Miracle-dz/UniFAR-FTUs huggingface dataset:
Gut Dysbiosis and Kidney Diseases

Gut dysbiosis is defined as disorders of gut microbiota and loss of barrier integrity, which are ubiquitous on pathological conditions and associated with the development of various diseases. Kidney diseases are accompanied with gut dysbiosis and metabolic disorders, which in turn contribute to the pathogenesis and progression of kidney diseases. Microbial alterations trigger production of harmful metabolites such as uremic toxins and a decrease in the number of beneficial ones such as SCFAs, which is the major mechanism of gut dysbiosis on kidney diseases according to current studies. In addition, the activation of immune responses and mitochondrial dysfunction by gut dysbiosis, also lead to the development of kidney diseases. Based on the molecular mechanisms, modification of gut dysbiosis via probiotics, prebiotics and synbiotics is a potential approach to slow kidney disease progression. Fecal microbiota transplantation (FMT) and genetic manipulation of the gut microbiota are also promising choices. However, the clinical use of probiotics in kidney disease is not supported by the current clinical evidence. Further studies are necessary to explore the causal relationships of gut dysbiosis and kidney diseases, the efficiency and safety of therapeutic strategies targeting gut-kidney axis.
Beyond Immunosuppression: The Multifaceted Functions of Tumor-Promoting Myeloid Cells in Breast Cancers

Breast cancers are commonly associated with an immunosuppressive microenvironment responsible for tumor escape from anti-cancer immunity. Cells of the myeloid lineage account for a major part of this tumor-promoting landscape. These myeloid cells are composed of heterogeneous subsets at different stages of differentiation and have traditionally been described by their cardinal ability to suppress innate and adaptive anticancer immunity. However, evidence has accumulated that, beyond their immunosuppressive properties, breast cancer-induced myeloid cells are also equipped with a broad array of “non-immunological” tumor-promoting functions. They therefore represent major impediments for anticancer therapies, particularly for immune-based interventions. We herein analyze and discuss current literature related to the versatile properties of the different myeloid cell subsets engaged in breast cancer development. We critically assess persisting difficulties and challenges in unequivocally discriminate dedicated subsets, which has so far prevented both the selective targeting of these immunosuppressive cells and their use as potential biomarkers. In this context, we propose the concept of IMCGL, “pro-tumoral immunosuppressive myeloid cells of the granulocytic lineage”, to more accurately reflect the contentious nature and origin of granulocytic cells in the breast tumor microenvironment. Future research prospects related to the role of this myeloid landscape in breast cancer are further considered.
Status of Clinical Neuropsychology Training in Finland

This paper provides information on different training models within clinical neuropsychology in Finland. Systematic specialization training program began in Finland in 1983. It was first organized mainly by the Finnish Neuropsychological Society and since 1997 by the Finnish universities. At present, close to 400 clinical neuropsychologists have completed the training. The number of professionals still does not cover the needs of the country (population 5.5 million, area 338,440 km2), and geographical disparities are a constant concern. The training models in Finland have changed over the years and will continue to evolve. Specialization training can be organized by national societies or by universities. It can lead to an academic degree or a diploma. It can be linked to doctoral studies or form a parallel track. Financial model can involve student fees or be governed by ministries (such as the Ministry of Education or Ministry of Health). This paper describes and compares different strategies in education that have impact on the output of professionals. One model does not fit all, or even one country at all times. The strategies of the stakeholder ministries can change over time. The experiences from Finland can be useful for other countries that are developing their models. The estimated need of practitioners and the educational resources including the available financial models for training differ between countries. The guiding principles in specialist training should focus on the advanced competencies expected from the neuropsychologist when entering the profession.
COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images

Diagnosis is a crucial precautionary step in research studies of the coronavirus disease, which shows indications similar to those of various pneumonia types. The COVID-19 pandemic has caused a significant outbreak in more than 150 nations and has significantly affected the wellness and lives of many individuals globally. Particularly, discovering the patients infected with COVID-19 early and providing them with treatment is an important way of fighting the pandemic. Radiography and radiology could be the fastest techniques for recognizing infected individuals. Artificial intelligence strategies have the potential to overcome this difficulty. Particularly, transfer learning MobileNetV2 is a convolutional neural network architecture that can perform well on mobile devices. In this study, we used MobileNetV2 with transfer learning and augmentation data techniques as a classifier to recognize the coronavirus disease. Two datasets were used: the first consisted of 309 chest X-ray images (102 with COVID-19 and 207 were normal), and the second consisted of 516 chest X-ray images (102 with COVID-19 and 414 were normal). We assessed the model based on its sensitivity rate, specificity rate, confusion matrix, and F1-measure. Additionally, we present a receiver operating characteristic curve. The numerical simulation reveals that the model accuracy is 95.8% and 100% at dropouts of 0.3 and 0.4, respectively. The model was implemented using Keras and Python programming.
Characterization of Macrophage-Like Cells in Retinal Vein Occlusion Using En Face Optical Coherence Tomography

Purpose To investigate the clinical features of a macrophage-like cell (MLC) obtained by en face optical coherence tomography (OCT) in retinal vein occlusion (RVO). Methods The study involved 36 patients with treatment-naïve unilateral acute RVO, including 21 branch RVO (BRVO) and 15 central RVO. Vessel density and macular thickness were quantified using OCT angiography. A 3-μm en face OCT slab on the inner limiting membrane in the optic nerve head (ONH) region or macular region was used to visualize the MLCs. The MLCs were binarized and quantified using a semiautomated method. The unaffected fellow eyes served as the control group. Results The morphology of MLCs appeared larger and plumper in RVO eyes. The mean MLC density in the ONH and macular regions was 2.46 times and 2.86 times higher than their fellow eyes, respectively (p < 0.001). The macular MLC density of the occlusive region was significantly lower than that of the unaffected region in BRVO (p = 0.01). The ONH and macular MLC densities in the non-perfused region were significantly lower than those in the perfused region in all RVO eyes (p < 0.001). The ONH MLC density in RVO eyes was negatively correlated with radial peripapillary capillary vessel density (r = −0.413, p = 0.012). Both ONH and macular MLC densities were positively correlated with macular thickness (r = 0.505, p = 0.002; r = 0.385, p = 0.02, respectively). Conclusion The increased density and changes of morphology characterized by OCT may indicate generalized activation and aggregation of MLCs in RVO. More MLCs are recruited in the perfused region rather than the non-perfused region. RVO eyes with a higher density of MLCs tend to suffer from the thicker macula.
Spatio-Temporal Variation of Synechococcus Assemblages at DNA and cDNA Levels in the Tropical Estuarine and Coastal Waters

Synechococcus is a major contributor to global marine primary production. Here, its spatio-temporal variations in abundance and phylogenetic structure were studied at three stations of the South China Sea at both DNA and cDNA levels. Synechococcus cell abundance was lowest in March, but highest in October at two coastal stations. Its abundance was higher at the estuarine station, which reached a peak value of 1.36 × 105 cells/ml in April, owing to the nitrogen nutrients discharged from the Sanya River. Gene and gene transcript abundances of four Synechococcus lineages, clades II, III, VIII, and S5.3, were studied by quantitative PCR, which showed that clade II was the most abundant lineage at both DNA and cDNA levels. High-throughput sequencing revealed that, at the DNA level, Synechococcus assemblage was dominated by clade SY4 (a novel clade defined in this study), S5.2, and clade II in the coastal waters and was dominated by freshwater/S5.2 Synechococcus, reaching a value up to 88.61% in June, in estuarine waters. Changes in salinity and nutrient concentration caused by seasonal monsoonal forcing and river discharge were the key determinants of the spatio-temporal variation in Synechococcus assemblages at the DNA level. In comparison, high dissimilation among samples at the same stations and in the same seasons leads to the imperceptible spatio-temporal variation pattern of Synechococcus assemblages at the cDNA level. Furthermore, co-occurrence networks disclosed that Synechococcus community had closer and more complex internal interactions at the cDNA level. These discrepancies highlighted the necessity to study Synechococcus assemblages at both DNA and cDNA levels.
Exodex Adam—A Reconfigurable Dexterous Haptic User Interface for the Whole Hand

Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly “feel” the environment they work in, whether virtual or a remote site through an avatar. We introduce the DLR Exodex Adam, a reconfigurable, dexterous, whole-hand haptic input device. The device comprises multiple modular, three degrees of freedom (3-DOF) robotic fingers, whose placement on the device can be adjusted to optimize manipulability for different user hand sizes. Additionally, the device is mounted on a 7-DOF robot arm to increase the user’s workspace. Exodex Adam uses a front-facing interface, with robotic fingers coupled to two of the user’s fingertips, the thumb, and two points on the palm. Including the palm, as opposed to only the fingertips as is common in existing devices, enables accurate tracking of the whole hand without additional sensors such as a data glove or motion capture. By providing “whole-hand” interaction with omnidirectional force-feedback at the attachment points, we enable the user to experience the environment with the complete hand instead of only the fingertips, thus realizing deeper immersion. Interaction using Exodex Adam can range from palpation of objects and surfaces to manipulation using both power and precision grasps, all while receiving haptic feedback. This article details the concept and design of the Exodex Adam, as well as use cases where it is deployed with different command modalities. These include mixed-media interaction in a virtual environment, gesture-based telemanipulation, and robotic hand–arm teleoperation using adaptive model-mediated teleoperation. Finally, we share the insights gained during our development process and use case deployments.
IRF4b and IRF8 Negatively Regulate RLR-Mediated NF-κB Signaling by Targeting MITA for Degradation in Teleost Fish

Mediator of IRF3 activation (MITA) is a significant signal adaptor in the retinoic acid-inducible gene-I like receptor (RLR) signaling pathway and plays an important role in the innate immune system. As a transcription factor, nuclear factor kappa B (NF-κB) can be available in many signaling pathways including the RLR signaling pathway and relative to biological processes like immune responses. In this study, it is determined that IRF4b and IRF8 can have a negative effect on NF-κB signaling pathway mediated by MITA in fish. Firstly, it is found that IRF4b and IRF8 have an inhibitory function on MITA-mediated NF-κB signaling pathway. It is interesting that IRF4b and IRF8 have similar functions to achieve precise downregulated and the degradation of MITA through the ubiquitin-proteasome pathway. IRF is taken as the core domain of IRF4b or IRF8 for the downregulation to MITA. This study provides data on MITA-mediated NF-κB signaling pathway in teleost fish and provides new insights into the regulatory mechanism in fish immune system.
The Prognostic Value of Integrated Analysis of Inflammation and Hypoxia-Related Genes in Idiopathic Pulmonary Fibrosis

Currently, the aetiology and pathogenesis of idiopathic pulmonary fibrosis (IPF) are still largely unclear. Moreover, patients with IPF exhibit a considerable difference in clinical presentation, treatment, and prognosis. Optimal biomarkers or models for IPF prognosis are lacking. Therefore, this study quantified the levels of various hallmarks using a single-sample gene set enrichment analysis algorithm. The hazard ration was calculated using Univariate Cox regression analysis based on the transcriptomic profile of bronchoalveolar lavage cells and clinical survival information. Afterwards, weighted Gene Co-expression Network Analysis was performed to construct a network between gene expression, inflammation response, and hypoxia. Subsequently, univariate Cox, random forest, and multivariate Cox regressions were applied to develop a robust inflammation and hypoxia-related gene signature for predicting clinical outcomes in patients with IPF. Furthermore, a nomogram was constructed to calculate risk assessment. The inflammation response and hypoxia were identified as latent risk factors for patients with IPF. Five genes, including HS3ST1, WFDC2, SPP1, TFPI, and CDC42EP2, were identified that formed the inflammation-hypoxia-related gene signature. Kaplan-Meier plotter showed that the patients with high-risk scores had a worse prognosis than those with low-risk scores in training and validation cohorts. The time-dependent concordance index and the receiver operating characteristic analysis revealed that the risk model could accurately predict the clinical outcome of patients with IPF. Therefore, this study contributes to elucidating the role of inflammation and hypoxia in IPF, which can aid in assessing individual prognosis and personalised treatment decisions.
Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection

Purpose Consistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models. Materials and Methods A total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named “full-database pre-trained strategy”. Results The model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%). Conclusions A consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.
The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma

Purpose This study aims to evaluate the value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) for evaluating extramural vascular invasion (EMVI) of rectal adenocarcinoma. Methods Ninety-six patients with pathologically confirmed rectal adenocarcinoma after surgical resections were collected. Patients were divided into the EMVI positive group (n=39) and the EMVI negative group (n=57). We measured the IVIM-DWI parameters and TA parameters of rectal adenocarcinoma. We compare the differences of the above parameters between the two groups and establish a prediction model through multivariate logistic regression analysis. the ROC curve was performed for parameters with each individual and in combination. Results ADC, D, D* value between the two groups were statistically significant (P= 0.015,0.031,0). Six groups of texture parameters were statistically significant between the two groups (P=0.007,0.037,0.011,0.005,0.007,0.002). Logistic regression prediction model shows that GLCM entropy_ALL DIRECTION_offset7_SD and D* are important independent predictors, and the AUC of the regression prediction model was 0.821, the sensitivity was 92.98%, the specificity was 61.54%, and the Yoden index was 0.5452. The AUC was significantly higher than that of other single parameters. Conclusion 3.0T MRI IVIM-DWI parameters combined with texture analysis can provide valuable information for EMVI evaluation of rectal adenocarcinoma before the operation.
Electrodermal Activity Biofeedback Alters Evolving Functional Brain Networks in People With Epilepsy, but in a Non-specific Manner

There is evidence that biofeedback of electrodermal activity (EDA) can reduce seizure frequency in people with epilepsy. Prior studies have linked EDA biofeedback to a diffuse brain activation as a potential functional mechanism. Here, we investigated whether short-term EDA biofeedback alters EEG-derived large-scale functional brain networks in people with epilepsy. In this prospective controlled trial, thirty participants were quasi-randomly assigned to one of three biofeedback conditions (arousal, sham, or relaxation) and performed a single, 30-min biofeedback training while undergoing continuous EEG recordings. Based on the EEG, we derived evolving functional brain networks and examined their topological, robustness, and stability properties over time. Potential effects on attentional-executive functions and mood were monitored via a neuropsychological assessment and subjective self-ratings. Participants assigned to the relaxation group seemed to be most successful in meeting the task requirements for this specific control condition (i.e., decreasing EDA). Participants in the sham group were more successful in increasing EDA than participants in the arousal group. However, only the arousal biofeedback training was associated with a prolonged robustness-enhancing effect on networks. Effects on other network properties were mostly unspecific for the different groups. None of the biofeedback conditions affected attentional-executive functions or subjective behavioral measures. Our results suggest that global characteristics of evolving functional brain networks are modified by EDA biofeedback. Some alterations persisted after the single training session; however, the effects were largely unspecific across the different biofeedback protocols. Further research should address changes of local network characteristics and whether multiple training sessions will result in more specific network modifications.
Genetic Parameters of White Striping and Meat Quality Traits Indicative of Pale, Soft, Exudative Meat in Turkeys (Meleagris gallopavo)

Due to the increasing prevalence of growth-related myopathies and abnormalities in turkey meat, the ability to include meat quality traits in poultry breeding strategies is an issue of key importance. In the present study, genetic parameters for meat quality traits and their correlations with body weight and meat yield were estimated using a population of purebred male turkeys. Information on live body, breast, thigh, and drum weights, breast meat yield, feed conversion ratio, breast lightness (L*), redness (a*), and yellowness (b*), ultimate pH, and white striping (WS) severity score were collected on 11,986 toms from three purebred genetic lines. Heritability and genetic and partial phenotypic correlations were estimated for each trait using an animal model with genetic line, hatch week-year, and age at slaughter included as fixed effects. Heritability of ultimate pH was estimated to be 0.34 ± 0.05 and a range of 0.20 ± 0.02 to 0.23 ± 0.02 for breast meat colour (L*, a*, and b*). White striping was also estimated to be moderately heritable at 0.15 ± 0.02. Unfavorable genetic correlations were observed between body weight and meat quality traits as well as white striping, indicating that selection for increased body weight and meat yield may decrease pH and increase the incidence of pale meat with more severe white striping. The results of this analysis provide insight into the effect of current selection strategies on meat quality and emphasize the need to include meat quality traits into future selection indexes for turkeys.
The Olfactory Tract: Basis for Future Evolution in Response to Rapidly Changing Ecological Niches

Within the forebrain the olfactory sensory system is unique from other sensory systems both in the projections of the olfactory tract and the ongoing neurogenic potential, characteristics conserved across vertebrates. Olfaction plays a crucial role in behaviors such as mate choice, food selection, homing, escape from predators, among others. The olfactory forebrain is intimately associated with the limbic system, the region of the brain involved in learning, memory, and emotions through interactions with the endocrine system and the autonomic nervous system. Previously thought to lack a limbic system, we now know that teleost fishes process emotions, have exceptional memories, and readily learn, behaviors that are often associated with olfactory cues. The association of neuromodulatory hormones, and more recently, the immune system, with odor cues underlies behaviors essential for maintenance and adaptation within natural ecological niches. Increasingly anthropogenic perturbations affecting ecosystems are impacting teleost fishes worldwide. Here we examine the role of the olfactory tract as the neural basis for the integration of environmental cues and resulting behaviors necessary for the regulation of biotic interactions that allow for future adaptation as the climate spins out of control.