Nuclei micro-array FISH, a desirable alternative for MCL diagnosis

Mantle cell lymphoma (MCL) is a rare, specific lymphoma subtype. Though the morphologic and immunophenotypic features of MCL have been well described in recent literatures, it is still a diagnostic dilemma because of its frequent confusion with other small B cell lymphomas (SBCLs). In the present study, we primarily focus on establishing a sensitive and specific method for the diagnosis of MCL, which is efficient to distinguish this disease from other SBCLs. We carried out our investigation for MCL and other SBCLs (including SLL, FL, MZL, and MALT) on their feature of morphology, immunophenotype, and t(11;14)(q13;q32) translocation analysis based on polymerase chain reaction (PCR) and interphase nuclei micro-array fluorescence in situ hybridization (FISH). The morphologic and immunologic analysis showed the positive rate of cyclin D1 was 76.47% in MCL, which was significantly higher than that in other SBCLs. The positive rate of t(11;14) translocation was 25.81% and 35.48%, respectively, tested by general and semi-nested PCR, while 93.10% positive rate was shown with low background and strong signals pattern when tested by Nuclei micro-array FISH. Our research shows that t(11;14) translocation is a special and useful diagnostic marker for MCL, and detection of the marker by nuclei micro-array FISH is convenient and economic, especially more sensitive and specific than other methods for the diagnosis of MCL

Preparation and emulsifying properties of trace elements fortified gum arabic

Gum arabic was enriched with trace elements (Zn2+, Fe3+, Fe2+) by ion exchange against ZnCl2, FeCl3 and FeCl2. Trace elements content, molecular parameters and emulsifying properties of the gum arabic rich in trace elements (GARTE) were characterized by flame atomic absorption spectrometry (FAAS), gel permeation chromatography-multi angle laser light scattering (GPC-MALLS), interfacial rheometer, laser particle analyzer and zeta potentiometry. With trace elements, molecular weight and arabinogalactan protein (AGP) content of gum arabic have increased probably due to the high surface energy leading to the aggregation of protein. GARTE has good emulsion stability performance with increasing molecular weight and AGP content compared to the control gum arabic. GARTE can be applied as a natural functional ingredient for trace element fortification, where the ferric ions and zinc ions are chelated by the self-assembled polymer host

A modular network model of aging

Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein–protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative–reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging

Exercise preference in stroke survivors: a concept analysis

BackgroundExercise preference in stroke survivors is related to their adherence to long-term rehabilitation regimen and functional recovery. Although explored recently, the term exercise preference still lacks a clear definition.ObjectiveThe aim of this study is to conceptualize exercise preference in stroke survivors.MethodsThe Walker and Avant method was applied as a framework for the conceptual analysis of exercise preference. Data from 34 publications were collected using seven databases (PubMed, Web of Science, Embase, CINAHL, CNKI, Wanfang Data, and CBM) and applied in the analysis. The search period was from the inception of the database to April 30, 2023.ResultsExercise preference in stroke survivors was defined according to four attributes: priority of choice, behavioral tendency, affective priming, and patience in adherence. The common antecedents of the concept of exercise preference in stroke survivors were classified into patient-related, therapy-related, and environmental-related categories and the consequences were classified into three categories: patient-related, rehabilitation provider–related, and rehabilitation service system–related.ConclusionExercise preference in stroke survivors refers to the patient’s choice, tendency, affective response, and attitude toward engagement in the recommended rehabilitation regimen. It is beneficial for understanding the essential attributes of exercise preference in stroke survivors by clarifying the concept. In addition, it will facilitate the development of instruments for assessing exercise preference in stroke survivors and the construction of theory-based intervention programs that can improve adherence to exercise rehabilitation

The flavor-changing single-top quark production in the littlest Higgs model with T parity at the LHC

The littlest Higgs model with discrete symmetry named "T-parity"(LHT) is an interesting new physics model which does not suffer strong constraints from electroweak precision data. One of the important features of the LHT model is the existence of new source of FC interactions between the SM fermions and the mirror fermions. These FC interactions can make significant loop-level contributions to the couplings $tcV$, and furthermore enhance the cross sections of the FC single-top quark production processes. In this paper, we study some FC single-top quark production processes, $pp\to t\bar{c}$ and $pp\to tV$, at the LHC in the LHT model. We find that the cross sections of these processes are strongly depended on the mirror quark masses. The processes $pp\to t\bar{c}$ and $pp\to tg$ have large cross sections with heavy mirror quarks. The observation of these FC processes at the LHC is certainly the clue of new physics, and further precise measurements of the cross scetions can provide useful information about the free parameters in the LHT model, specially about the mirror quark masses.Comment: 20 pages, 5 figure

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE

Identification of the Proliferation/Differentiation Switch in the Cellular Network of Multicellular Organisms

The protein–protein interaction networks, or interactome networks, have been shown to have dynamic modular structures, yet the functional connections between and among the modules are less well understood. Here, using a new pipeline to integrate the interactome and the transcriptome, we identified a pair of transcriptionally anticorrelated modules, each consisting of hundreds of genes in multicellular interactome networks across different individuals and populations. The two modules are associated with cellular proliferation and differentiation, respectively. The proliferation module is conserved among eukaryotic organisms, whereas the differentiation module is specific to multicellular organisms. Upon differentiation of various tissues and cell lines from different organisms, the expression of the proliferation module is more uniformly suppressed, while the differentiation module is upregulated in a tissue- and species-specific manner. Our results indicate that even at the tissue and organism levels, proliferation and differentiation modules may correspond to two alternative states of the molecular network and may reflect a universal symbiotic relationship in a multicellular organism. Our analyses further predict that the proteins mediating the interactions between these modules may serve as modulators at the proliferation/differentiation switch

Genome-wide analysis of long non-coding RNAs (lncRNAs) in tea plants (Camellia sinensis) lateral roots in response to nitrogen application

Tea (Camellia sinensis) is one of the significant cash crops in China. As a leaf crop, nitrogen supply can not only increase the number of new shoots and leaves but also improve the tenderness of the former. However, a conundrum remains in science, which is the molecular mechanism of nitrogen use efficiency, especially long non-coding RNA (lncRNA). In this study, a total of 16,452 lncRNAs were identified through high-throughput sequencing analysis of lateral roots under nitrogen stress and control conditions, of which 9,451 were differentially expressed lncRNAs (DE-lncRNAs). To figure out the potential function of nitrogen-responsive lncRNAs, co-expression clustering was employed between lncRNAs and coding genes. KEGG enrichment analysis revealed nitrogen-responsive lncRNAs may involve in many biological processes such as plant hormone signal transduction, nitrogen metabolism and protein processing in endoplasmic reticulum. The expression abundance of 12 DE-lncRNAs were further verified by RT-PCR, and their expression trends were consistent with the results of RNA-seq. This study expands the research on lncRNAs in tea plants, provides a novel perspective for the potential regulation of lncRNAs on nitrogen stress, and valuable resources for further improving the nitrogen use efficiency of tea plants

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE