Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of Diaphragm Mechanics in Pompe Disease Using Dynamic 3D MRI

Background Diaphragm weakness is the main reason for respiratory dysfunction in patients with Pompe disease, a progressive metabolic myopathy affecting respiratory and limb-girdle muscles. Since respiratory failure is the major cause of death among adult patients, early identification of respiratory muscle involvement is necessary to initiate treatment in time and possibly prevent irreversible damage. In this paper we investigate the suitability of dynamic MR imaging in combination with state-of-the-art image analysis methods to assess respiratory muscle weakness. Methods The proposed methodology relies on image registration and lung surface extraction to quantify lung kinematics during breathing. This allows for the extraction of geometry and motion features of the lung that characterize the independent contribution of the diaphragm and the thoracic muscles to the respiratory cycle. Results Results in 16 3D+t MRI scans (10 Pompe patients and 6 controls) of a slow expiratory maneuver show that kinematic analysis from dynamic 3D images reveals important additional information about diaphragm mechanics and respiratory muscle involvement when compared to conventional pulmonary function tests. Pompe patients with severely reduced pulmonary function showed severe diaphragm weakness presented by minimal motion of the diaphragm. In patients with moderately reduced pulmonary function, cranial displacement of posterior diaphragm parts was reduced and the diaphragm dome was oriented more horizontally at full inspiration compared to healthy controls. Conclusion Dynamic 3D MRI provides data for analyzing the contribution of both diaphragm and thoracic muscles independently. The proposed image analysis method has the potential to detect less severe diaphragm weakness and could thus be used to determine the optimal start of treatment in adult patients with Pompe disease in prospect of increased treatment response.
CorNet: Assigning function to networks of co-evolving residues by automated literature mining

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.
Establishment of a normal-derived estrogen receptor-positive cell line comparable to the prevailing human breast cancer subtype

Understanding human cancer increasingly relies on insight gained from subtype specific comparisons between malignant and non-malignant cells. The most frequent subtype in breast cancer is the luminal. By far the most frequently used model for luminal breast cancer is the iconic estrogen receptor-positive (ERpos) MCF7 cell line. However, luminal specific comparisons have suffered from the lack of a relevant non-malignant counterpart. Our previous work has shown that transforming growth factor-β receptor (TGFβR) inhibition suffices to propagate prospectively isolated ERpos human breast luminal cells from reduction mammoplasties (HBEC). Here we demonstrate that transduction of these cells with hTERT/shp16 renders them immortal while remaining true to the luminal lineage including expression of functional ER (iHBECERpos). Under identical culture conditions a major difference between MCF7 and normal-derived cells is the dependence of the latter on TGFβR inhibition for ER expression. In a breast fibroblast co-culture model we further show that whereas MCF7 proliferate concurrently with ER expression, iHBECERpos form correctly polarized acini, and segregate into proliferating and ER expressing cells. We propose that iHBECERpos may serve to shed light on hitherto unappreciated differences in ER regulation and function between normal breast and breast cancer.
The role of the dopamine D1 receptor in social cognition: studies using a novel genetic rat model

ABSTRACT Social cognition is an endophenotype that is impaired in schizophrenia and several other (comorbid) psychiatric disorders. One of the modulators of social cognition is dopamine, but its role is not clear. The effects of dopamine are mediated through dopamine receptors, including the dopamine D1 receptor (Drd1). Because current Drd1 receptor agonists are not Drd1 selective, pharmacological tools are not sufficient to delineate the role of the Drd1. Here, we describe a novel rat model with a genetic mutation in Drd1 in which we measured basic behavioural phenotypes and social cognition. The I116S mutation was predicted to render the receptor less stable. In line with this computational prediction, this Drd1 mutation led to a decreased transmembrane insertion of Drd1, whereas Drd1 expression, as measured by Drd1 mRNA levels, remained unaffected. Owing to decreased transmembrane Drd1 insertion, the mutant rats displayed normal basic motoric and neurological parameters, as well as locomotor activity and anxiety-like behaviour. However, measures of social cognition like social interaction, scent marking, pup ultrasonic vocalizations and sociability, were strongly reduced in the mutant rats. This profile of the Drd1 mutant rat offers the field of neuroscience a novel genetic rat model to study a series of psychiatric disorders including schizophrenia, autism, depression, bipolar disorder and drug addiction. Summary: Rats with a novel dopamine D1 receptor mutation, which causes reduced membrane Drd1 expression, display impaired social cognition, although their exploratory and anxiety-like behaviours are unaffected.
Association Between Modifiable Risk Factors and Pharmaceutical Expenditures Among Adults With Atherosclerotic Cardiovascular Disease in the United States: 2012–2013 Medical Expenditures Panel Survey

Background Atherosclerotic cardiovascular disease (ASCVD) causes most deaths in the United States and accounts for the highest healthcare spending. The association between the modifiable risk factors (MRFs) of ASCVD and pharmaceutical expenditures are largely unknown. Methods and Results We examined the association between MRFs and pharmaceutical expenditures among adults with ASCVD using the 2012 and 2013 Medical Expenditure Panel Survey. A 2‐part model was used while accounting for the survey's complex design to obtain nationally representative results. All costs were adjusted to 2013 US dollars using the gross domestic product deflator. The annual total pharmaceutical expenditure among those with ASCVD was $71.6 billion, 33% of which was for medications for cardiovascular disease and 14% medications for diabetes mellitus. The adjusted relationship between MRFs and pharmaceutical expenditures showed significant marginal increase in average annual pharmaceutical expenditure associated with inadequate physical activity ($519 [95% confidence interval (CI), $12–918; P=0.011]), dyslipidemia ($631 [95% CI, $168–1094; P=0.008]), hypertension: ($1078 [95% CI, $697–1460; P<0.001)], and diabetes mellitus ($2006 [95% CI, $1470–2542]). Compared with those with optimal MRFs (0–1), those with average MRFs (2–3) spent an average of $1184 (95% CI, $805–1564; P<0.001) more on medications, and those with poor MRFs (≥4) spent $2823 (95% CI, $2338–3307; P<0.001) more. Conclusions Worsening MRFs were proportionally associated with higher annual pharmaceutical expenditures among patients with established ASCVD regardless of non‐ASCVD comorbidity. In‐depth studies of the roles played by other factors in this association can help reduce medication‐related expenditures among ASCVD patients.
The Tissue-Specific Rep8/UBXD6 Tethers p97 to the Endoplasmic Reticulum Membrane for Degradation of Misfolded Proteins

The protein known as p97 or VCP in mammals and Cdc48 in yeast is a versatile ATPase complex involved in several biological functions including membrane fusion, protein folding, and activation of membrane-bound transcription factors. In addition, p97 plays a central role in degradation of misfolded secretory proteins via the ER-associated degradation pathway. This functional diversity of p97 depends on its association with various cofactors, and to further our understanding of p97 function it is important that these cofactors are identified and analyzed. Here, we isolate and characterize the human protein named Rep8 or Ubxd6 as a new cofactor of p97. Mouse Rep8 is highly tissue-specific and abundant in gonads. In testes, Rep8 is expressed in post-meiotic round spermatids, whereas in ovaries Rep8 is expressed in granulosa cells. Rep8 associates directly with p97 via its UBX domain. We show that Rep8 is a transmembrane protein that localizes to the ER membrane with its UBX domain facing the cytoplasm. Knock-down of Rep8 expression in human cells leads to a decreased association of p97 with the ER membrane and concomitantly a retarded degradation of misfolded ER-derived proteasome substrates. Thus, Rep8 tethers p97 to the ER membrane for efficient ER-associated degradation.
Why did an effective Dutch complex psycho-social intervention for people with dementia not work in the German healthcare context? Lessons learnt from a process evaluation alongside a multicentre RCT

Background The positive effects of the Dutch Community Occupational Therapy in Dementia programme on patients' daily functioning were not found in a multicentre randomised controlled trial (RCT) in Germany. Objectives To evaluate possible effect modification on the primary outcome within the German RCT with regard to (1) participant characteristics, (2) treatment performance and (3) healthcare service utilisation; and (4) to compare the design and primary outcome between the German and the original Dutch study. Methods (1) The impact of participant baseline data on the primary outcome was analysed in exploratory ANCOVA and regression analyses. (2) Therapists completed questionnaires on context and performance problems. The main problems were identified by a qualitative content analysis and focus-group discussion. Associations of the primary outcome with scores of participant adherence and treatment performance were evaluated by regression analysis. (3) Utilisation rates of healthcare services were controlled for significant group differences. (4) Differences in the Dutch and German study design were identified, and the primary outcome was contrasted at the item level. Results (1) Participant characteristics could not explain more than 5% of outcome variance. (2) The treatment performance of some active intervention components was poor but not significantly associated with the primary outcome. (3) There were no significant group differences in the utilisation of healthcare resources. (4) In contrast to the Dutch waiting-control group, the active intervention in the German control group may have reduced group differences in the current RCT. The German patients demonstrated a higher independence at baseline and less improvement in instrumental activities of daily living. Conclusion The differences in outcome may be explained by a more active control treatment, partially poor experimental treatment and less room for improvement in the German sample. Future cross-national transfers should be prepared by pilot studies assessing the applicability of the intervention and patient needs specific to the target country. Trial registration International Clinical Trials Registry Platform, DRKS00000053.
Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.
Slow diffusive dynamics in a chaotic balanced neural network

It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic) neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.
Crowding, visual awareness, and their respective neural loci

In peripheral vision, object identification can be impeded when a target object is flanked by other objects. This phenomenon of crowding has been attributed to basic processes associated with image encoding by the visual system, but the neural origin of crowding is not known. Determining whether crowding depends on subjective awareness of the flankers can provide information on the neural origin of crowding. However, recent studies that manipulated flanker awareness have yielded conflicting results. In the current study, we suppressed flanker awareness with two methods: interocular suppression (IOS) and adaptation-induced blindness (AIB). We tested two different types of stimuli: gratings and letters. With IOS, we found that the magnitude of crowding increased as the number of physical flankers increased, even when the observers did not report seeing any of the flankers. In contrast, when flanker awareness was manipulated with AIB, the magnitude of crowding increased with the number of perceived flankers. Our results show that whether crowding is contingent on awareness of the flankers depends on the method used to suppress awareness. In addition, our results imply that the locus of crowding is upstream from the neural locus of IOS and close to or downstream from that of AIB. Neurophysiology and neuroimaging studies jointly implicate mid-to-high level visual processing stages for IOS, while direct evidence regarding the neural locus of AIB is limited. The most consistent interpretation of our empirical findings is to place the neural locus of crowding at an early cortical site, such as V1 or V2.
Transfer RNA is highly unstable during early amino acid starvation in Escherichia coli

Due to its long half-life compared to messenger RNA, bacterial transfer RNA is known as stable RNA. Here, we show that tRNAs become highly unstable as part of Escherichia coli's response to amino acid starvation. Degradation of the majority of cellular tRNA occurs within twenty minutes of the onset of starvation for each of several amino acids. Both the non-cognate and cognate tRNA for the amino acid that the cell is starving for are degraded, and both charged and uncharged tRNA species are affected. The alarmone ppGpp orchestrates the stringent response to amino acid starvation. However, tRNA degradation occurs in a ppGpp-independent manner, as it occurs with similar kinetics in a relaxed mutant. Further, we also observe rapid tRNA degradation in response to rifampicin treatment, which does not induce the stringent response. We propose a unifying model for these observations, in which the surplus tRNA is degraded whenever the demand for protein synthesis is reduced. Thus, the tRNA pool is a highly regulated, dynamic entity. We propose that degradation of surplus tRNA could function to reduce mistranslation in the stressed cell, because it would reduce competition between cognate and near-cognate charged tRNAs at the ribosomal A-site.
The power of data mining in diagnosis of childhood pneumonia

Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (C-reactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians.
Bifidobacterium bifidum Actively Changes the Gene Expression Profile Induced by Lactobacillus acidophilus in Murine Dendritic Cells

Dendritic cells (DC) play a pivotal regulatory role in activation of both the innate as well as the adaptive immune system by responding to environmental microorganisms. We have previously shown that Lactobacillus acidophilus induces a strong production of the pro-inflammatory and Th1 polarizing cytokine IL-12 in DC, whereas bifidobacteria do not induce IL-12 but inhibit the IL-12 production induced by lactobacilli. In the present study, genome-wide microarrays were used to investigate the gene expression pattern of murine DC stimulated with Lactobacillus acidophilus NCFM and Bifidobacterium bifidum Z9. L. acidophilus NCFM strongly induced expression of interferon (IFN)-β, other virus defence genes, and cytokine and chemokine genes related to the innate and the adaptive immune response. By contrast, B. bifidum Z9 up-regulated genes encoding cytokines and chemokines related to the innate immune response. Moreover, B. bifidum Z9 inhibited the expression of the Th1-promoting genes induced by L. acidophilus NCFM and had an additive effect on genes of the innate immune response and Th2 skewing genes. The gene encoding Jun dimerization protein 2 (JDP2), a transcription factor regulating the activation of JNK, was one of the few genes only induced by B. bifidum Z9. Neutralization of IFN-β abrogated L. acidophilus NCFM-induced expression of Th1-skewing genes, and blocking of the JNK pathway completely inhibited the expression of IFN-β. Our results indicate that B. bifidum Z9 actively inhibits the expression of genes related to the adaptive immune system in murine dendritic cells and that JPD2 via blocking of IFN-β plays a central role in this regulatory mechanism.
Send Orders of Reprints at Reprints@benthamscience.net Interactive Software " Isotonic Design Using Normalized Equivalent Toxicity Score (id-nets ©tm ) " for Cancer Phase I Clinical Trials

Isotonic Design using Normalized Equivalent Toxicity Score (ID-NETS) is a novel Phase I design that integrates the novel toxicity scoring system originally proposed by Chen et al. [1] and the original Isotonic Design proposed by Leung et al. [2]. ID-NETS has substantially improved the accuracy of maximum tolerated dose (MTD) estimation and trial efficiency in the Phase I clinical trial setting by fully utilizing all toxicities experienced by each patient and treating toxicity response as a quasi-continuous variable instead of a binary indicator of dose limiting toxicity (DLT). To facilitate the incorporation of the ID-NETS method into the design and conduct of Phase I clinical trials, we have designed and developed a user-friendly software, ID-NETS ©TM , which has two functions: 1) Calculating the recommended dose for the subsequent patient cohort using available completed data; and 2) Performing simulations to obtain the operating characteristics of a trial designed with ID-NETS. Currently, ID-NETS
An Exploratory Investigation of E-Rest: Teletherapy for Chronically Aphasic Speakers

Delivering aphasia therapy via telecommunication may provide a means to deliver intensive therapy in a cost-effective way. Teletherapy, remotely-administered (language) treatment, may support the repetitive drill practices that people with chronic aphasia need to perform when learning to compensate for their lasting language difficulties. The use of teletherapy may allow speech and language pathologists (SLPs) to focus in-person sessions more strongly on the generalisation of therapy effects to daily life. This single subject study is an investigation whether a teletherapy application called e-REST meets the criteria of accessibility, user-friendliness, as well as effectiveness. e-REST, the teletherapy version of the Dutch and adapted Reduced Syntax Therapy, teaches chronically aphasic speakers of Dutch who experience difficulties in sentence production to convey their messages in a kind of telegraphic style. The results obtained suggest that it is reasonable to conduct a larger study into the user-friendliness, accessibility, effectiveness, and cost-effectiveness of e-REST.