Anesthetic management of a patient with dilated cardiomyopathy and purpura for interventional thrombectomy of both femoral artery: Case report.

RATIONALE: Anesthesia management of patients with dilated cardiomyopathy (DCM) has always been a challenge for anesthesiologists. Eighty percent of patients with DCM have heart failure as the first symptom, which may be accompanied by arrhythmias, thromboembolism, etc. Thrombosis is a significant contributing factor to adverse cardiovascular and cerebrovascular events, and its risk is severely underestimated in the anesthetic management of DCM. PATIENT CONCERNS: We present a case of a 54-year-old hypersensitive female patient with dilated cardiomyopathy and purpura who underwent an interventional thrombectomy under general anesthesia following a lower limb thromboembolism. DIAGNOSIS: Patient underwent an interventional thrombectomy under general anesthesia, with in situ thrombosis occurring during the surgery. INTERVENTIONS: After maintaining stable hemodynamics, proceed with the intervention to retrieve the embolus. OUTCOME: Patients in the advanced DCM developed acute thrombosis twice during embolization. LESSONS: This case discusses the causes of intraoperative thrombosis and summarizes and reflects on the anesthesia management of this case, which has always been one of the difficult points for anesthesiologists to master. In the anesthesia management of DCM patients, it is also necessary to maintain hemodynamic stability, enhance perioperative coagulation management, use anticoagulants rationally, and avoid the occurrence of thrombotic events.

Stubborn hypoxemia after mild to moderate sedation and analgesia: A case report.

INTRODUCTION: Atelectasis typically denotes the partial or complete collapse of lung segments, lobes, or lobules in individuals, leading to a compromised respiratory function. The prevalence of perioperative atelectasis may be significantly underestimated, particularly among patients subjected to general anesthesia. PATIENT CONCERNS: This article conducts a retrospective analysis of a case involving refractory hypoxemia in a patient with a liver tumor who was admitted to Yanbian University Affiliated Hospital (Yanbian Hospital) after undergoing mild-to-moderate sedation and analgesia outside the operating room. DIAGNOSIS: Based on the results of CT examination and present history, the patient was diagnosed with intraoperative atelectasis. INTERVENTION: After the surgery, the patient was transferred to the recovery ward, where nasal oxygen therapy and nebulized inhalation treatment were administered. Vital signs were closely monitored at the bedside, gradually returning to the preoperative baseline. OUTCOME: Postoperatively, the patient developed atelectasis, with the percentage of lung opacity shown in the image decreasing from 9.2% of the total thoracic cage area to 8.4%. CONCLUSION: During non-intubated intravenous anesthesia, patients with compromised pulmonary conditions are more susceptible to refractory hypoxemia. Therefore, a personalized approach should be adopted regarding oxygen concentration and the dosage and type of medication. Additionally, preparations for appropriate airway management measures are essential to safeguard patient safety in the event of respiratory issues.

Genome-Wide Association Studies Reveal Candidate Genes Associated with Pigmentation Patterns of Single Feathers of Tianfu Nonghua Ducks.

In modern advanced genetics and breeding programs, the study of genes related to pigmentation in ducks is gaining much attention and popularity. Genes and DNA mutation cause variations in the plumage color traits of ducks. Therefore, discovering related genes responsible for different color traits and pigment patterns on each side of the single feathers in Chinese ducks is important for genetic studies. In this study, we collected feather images from 340 ducks and transported them into Image Pro Plus (IPP) 6.0 software to quantify the melanin content in the feathers. Thereafter, a genome-wide association study was conducted to reveal the genes responsible for variations in the feather color trait. The results from this study revealed that the pigmented region was larger in the male ducks as compared to the female ducks. In addition, the pigmented region was larger on the right side of the feather vane than on the left side in both dorsal and ventral feathers, and a positive correlation was observed among the feather color traits. Further, among the annotated genes, WNT3A, DOCK1, RAB1A, and ALDH1A3 were identified to play important roles in the variation in pigmented regions of the various feathers. This study also revealed that five candidate genes, including DPP8, HACD3, INTS14, SLC24A1, and DENND4A, were associated with the color pigment on the dorsal feathers of the ducks. Genes such as PRKG1, SETD6, RALYL, and ZNF704 reportedly play important roles in ventral feather color traits. This study revealed that genes such as WNT3A, DOCK1, RAB1A, and ALDH1A3 were associated with different pigmentation patterns, thereby providing new insights into the genetic mechanisms of single-feather pigmentation patterns in ducks.

Controlled co-immobilisation of proteins via 4'-phosphopantetheine-mediated site-selective covalent linkage.

In Escherichia coli, acyl carrier protein (ACP) is posttranslationally converted into its active holo-ACP form via covalent linkage of 4'-phosphopantetheine (4'-PP) to residue serine-36. We found that the long flexible 4'-PP arm could react chemoselectively with the iodoacetyl group introduced on solid supports with high efficiency under mild conditions. Based on this finding, we developed site-selective immobilisation of proteins via the active holo-ACP fusion tag, independently of the physicochemical properties of the protein of interest. Furthermore, the molecular ratios of co-immobilised proteins can be manipulated because the tethering process is predominantly directed by the molar concentrations of diverse holo-ACP fusions during co-immobilisation. Conveniently tuning the molecular ratios of co-immobilised proteins allows their cooperation, leading to a highly productive multi-protein co-immobilisation system. Kinetic studies of enzymes demonstrated that α-amylase (Amy) and methyl parathion hydrolase (MPH) immobilised via active tag holo-ACP had higher catalytic efficiency (kcat/Km) in comparison with their corresponding counterparts immobilised via the sulfhydryl groups (-SH) of these proteins. The immobilised holo-ACP-Amy also presented higher thermostability compared with free Amy. The enhanced α-amylase thermostability upon immobilisation via holo-ACP renders it more suitable for industrial application.

Design Meets Neuroscience: An Electroencephalogram Study of Design Thinking in Concept Generation Phase.

Extant research on design thinking is subjective and limited. This manuscript combines protocol analysis and electroencephalogram (EEG) to read design thoughts in the core design activities of concept generation phase. The results suggest that alpha band power had event related synchronization (ERS) in the scenario task and divergent thinking occupies a dominant position. However, it had event related desynchronization (ERD) in analogy and inference activities, etc., and it is stronger for mental pressure and exercised cognitive processing. In addition, the parietooccipital area differs significantly from other brain areas in most design activities. This study explores the relationship of different design thinking and EEG data, which is innovative and professional in the field of design, providing a more objective data basis and evaluation method for future applied research and diverse educational practices.

BEN-solo factors partition active chromatin to ensure proper gene activation in Drosophila.

The Drosophila genome encodes three BEN-solo proteins including Insensitive (Insv), Elba1 and Elba2 that possess activities in transcriptional repression and chromatin insulation. A fourth protein-Elba3-bridges Elba1 and Elba2 to form an ELBA complex. Here, we report comprehensive investigation of these proteins in Drosophila embryos. We assess common and distinct binding sites for Insv and ELBA and their genetic interdependencies. While Elba1 and Elba2 binding generally requires the ELBA complex, Elba3 can associate with chromatin independently of Elba1 and Elba2. We further demonstrate that ELBA collaborates with other insulators to regulate developmental patterning. Finally, we find that adjacent gene pairs separated by an ELBA bound sequence become less differentially expressed in ELBA mutants. Transgenic reporters confirm the insulating activity of ELBA- and Insv-bound sites. These findings define ELBA and Insv as general insulator proteins in Drosophila and demonstrate the functional importance of insulators to partition transcription units.

Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks.

BACKGROUND: Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks. The advent of electronic medical records (EMRs) has not only changed the format of medical records but also helped users to obtain information faster. However, there are many challenges regarding researching directly using Chinese EMRs, such as low quality, huge quantity, imbalance, semi-structure and non-structure, particularly the high density of the Chinese language compared with English. Therefore, effective word segmentation, word representation and model architecture are the core technologies in the literature on Chinese EMRs. RESULTS: In this paper, we propose a deep learning framework to study intelligent diagnosis using Chinese EMR data, which incorporates a convolutional neural network (CNN) into an EMR classification application. The novelty of this paper is reflected in the following: (1) We construct a pediatric medical dictionary based on Chinese EMRs. (2) Word2vec adopted in word embedding is used to achieve the semantic description of the content of Chinese EMRs. (3) A fine-tuning CNN model is constructed to feed the pediatric diagnosis with Chinese EMR data. Our results on real-world pediatric Chinese EMRs demonstrate that the average accuracy and F1-score of the CNN models are up to 81%, which indicates the effectiveness of the CNN model for the classification of EMRs. Particularly, a fine-tuning one-layer CNN performs best among all CNNs, recurrent neural network (RNN) (long short-term memory, gated recurrent unit) and CNN-RNN models, and the average accuracy and F1-score are both up to 83%. CONCLUSION: The CNN framework that includes word segmentation, word embedding and model training can serve as an intelligent auxiliary diagnosis tool for pediatricians. Particularly, a fine-tuning one-layer CNN performs well, which indicates that word order does not appear to have a useful effect on our Chinese EMRs.

An experimental study of the intrinsic stability of random forest variable importance measures.

BACKGROUND: The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. RESULTS: The experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability. CONCLUSION: First, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets.

Recombinant Passenger Proteins Can Be Conveniently Purified by One-Step Affinity Chromatography.

Fusion tag is one of the best available tools to date for enhancement of the solubility or improvement of the expression level of recombinant proteins in Escherichia coli. Typically, two consecutive affinity purification steps are often necessitated for the purification of passenger proteins. As a fusion tag, acyl carrier protein (ACP) could greatly increase the soluble expression level of Glucokinase (GlcK), α-Amylase (Amy) and GFP. When fusion protein ACP-G2-GlcK-Histag and ACP-G2-Amy-Histag, in which a protease TEV recognition site was inserted between the fusion tag and passenger protein, were coexpressed with protease TEV respectively in E. coli, the efficient intracellular processing of fusion proteins was achieved. The resulting passenger protein GlcK-Histag and Amy-Histag accumulated predominantly in a soluble form, and could be conveniently purified by one-step Ni-chelating chromatography. However, the fusion protein ACP-GFP-Histag was processed incompletely by the protease TEV coexpressed in vivo, and a large portion of the resulting target protein GFP-Histag aggregated in insoluble form, indicating that the intracellular processing may affect the solubility of cleaved passenger protein. In this context, the soluble fusion protein ACP-GFP-Histag, contained in the supernatant of E. coli cell lysate, was directly subjected to cleavage in vitro by mixing it with the clarified cell lysate of E. coli overexpressing protease TEV. Consequently, the resulting target protein GFP-Histag could accumulate predominantly in a soluble form, and be purified conveniently by one-step Ni-chelating chromatography. The approaches presented here greatly simplify the purification process of passenger proteins, and eliminate the use of large amounts of pure site-specific proteases.

Reliable multi-label learning via conformal predictor and random forest for syndrome differentiation of chronic fatigue in traditional Chinese medicine.

OBJECTIVE: Chronic Fatigue (CF) still remains unclear about its etiology, pathophysiology, nomenclature and diagnostic criteria in the medical community. Traditional Chinese medicine (TCM) adopts a unique diagnostic method, namely 'bian zheng lun zhi' or syndrome differentiation, to diagnose the CF with a set of syndrome factors, which can be regarded as the Multi-Label Learning (MLL) problem in the machine learning literature. To obtain an effective and reliable diagnostic tool, we use Conformal Predictor (CP), Random Forest (RF) and Problem Transformation method (PT) for the syndrome differentiation of CF. METHODS AND MATERIALS: In this work, using PT method, CP-RF is extended to handle MLL problem. CP-RF applies RF to measure the confidence level (p-value) of each label being the true label, and then selects multiple labels whose p-values are larger than the pre-defined significance level as the region prediction. In this paper, we compare the proposed CP-RF with typical CP-NBC(Naïve Bayes Classifier), CP-KNN(K-Nearest Neighbors) and ML-KNN on CF dataset, which consists of 736 cases. Specifically, 95 symptoms are used to identify CF, and four syndrome factors are employed in the syndrome differentiation, including 'spleen deficiency', 'heart deficiency', 'liver stagnation' and 'qi deficiency'. THE RESULTS: CP-RF demonstrates an outstanding performance beyond CP-NBC, CP-KNN and ML-KNN under the general metrics of subset accuracy, hamming loss, one-error, coverage, ranking loss and average precision. Furthermore, the performance of CP-RF remains steady at the large scale of confidence levels from 80% to 100%, which indicates its robustness to the threshold determination. In addition, the confidence evaluation provided by CP is valid and well-calibrated. CONCLUSION: CP-RF not only offers outstanding performance but also provides valid confidence evaluation for the CF syndrome differentiation. It would be well applicable to TCM practitioners and facilitate the utilities of objective, effective and reliable computer-based diagnosis tool.

Integrated application of uniform design and least-squares support vector machines to transfection optimization.

BACKGROUND: Transfection in mammalian cells based on liposome presents great challenge for biological professionals. To protect themselves from exogenous insults, mammalian cells tend to manifest poor transfection efficiency. In order to gain high efficiency, we have to optimize several conditions of transfection, such as amount of liposome, amount of plasmid, and cell density at transfection. However, this process may be time-consuming and energy-consuming. Fortunately, several mathematical methods, developed in the past decades, may facilitate the resolution of this issue. This study investigates the possibility of optimizing transfection efficiency by using a method referred to as least-squares support vector machine, which requires only a few experiments and maintains fairly high accuracy. RESULTS: A protocol consists of 15 experiments was performed according to the principle of uniform design. In this protocol, amount of liposome, amount of plasmid, and the number of seeded cells 24 h before transfection were set as independent variables and transfection efficiency was set as dependent variable. A model was deduced from independent variables and their respective dependent variable. Another protocol made up by 10 experiments was performed to test the accuracy of the model. The model manifested a high accuracy. Compared to traditional method, the integrated application of uniform design and least-squares support vector machine greatly reduced the number of required experiments. What's more, higher transfection efficiency was achieved. CONCLUSION: The integrated application of uniform design and least-squares support vector machine is a simple technique for obtaining high transfection efficiency. Using this novel method, the number of required experiments would be greatly cut down while higher efficiency would be gained. Least-squares support vector machine may be applicable to many other problems that need to be optimized.

Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine.

Most classifiers output predictions for new instances without indicating how reliable they could be. Transductive confidence machine (TCM) is a novel framework that provides hedged prediction coupled with valid confidence. Many popular machine learning algorithms can be transformed into the framework of TCM, and therefore be used for producing hedged predictions. This paper incorporates random forest (RF) to propose a method named TCM-RF for classification of chronic gastritis data. Our method benefits from TCM-RF's high performance when features are noisy, highly correlated and of mixed types. The experimental results show that TCM-RF produces informative as well as effective predictions.

Using random forest for reliable classification and cost-sensitive learning for medical diagnosis.

BACKGROUND: Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. RESULTS: In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. CONCLUSION: The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.