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We determined pH, morphological parameters of different parts of the digestive canal, and morphometric parameters of the stomach. The addition of prebiotic inulin to calves' diet causes the increase of pH in rumen, abomasum, and intestines but when inulin was added to S. cerevisiae, pH decreased and was even lower than in the control group. Prebiotic inulin and its synbiotic with yeast S. cerevisiae positively impact the development of almost all morphological structures of rumen saccus dorsalis, rumen saccus ventralis, and intestine; moreover, calves from the synbiotic group showed better results in virtually all parameters. However, both inulin and synbiotic did not affect the weight and relative weight of different parts of the stomach. Tested synbiotic has the potential to promote the development of the rumen and other parts of the digestive canal of calves.A single centre, single-blinded, prospective, randomized, controlled clinical study was conducted to evaluate the effectiveness of twice weekly fluorescent light energy therapy (Phovia™) as adjunct to systemic antibiotics in the management of deep pyoderma in dogs. Dogs with clinical lesions consistent with deep pyoderma, positive bacterial culture, and showing neutrophil engulfing bacteria at cytology were included in the study. Assessments were undertaken weekly for 8 weeks and every 2 weeks thereafter until 12 weeks after enrolment. At each visit, lesions were scored and cytology was conducted to determine a neutrophil engulfing bacteria score. All dogs (Groups A and B) were treated with systemic antibiotic twice daily, and Group B received additionally Phovia twice weekly. Median treatment duration was 11.7 weeks for Group A and 5.7 weeks for Group B. check details After 8 weeks of treatment, the percentage of dogs that achieved clinical resolution was 35.0% and 88.0% for Groups A and B, respectively. Lesion scores showed highly statistically significant difference in favour of Group B from week 3 to 8, and neutrophil engulfing bacteria scores showed statistical difference from week 2 onwards in favour of Group B. These results indicate that Phovia, when used as an adjunct to systemic antibiotics, can accelerate time to clinical resolution in cases of canine deep pyoderma.Myelodysplastic syndromes (MDS) are a spectrum of clonal stem-cell disorders characterized clinically by bone-marrow failure. Resultant cytopenias are responsible for significant mortality and decreased quality of life in patients with MDS. In patients with low-risk MDS (LR-MDS), anemia is the most common cytopenia and erythropoiesis-stimulating agents (ESA) are usually used as first-line therapy. Those patients who become refractory to ESA have a poor survival. Available treatment options such as lenalidomide, hypomethylating agents, and immunosuppressive therapy can provide some hematologic response among selected subsets of patients, however durable responses are limited, and these agents can carry significant adverse effects. Chronic transfusions help to alleviate symptoms of anemia but still carry risks associated with transfusion and iron overload. Luspatercept, recently approved for those LR-MDS with ring sideroblasts refractory to ESA, was found to have an improvement in transfusion independence with a well-tolerated safety profile. While anemia is the most common cytopenia, thrombocytopenia and neutropenia management is challenging and the co-occurrence of these cytopenias with anemia may dictate the choice of therapy. In this article, we review LR-MDS and discuss the optimal use of current treatment options and explore new therapeutic options on the horizon.This study aims to explore the application of blockchain technology in smart healthcare, establish a hierarchical theoretical framework of smart healthcare, reveal the impact of blockchain on smart healthcare, and finally, construct a development application system of smart healthcare under the blockchain based on stakeholder theory. However, such a hierarchical theoretical framework should consider not only the necessary attributes and the interrelationship among various aspects and attributes but also the role of multiple stakeholders. Therefore, the paper uses fuzzy set theory to filter unnecessary attributes, proposes a decision-making and experimental evaluation laboratory (DEMATEL) to manage the complex interrelationships between various aspects and attributes, and uses Interpretive Structure Modeling (ISM) to divide the hierarchy and construct a hierarchical theoretical framework. The results show that (1) the top-level design, the medical record management, and the doctor management are the root causes of system. (2) The specific application of blockchain in the field of smart healthcare is mainly carried out around the intelligent contract, which relies on the medical record management and is constrained by the system, and optimization of application is the key to system upgrading. (3) The internal and external regulation, the medical insurance, and the environmental governance play a guaranteed role for the development of the system and effectively safeguard the interests of stakeholders. (4) The application system of smart healthcare under the blockchain needs to be built based on three layers the transaction layer, information layer, and stakeholder layer. The theoretical hierarchical framework is intended to guide smart healthcare towards blockchain applications, and stakeholders are suggested to participate in the development application systems.Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline.