Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers.

BACKGROUND: Asthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy. METHODS: The clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC's performance was compared with five state-of-the-art predictive models. RESULTS: Experimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained [Formula: see text] values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance. CONCLUSION: As observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management.

Extraordinarily Time-and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep.

Canonical correlation analysis (CCA) is a correlation analysis technique that is widely used in statistics and the machine-learning community. However, the high complexity involved in the training process lays a heavy burden on the processing units and memory system, making CCA nearly impractical in large-scale data. To overcome this issue, a novel CCA method that tries to carry out analysis on the dataset in the Fourier domain is developed in this article. Appling Fourier transform on the data, we can convert the traditional eigenvector computation of CCA into finding some predefined discriminative Fourier bases that can be learned with only element-wise dot product and sum operations, without complex time-consuming calculations. As the eigenvalues come from the sum of individual sample products, they can be estimated in parallel. Besides, thanks to the data characteristic of pattern repeatability, the eigenvalues can be well estimated with partial samples. Accordingly, a progressive estimate scheme is proposed, in which the eigenvalues are estimated through feeding data batch by batch until the eigenvalues sequence is stable in order. As a result, the proposed method shows its characteristics of extraordinarily fast and memory efficiencies. Furthermore, we extend this idea to the nonlinear kernel and deep models and obtained satisfactory accuracy and extremely fast training time consumption as expected. An extensive discussion on the fast Fourier transform (FFT)-CCA is made in terms of time and memory efficiencies. Experimental results on several large-scale correlation datasets, such as MNIST8M, X-RAY MICROBEAM SPEECH, and Twitter Users Data, demonstrate the superiority of the proposed algorithm over state-of-the-art (SOTA) large-scale CCA methods, as our proposed method achieves almost same accuracy with the training time of our proposed method being 1000 times faster. This makes our proposed models best practice models for dealing with large-scale correlation datasets. The source code is available at https://github.com/Mrxuzhao/FFTCCA.

Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE: This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS: In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS: Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS: The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.

Spinal cord dorsal horn sensory gate in preclinical models of chemotherapy-induced painful neuropathy and contact dermatitis chronic itch becomes less leaky with Kcc2 gene expression-enhancing treatments.

Low intraneuronal chloride in spinal cord dorsal horn (SCDH) pain relay neurons is of critical relevance for physiological transmission of primary sensory afferents because low intraneuronal chloride dictates GABA-ergic and glycin-ergic neurotransmission to be inhibitory. If neuronal chloride rises to unphysiological levels, the primary sensory gate in the spinal cord dorsal horn becomes corrupted, with resulting behavioral hallmarks of hypersensitivity and allodynia, for example in pathological pain. Low chloride in spinal cord dorsal horn neurons relies on the robust gene expression of Kcc2 and sustained transporter function of the KCC2 chloride-extruding electroneutral transporter. Based on a recent report where we characterized the GSK3-inhibitory small molecule, kenpaullone, as a Kcc2 gene expression-enhancer that potently repaired diminished Kcc2 expression and KCC2 transporter function in SCDH pain relay neurons, we extend our recent findings by reporting (i) effective pain control in a preclinical model of taxol-induced painful peripheral neuropathy that was accomplished by topical application of a TRPV4/TRPA1 dual-inhibitory compound (compound 16-8), and was associated with the repair of diminished Kcc2 gene expression in the SCDH; and (ii) potent functioning of kenpaullone as an antipruritic in a DNFB contact dermatitis preclinical model. These observations suggest that effective peripheral treatment of chemotherapy-induced painful peripheral neuropathy impacts the pain-transmitting neural circuit in the SCDH in a beneficial manner by enhancing Kcc2 gene expression, and that chronic pruritus might be relayed in the primary sensory gate of the spinal cord, following similar principles as pathological pain, specifically relating to the critical functioning of Kcc2 gene expression and the KCC2 transporter function.

Maximum neighborhood margin discriminant projection for classification.

We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.

Inhibition of phosphodiesterases leads to prevention of the mitochondrial permeability transition pore opening and reperfusion injury in cardiac H9c2 cells.

PURPOSE: We tested if inhibition of phosphodiesterases (PDEs) with IBMX (1-methyl-3-isobutylxanthine) can modulate the mitochondrial permeability transition pore (mPTP) opening by inactivating glycogen synthase kinase 3ß (GSK-3ß). METHODS: H9c2 cells were exposed to 600 µM H(2)O(2) for 20 min to cause the mPTP opening. Mitochondrial membrane potential (ΔΨm) was assessed by imaging cells loaded with tetramethylrhodamine ethyl ester (TMRE). Cell viability was measured with propidium iodide (PI) fluorometry using a fluorescence reader. Ischemia/reperfusion injury was induced by exposing cells to ischemic solution for 90 min followed by 30 min of reperfusion. RESULTS: IBMX reduced loss of ΔΨm caused by H(2)O(2), indicating that inhibition of PDEs can prevent the mPTP opening. However, IBMX could not inhibit the pore opening in cells transfected with the constitutively active GSK-3ß (GSK-3ß-S9A) mutant, suggesting a critical role of GSK-3ß in the action of IBMX. IBMX also reduced reperfusion injury in a GSK-3ß dependent manner. In support, IBMX increased GSK-3ß phosphorylation at Ser(9), an effect that was reversed by both the PKA inhibitor H89 and the PKG inhibitor KT5823. In support, IBMX activated both PKA and PKG. IBMX failed to prevent the loss of ΔΨm in the presence of H89 or PKA siRNA. Similarly, both KT5823 and PKG siRNA reversed the protective effect of IBMX. CONCLUSION: Inhibition of PDEs prevents the mPTP opening by inactivating GSK-3ß through PKA and PKG. GSK-3ß is a common downstream target of PKA and PKG. Inhibition of PDEs may be a useful approach to prevent reperfusion injury.

The critical role of intracellular zinc in adenosine A(2) receptor activation induced cardioprotection against reperfusion injury.

Exogenous zinc can protect cardiac cells from reperfusion injury, but the exact roles of endogenous zinc in the pathogenesis of reperfusion injury and in adenosine A(2) receptor activation-induced cardioprotection against reperfusion injury remain unknown. Adenosine A(1)/A(2) receptor agonist 5'-(N-ethylcarboxamido) adenosine (NECA) given at reperfusion reduced infarct size in isolated rat hearts subjected to 30min ischemia followed by 2h of reperfusion. This effect of NECA was partially but significantly blocked by the zinc chelator N,N,N',N'-tetrakis-(2-pyridylmethyl) ethylenediamine (TPEN), and ZnCl(2) given at reperfusion mimicked the effect of NECA by reducing infarct size. Total tissue zinc concentrations measured with inductively coupled plasma optical emission spectroscopy (ICPOES) were decreased upon reperfusion in rat hearts and this was reversed by NECA. NECA increased intracellular free zinc during reperfusion in the heart. Confocal imaging study showed a rapid increase in intracellular free zinc in isolated rat cardiomyocytes treated with NECA. Further experiments revealed that NECA increased total zinc levels upon reperfusion in mitochondria isolated from isolated hearts. NECA attenuated mitochondrial swelling upon reperfusion in isolated hearts and this was inhibited by TPEN. Similarly, NECA prevented the loss of mitochondrial membrane potential (DeltaPsim) caused by oxidant stress in cardiomyocytes. Finally, both NECA and ZnCl(2) inhibited the mitochondrial metabolic activity. NECA-induced cardioprotection against reperfusion injury is mediated by intracellular zinc. NECA prevents reperfusion-induced zinc loss and relocates zinc to mitochondria. The inhibitory effects of zinc on both the mPTP opening and the mitochondrial metabolic activity may account for the cardioprotective effect of NECA.

Adenosine A2A and A2B receptors work in concert to induce a strong protection against reperfusion injury in rat hearts.

We aimed to test if stimulation of both adenosine A2A and A2B receptors is required to produce an effective cardioprotection against reperfusion injury. Isolated rat hearts were subjected to 30-min regional ischemia followed by 2 h of reperfusion. The adenosine A1/A2 receptor agonist 5'-(N-ethylcarboxamido) adenosine (NECA) given at reperfusion reduced infarct size, an effect that was reversed by both the adenosine A2A antagonist SCH58261 and the A2B antagonist MRS1706. The A2B agonist BAY 60-6583 but not the selective A2A agonist CGS21680 reduced infarct size. Interestingly, a combination of BAY 60-6583 and CGS21680 further reduced infarct size. These results suggest that both A2A and A2B receptors are involved in NECA's anti-infarct effect at reperfusion. NECA attenuated mitochondrial swelling upon reperfusion and this was blocked by both SCH58261 and MRS1706, indicating that activation of A2 receptors with NECA can modulate reperfusion-induced mitochondrial permeability transition pore (mPTP) opening. In support, NECA also prevented oxidant-induced loss of mitochondrial membrane potential (DeltaPsi(m)) and matrix Ca2+ overload in cardiomyocytes via both the A2 receptors. In addition, NECA increased mitochondrial glycogen synthase kinase-3beta (GSK-3beta) phosphorylation upon reperfusion and this was again blocked by SCH58261 and MRS1706. In conclusion, A2A and A2B receptors work in concert to prevent reperfusion injury in rat hearts treated with NECA. NECA may protect the heart by modulating the mPTP opening through inactivating mitochondrial GSK-3beta. A simultaneous stimulation of A2A and A2B receptors at reperfusion is required to produce a strong cardioprotection against reperfusion injury.

Phylogenetic analysis in chicken breeds inferred from complete cytochrome b gene information.

Complete cytochrome b (cyt b) gene (1140 bp) nucleotide sequences were used to investigate characteristics of the genetic constitution of Chunky broiler chickens, and these were compared with the Hy-Line and WL-GM (Garber) line of White Leghorn, the GSP line of Fayoumi, the BM-C line of Black Minorca (egg-chickens), and an outgroup of wild-origin Japanese quail. A high genetic difference (five haplotypes) was observed at the cytochrome b region in the Chunky broiler in contrast to the high homologies observed among the other chicken breeds (egg-purpose). Chunky broilers can be distinguished from the other breeds (White Leghorn, Fayoumi, and Black Minorca) at positions 552 and 779. The molecular phylogenetic tree exhibited genetic differences within Chunky broilers, and between Chunky broilers and the other three chicken breeds. As a result, some chicken strains or breeds apparently different from the other egg-chickens may have contributed to the Chunky broiler formation. Artificial selection may be one of the biggest factors causing nucleotide diversity in the chicken breeds.