering CT non-calcified segments, excluding blood, resulted in two clusters C1 and C2 with a mean density of 30.63 ± 5.06 HU in C1 and 43.06 ± 4.71 HU in C2 (P < 0.05), and their respective SUV were found statistically different in 18F-NaF and 18F-FDG. The 18F-NaF images showed plaques not detected on CT images, where the 18F-FDG SUV values were high in comparison to artery walls without plaques. The density on CT images alone corresponding to these plaques could be further investigated to see whether it can be an indicator of the active plaques.

PMID:36715851 | DOI:10.1007/s13246-023-01218-7

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Nevin Manimala Statistics

Morphological Variation in the Pelvic Floor Muscle Complex of Nulliparous, Pregnant, and Parous Women

Ann Biomed Eng. 2023 Jan 30. doi: 10.1007/s10439-023-03150-z. Online ahead of print.

ABSTRACT

Specific levator ani muscle imaging measures change with pregnancy and vaginal parity, though entire pelvic floor muscle complex (PFMC) shape variation related to pregnancy-induced and postpartum remodeling has never been quantified. We used statistical shape modeling to compute the 3D variation in PFMC morphology of reproductive-aged nulliparous, late pregnant, and parous women. Pelvic magnetic resonance images were collected retrospectively and PFMCs were segmented. Modes of variation and principal component scores, generated via statistical shape modeling, defined significant morphological variation. Nulliparous (have never given birth), late pregnant (3rd trimester), and parous (have given birth and not currently pregnant) PFMCs were compared via MANCOVA. The overall PFMC shape, mode 2, and mode 3 significantly differed across patient groups (p < 0.001, = 0.002, = 0.001, respectively). This statistical shape analysis described greater perineal and external anal sphincter descent, increased iliococcygeus concavity, and a proportionally wider mid-posterior levator hiatus in late pregnant compared to nulliparous and parous women. The late pregnant group was the most divergent, highlighting differences that likely reduce the mechanical burden of vaginal childbirth. This robust quantification of PFMC shape provides insight to pregnancy and postpartum remodeling and allows for generation of representative non-patient-specific PFMCs that can be used in biomechanical simulations.

PMID:36715838 | DOI:10.1007/s10439-023-03150-z

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Nevin Manimala Statistics

Cancer-associated fibroblasts in papillary thyroid carcinoma

Clin Exp Med. 2023 Jan 30. doi: 10.1007/s10238-023-00998-2. Online ahead of print.

ABSTRACT

Papillary thyroid carcinoma (PTC) has a relatively good prognosis, yet there are some invasive PTC cases with worse clinicopathological features and poor outcome. Cancer-associated fibroblasts (CAFs) play an important role in cancer invasion and metastasis. This study aimed to investigate the expression of marker proteins of CAFs in PTC and their correlations with clinicopathological features through immunohistochemistry. The medical records of 125 PTC patients were reviewed in this study, whose specimens were retrieved for immunohistochemistry. Four CAFs marker proteins, FAP fibroblast activated protein (FAP), α-smooth muscle actin (α-SMA), Vimentin and platelet-derived growth factor receptor-α(PDGFR-α), were stained and scored. Then, statistical analyses were performed. The immunoreactivity scores of FAP and α-SMA correlated with tumor size, BRAF mutation, extrathyroidal, invasion, pathological subtype, lymph node metastasis and ATA risk stratification. Moreover, binary logistic regression analysis and receiver operating characteristic curves showed that high FAP and α-SMA immunoreactivity scores were risk factors for extrathyroidal invasion, BRAF mutation, multi-focality and lymph node metastasis (especially N1b) with good sensitivity and accuracy in prediction. A better performance was found in FAP than α-SMA. Strong expressions of CAFs were risk factors for worse thyroid cancer clinicopathological features. FAP was the better CAFs marker for PTC.

PMID:36715834 | DOI:10.1007/s10238-023-00998-2

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Nevin Manimala Statistics

Evaluation of machine learning algorithms for groundwater quality modeling

Environ Sci Pollut Res Int. 2023 Jan 30. doi: 10.1007/s11356-023-25596-3. Online ahead of print.

ABSTRACT

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings.

PMID:36715809 | DOI:10.1007/s11356-023-25596-3

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Nevin Manimala Statistics

Coupling machine learning with signal process techniques and particle swarm optimization for forecasting flood routing calculations in the Eastern Black Sea Basin, Türkiye

Environ Sci Pollut Res Int. 2023 Jan 30. doi: 10.1007/s11356-023-25496-6. Online ahead of print.

ABSTRACT

With the effect of global warming, the frequency of floods, one of the most important natural disasters, increases, and this increases the damage it causes to people and the environment. Flood routing models play an important role in predicting floods so that all necessary precautions are taken before floods reach the region, loss of life and property in the region is prevented, and agricultural lands are protected. This research aims to compare the performance of hybrid machine learning models such as least-squares support vector machine technique hybridized with particle swarm optimization, empirical mode decomposition, variational mode decomposition, and discrete wavelet transform processes for flood routing estimation models in Ordu, Eastern Black Sea Basin, Türkiye. In addition, it is aimed to examine the effect of data division in flood forecasting. Accordingly, 70%, 80%, and 90% of the data were used for training, respectively. For this purpose, the flood data of 2009 and 2013 in Ordu were used. The performance of the established models was evaluated with the help of statistical indicators such as mean bias error, mean absolute percentage error, determination coefficient, Nash-Sutcliffe efficiency, Taylor Diagrams, and boxplot. As a result of the study, the particle swarm optimization least-squares support vector machine technique was chosen as the most successful model in predicting flood routing results. In addition, the optimum data partition ratio was found to be Train:70:Test:30 in the flood routing calculation. The findings are essential regarding flood management and taking necessary precautions before the flood occurs.

PMID:36715798 | DOI:10.1007/s11356-023-25496-6

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Nevin Manimala Statistics

Does previous fragility fracture impact upon mortality in a hip fracture cohort? a retrospective study

Ir J Med Sci. 2023 Jan 30. doi: 10.1007/s11845-022-03267-5. Online ahead of print.

ABSTRACT

BACKGROUND: Fragility fractures are described as fractures resulting from low-energy trauma and are considered diagnostic of reduced bone mineral density or osteoporosis. They often present as hip fractures with hip fractures remaining a common but devastating injury among older patients. Many factors influence a patient’s risk of hip fracture and their subsequent risk of death.

AIM: In this study, we examined if previous fragility fracture impacts upon mortality after hip fracture.

METHODS: This was a retrospective single-center cohort study of patients included in the Irish Hip Fracture registry over a 5-year time period. Epidemiological data including gender, age, type of fracture, type of surgery, bone protection medication, American Society of Anesthetics (ASA) grade, and post-fracture outcomes including death at 30 days and death at 1 year were recorded. The presence or absence of a previous fragility fracture was examined to explore if a previous fragility fracture was an independent predictor of mortality.

RESULTS: There were 964 patients included, and 290 of whom had sustained a previous fragility fracture; 289 patients were males and 675 females, 33 patients had died in the 30 days following their surgery, and 180 patients had died within 1 year. We found statistically significant results for gender and age but not for previous fragility fracture influencing mortality (p value 0.230).

CONCLUSION: We found that previous fragility fracture does not impact upon mortality in a hip fracture cohort. However, gender and age did impact upon mortality in this study.

PMID:36715792 | DOI:10.1007/s11845-022-03267-5

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Nevin Manimala Statistics

The influence of a probiotic/prebiotic supplement on microbial and metabolic parameters of equine cecal fluid or fecal slurry in vitro

J Anim Sci. 2023 Jan 28:skad034. doi: 10.1093/jas/skad034. Online ahead of print.

ABSTRACT

The microbes that reside within the equine hindgut create a complex and dynamic ecosystem. The equine hindgut microbiota is intimately associated with health and, as such, represents an area which can be beneficially modified. Synbiotics, supplements that combine probiotic microorganisms with prebiotic ingredients, are a potential means of influencing the hindgut microbiota to promote health and prevent disease. The objective of the current study was to evaluate the influence of an equine probiotic/prebiotic supplement on characteristics of the microbiota and metabolite production in vitro. Equine cecal fluid and fecal material were collected from an abattoir in QC, CAN. Five hundred ml of cecal fluid was used to inoculate chemostat vessels maintained as batch fermenters (chemostat cecal, n=11) with either 0g (control) or 0.44g of supplement added at 12h intervals. One hundred ml of cecal fluid (anaerobic cecal, n=15) or 5% fecal slurry (anaerobic fecal, n=6) were maintained in an anaerobic chamber with either 0g (control) or 0.356g of supplement added at the time of vessel establishment. Samples were taken from vessels at vessel establishment (0h), 24h, or 48h of incubation. Illumina sequencing of the V4 region of the 16S rRNA gene and bioinformatics were performed for microbiome analysis. Metabolite data was obtained via NMR spectroscopy. All statistical analyses were run in SAS 9.4. There was no effect of treatment at 24h or 48h on alpha or beta diversity indices and limited taxonomic differences were noted. Acetate, propionate, and butyrate were higher in treated compared to untreated vessels in all methods. A consistent effect of supplementation on the metabolic profile with no discernable impact on the microbiota of these in vitro systems indicates inoculum microbe viability and a utilization of the provided fermentable substrate within the systems. Although no changes within the microbiome were apparent, the consistent changes in metabolites indicates a potential prebiotic effect of the added supplement and merits further exploration.

PMID:36715114 | DOI:10.1093/jas/skad034

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Nevin Manimala Statistics

Automated Creak Differentiates Adductor Laryngeal Dystonia and Muscle Tension Dysphonia

Laryngoscope. 2023 Jan 30. doi: 10.1002/lary.30588. Online ahead of print.

ABSTRACT

OBJECTIVE: The purpose of this study was to determine whether automated estimates of vocal creak would differentiate speakers with adductor laryngeal dystonia (AdLD) from speakers with muscle tension dysphonia (MTD) and speakers without voice disorders.

METHODS: Sixteen speakers with AdLD, sixteen speakers with MTD, and sixteen speakers without voice disorders were recorded in a quiet environment reading aloud a standard paragraph. An open-source creak detector was used to calculate the percentage of creak (% creak) in each of the speaker’s six recorded sentences.

RESULTS: A Kruskal-Wallis one-way analysis of variance revealed a statistically significant effect of group on the % creak with a large effect size. Pairwise Wilcoxon tests revealed a statistically significant difference in % creak between speakers with AdLD and controls as well as between speakers with AdLD and MTD. Receiver operating characteristic curve analyses indicated that % creak differentiated AdLD from both controls and speakers with MTD with high sensitivity and specificity (area under the curve statistics of 0.94 and 0.86, respectively).

CONCLUSION: Percentage of creak as calculated by an automated creak detector may be useful as a quantitative indicator of AdLD, demonstrating the potential for use as a screening tool or to aid in a differential diagnosis.

LEVEL OF EVIDENCE: 3 Laryngoscope, 2023.

PMID:36715109 | DOI:10.1002/lary.30588

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Nevin Manimala Statistics

Mother-Child Neural Synchronization Is Time Linked to Mother-Child Positive Affective State Matching

Soc Cogn Affect Neurosci. 2023 Jan 27:nsad001. doi: 10.1093/scan/nsad001. Online ahead of print.

ABSTRACT

BACKGROUND: In the first years of life, in which self-regulation occurs via external means, mother-child synchronization of positive affect (PA) facilitates regulation of child homeostatic systems. Mother-child affective synchrony may contribute to mother-child synchronization of neural systems, but limited research has explored this possibility.

METHODS: Participants were 41 healthy mother-child dyads (56% girls; Mage=24.76 months; SD=8.77 months, Range=10 to 42 months). Mothers’ and children’s brain activity were assessed simultaneously using near-infrared spectroscopy while engaging in dyadic play. Mother and child PA during play were coded separately to characterize periods in which mothers and children (1) matched on high PA (2) matched on low/no PA or (3) showed a mismatch in PA. Models evaluated moment-to-moment correlations between affective matching and neural synchrony in mother-child dyads.

RESULTS: Greater positive affective synchrony, in which mother and child showed similarly high levels of PA but not similarly low levels of PA, was related to greater synchrony in medial and lateral frontal and temporoparietal regions. Age moderated associations between mother and child neural activity, but only during moments of high PA state matching.

CONCLUSIONS: Positive, synchronous mother-child interactions may foster greater neural responding in affective and social regions important for self-regulation and interpersonal bonds.

PMID:36715078 | DOI:10.1093/scan/nsad001

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Nevin Manimala Statistics

A sensitive LC-MS/MS Method for the Simultaneous Determination of Skimmin, a Potential Agent for Treating Postpartum Stroke, and Its Metabolite Umbelliferone in Rat Plasma

J AOAC Int. 2023 Jan 28:qsad012. doi: 10.1093/jaoacint/qsad012. Online ahead of print.

ABSTRACT

BACKGROUND: Skimmin, a potential agent for treating postpartum stroke, is one of the most important coumarins extracted from the leave of skimmia.

OBJECTIVE: In this study, a specific, sensitive and simple high performance liquid chromatography with tandem mass spectrometry method for the simultaneous determination of skimmin and its metabolite umbelliferone in rat plasma was established and validated.

METHODS: Chromatographic separation was performed by an Inertsil ODS-3 column (50 mm × 4.6 mm, 5 μm) with a mobile phase consisting of 0.1% formic acid in distilled water-acetonitrile at a flow rate of 0.5 mL/min with gradient elution mode. All analytes were detected and quantified in negative multiple reaction monitoring.

RESULTS: All calibration curves shown good linearity (r > 0.995) over the concentration range of 10-10000 ng/mL and 2.0-2000 ng/mL for skimmin and umbelliferone, respectively. The selectivity, sensitivity, extraction recovery, matrix effect, and stability met all requirements.

CONCLUSIONS: The analysis method was successfully applied to pharmacokinetic study of skimmin and umbelliferone in rats following oral administration of skimmin at the doses of 10, 30 and 90 mg/kg. With the exception of AUC(0-∞) and Cmax, MRT and Cl/F of skimmin had significant statistical difference with the increasing doses. Skimmin might exhibit nonlinear pharmacokinetic characteristics in rats.

HIGHLIGHTS: This was the first study to investigate the pharmacokinetic characteristics of skimmin as a candidate agent for treating postpartum stroke.

PMID:36715062 | DOI:10.1093/jaoacint/qsad012