Interferon-Gamma Release Assay Combined with Renal Indicators to Reduce the False-Negativity of Latent Tuberculosis Infection in End-Stage Renal Disease with Hemodialysis Patients.

BACKGROUND: Tuberculosis (TB) is still the main cause of mortality due to a single transfectant, Mycobacterium tuberculosis (MTB). Latent tuberculosis infection (LTBI) is a condition characterized by the presence of tuberculosis (TB) that is not clinically apparent but nonetheless shows a sustained response to MTB. Presently, tuberculin skin test (TST) and interferon gamma (IFN-γ) release assays (IGRAs) are mainly used to detect LTBI via cell-mediated immunity of T-cells. For people with end-stage renal disease (ESRD), the diagnosis of patients infected with MTB is difficult because of T-cell dysfunction. To get more accurate diagnosis results of LTBI, it must compensate for the deficiency of IGRA tests. METHODS: Sixty-seven hemodialysis (HD) patients and 96 non-HD patients were enrolled in this study and the study population is continuously included. IFN-γ levels were measured by the QuantiFERON-TB Gold In-Tube (QFT-GIT) test. Kidney function indicators, blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR) were used to compensate for the declined IFN-γ levels in the IGRA test. RESULTS: In individuals who were previously undetected, the results of compensation with serum Cr increased by 10.81%, allowing for about 28% more detection, and compensation with eGFR increased by 5.41%, allowing for approximately 14% more detectable potential among them and employing both of them could enhance the prior shortcomings of IGRA tests. when both are used, the maximum compensation results show a sensitivity increase rate of 8.81%, and approximately 23% of patients who were previously undetectable may be found. CONCLUSION: Therefore, the renal function markers which are routine tests for HD patients to compensate for the deficiency of IGRA tests could increase the accuracy of LTBI diagnosis.

Effect of job satisfaction on depression after adjusting for satisfaction with other life domains.

Background: Studies on the association between job satisfaction and depression have often been reported. However, no study has examined how job satisfaction impacts depression while considering satisfaction with other aspects of life. In this study, we evaluated the effect of job satisfaction on depression after adjusting for satisfaction with other domains of life. Methods: We used data from the 16th wave of the Korean Welfare Panel Study. A total of 3568 current employees without depression who completed a survey were included. Depression was measured using the abbreviated version of the CES-D scale. Various types of satisfaction, including job satisfaction, were measured using single-item questions and a 5-point Likert scale. The association between job satisfaction and depression after considering satisfaction with other life domains was analyzed using a multiple logistic regression model. Results: Crude models showed a significant association between job satisfaction and depression in males (odds ratio [OR]: 7.90; 95% confidence interval [CI]: 4.11-15.23) and females (OR: 7.12; 95% CI: 4.40-11.50). When adjusted for sociodemographic factors, health-related factors, and work-related factors, the association remained significant in males (OR: 6.20; 95% CI: 3.10-12.41) and females (OR: 6.28; 95% confidence interval (CI): 3.79-10.40). However, when satisfaction with other life domains was included, the association remained significant only in females (OR: 3.55; 95% CI: 1.93-6.50). Conclusions: This study shows an association between job satisfaction and depression in Korean employees. However, when satisfaction with other life domains was considered, this association remained significant only in women. Regular screening of job satisfaction should be considered as a means of preventing and managing depression among female employees.

Advancements in Sustainable Plant-Based Alternatives: Exploring Proteins, Fats, and Manufacturing Challenges in Alternative Meat Production.

The rise in plant-based food consumption is propelled by concerns for sustainability, personal beliefs, and a focus on healthy dietary habits. This trend, particularly in alternative meat, has attracted attention from specialized brands and eco-friendly food companies, leading to increased interest in plant-based alternatives. The dominant plant-based proteins, derived mainly from legumes, include soy protein isolates, which significantly impact sensory factors. In the realm of plant-based fats, substitutes are categorized into fat substitutes based on fats and fat mimetics based on proteins and carbohydrates. The production of these fats, utilizing gums, emulsions, gels, and additives, explores characteristics influencing the appearance, texture, flavor, and storage stability of final plant-based products. Analysis of plant-based proteins and fats in hamburger patties provides insights into manufacturing methods and raw materials used by leading alternative meat companies. However, challenges persist, such as replicating meat's marbling characteristic and addressing safety considerations in terms of potential allergy induction and nutritional supplementation. To enhance functionality and develop customized plant-based foods, it is essential to explore optimal combinations of various raw materials and develop new plant-based proteins and fat separation.

High Procalcitonin, C-Reactive Protein, and α-1 Acid Glycoprotein Levels in Whole Blood Samples Could Help Rapid Discrimination of Active Tuberculosis from Latent Tuberculosis Infection and Healthy Individuals.

Tuberculosis (TB) management is important for prompt discrimination of latent TB infection (LTBI) from active TB and proper treatment. Whole blood Interferon-gamma (IFN-γ) release assay (IGRA) is used to diagnose LTBI based on the secretion of IFN-γ by T-cells in the whole blood by using a specific antigen of Mycobacterium tuberculosis. However, the ability of IGRA to distinguish active TB from LTBI is considerably limited. Distinguishing active TB from LTBI is necessary to identify indicators that can be used to effectively manage TB and develop diagnostic methods. In the present study, we used a Luminex multiplex bead array (a bead-based antibody−antigen sandwich method). The whole blood level of acute phase proteins (APPs), such as endoglin (ENG), procalcitonin (PCT), C-reactive protein (CRP), and α1-acid glycoprotein (AGP), in active TB, LTBI, and healthy individuals were analyzed and quantified. The APP test results for the serum and whole blood samples showed that the levels of PCT, CRP, and AGP were significantly increased (p < 0.0500; area under curve = 0.955) in active TB. The level of these markers in the whole blood of active TB, LTBI, and healthy individuals could provide data for effective diagnosis and treatment of TB.

Fast inference of spinal neuromodulation for motor control using amortized neural networks.

Objective.Epidural electrical stimulation (EES) has emerged as an approach to restore motor function following spinal cord injury (SCI). However, identifying optimal EES parameters presents a significant challenge due to the complex and stochastic nature of muscle control and the combinatorial explosion of possible parameter configurations. Here, we describe a machine-learning approach that leverages modern deep neural networks to learn bidirectional mappings between the space of permissible EES parameters and target motor outputs.Approach.We collected data from four sheep implanted with two 24-contact EES electrode arrays on the lumbosacral spinal cord. Muscle activity was recorded from four bilateral hindlimb electromyography (EMG) sensors. We introduce a general learning framework to identify EES parameters capable of generating desired patterns of EMG activity. Specifically, we first amortize spinal sensorimotor computations in a forward neural network model that learns to predict motor outputs based on EES parameters. Then, we employ a second neural network as an inverse model, which reuses the amortized knowledge learned by the forward model to guide the selection of EES parameters.Main results.We found that neural networks can functionally approximate spinal sensorimotor computations by accurately predicting EMG outputs based on EES parameters. The generalization capability of the forward model critically benefited our inverse model. We successfully identified novel EES parameters, in under 20 min, capable of producing desired target EMG recruitment duringin vivotesting. Furthermore, we discovered potential functional redundancies within the spinal sensorimotor networks by identifying unique EES parameters that result in similar motor outcomes. Together, these results suggest that our framework is well-suited to probe spinal circuitry and control muscle recruitment in a completely data-driven manner.Significance.We successfully identify novel EES parameters within minutes, capable of producing desired EMG recruitment. Our approach is data-driven, subject-agnostic, automated, and orders of magnitude faster than manual approaches.

Emergence of Content-Agnostic Information Processing by a Robot Using Active Inference, Visual Attention, Working Memory, and Planning.

Generalization by learning is an essential cognitive competency for humans. For example, we can manipulate even unfamiliar objects and can generate mental images before enacting a preplan. How is this possible? Our study investigated this problem by revisiting our previous study (Jung, Matsumoto, & Tani, 2019), which examined the problem of vision-based, goal-directed planning by robots performing a task of block stacking. By extending the previous study, our work introduces a large network comprising dynamically interacting submodules, including visual working memory (VWMs), a visual attention module, and an executive network. The executive network predicts motor signals, visual images, and various controls for attention, as well as masking of visual information. The most significant difference from the previous study is that our current model contains an additional VWM. The entire network is trained by using predictive coding and an optimal visuomotor plan to achieve a given goal state is inferred using active inference. Results indicate that our current model performs significantly better than that used in Jung et al. (2019), especially when manipulating blocks with unlearned colors and textures. Simulation results revealed that the observed generalization was achieved because content-agnostic information processing developed through synergistic interaction between the second VWM and other modules during the course of learning, in which memorizing image contents and transforming them are dissociated. This letter verifies this claim by conducting both qualitative and quantitative analysis of simulation results.

The downregulation of IGFBP3 by TGF-ß signaling in oral cancer contributes to the osteoclast differentiation.

Oral squamous cell carcinoma (OSCC) frequently invades nearby bone and bone involvement determines the prognosis of patients. Growth factors, stored in the bone matrix and released during bone destruction, are known as key components in the bone-tumor interaction. However, the coordination of growth factor signals and the precise mechanism of bone destruction in oral cancer are still unclear. In the study, we investigated the differential cytokine expression profile of oral cancer cells by TGF-ß treatment and the function of altered expression of cytokines on the osteoclast differentiation. We established TGFBR2-knockdown cells using small hairpin RNA. TGF-ß was treated to both TGFBR2 expressing and knockdown cells and the culture supernatants were analyzed using a cytokine array kit. We found that the TGF-ß inhibited IGFBP3 level and enhanced MMP9 level. We confirmed this regulation of IGFBP3 and MMP9 by TGF-ß using ELISA and zymography, respectively. IGFBP3 is known as to modulate the bioavailability of IGF1, which is abundant in the bone microenvironment and regulates osteoclast differentiation. Therefore, we further analyzed the function of IGFBP3 on osteoclastogenesis. Although IGFBP3 increased the viability of murine bone marrow macrophages, the osteoclast differentiation of these cells was blocked by IGFBP3 in a dose-dependent manner. These results revealed a novel pathway for the regulation of osteoclastogenesis by oral cancer cells, which may be a new therapeutic target for osteolysis induced by oral cancer infiltrating into the bone.

Adaptive detrending to accelerate convolutional gated recurrent unit training for contextual video recognition.

Video image recognition has been extensively studied with rapid progress recently. However, most methods focus on short-term rather than long-term (contextual) video recognition. Convolutional recurrent neural networks (ConvRNNs) provide robust spatio-temporal information processing capabilities for contextual video recognition, but require extensive computation that slows down training. Inspired by normalization and detrending methods, in this paper we propose "adaptive detrending" (AD) for temporal normalization in order to accelerate the training of ConvRNNs, especially of convolutional gated recurrent unit (ConvGRU). For each neuron in a recurrent neural network (RNN), AD identifies the trending change within a sequence and subtracts it, removing the internal covariate shift. In experiments testing for contextual video recognition with ConvGRU, results show that (1) ConvGRU clearly outperforms feed-forward neural networks, (2) AD consistently and significantly accelerates training and improves generalization, (3) performance is further improved when AD is coupled with other normalization methods, and most importantly, (4) the more long-term contextual information is required, the more AD outperforms existing methods.

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

Effects of Family Meal Frequency on Risk Factors for Cardiovascular Disease in Korean Elderly Males and Females.

BACKGROUND: In the case of the elderly who highly depend on family, serious health problems can be caused due to the reduction of family meals. Therefore, this study aims to suggest the fundamental data for management of cardiovascular disease, one of the major causes of death in elderly Koreans, by investigating the effects of family meal frequency on the risk factors for cardiovascular disease in Korean elderly males and females. METHODS: The raw data of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES III) were utilized. Data of 1,236 respondents were extracted for analysis regarding anthropometry, blood, blood pressure, nutrients and total energy intake. For collected data, using SPSS 18.0 and Amos 18.0, the mean and standard deviation, and the path coefficient between groups through a multi-group analysis by structural equation model were checked. RESULTS: As family meal frequency increased, triglyceride and fasting blood glucose in Korean elderly males were likely to decrease, which led to conflicting results with those of Korean elderly females. CONCLUSION: Frequent family meal makes a positive effect on reducing several risk factors for cardiovascular disease in Korean elderly.

Sparganosis of the breast and lower extremities: sonographic appearance.

Sparganosis is a rare parasitic infection caused by larvae of the genus Spirometra. It can involve any part of the human body and usually manifests as a mass in various locations. We report a case of recurrent sparganosis in the breast and lower extremities. Our patient had recurrent subcutaneous masses in her breast and lower leg that showed characteristic ultrasonographic imaging findings of serpentine, tubular structures with surrounding increased echogenicity. These imaging findings are well correlated with pathologic findings. Worms were identified in resected specimens confirming sparganosis.

Indication for Co-evolution of Lactobacillus johnsonii with its hosts.

BACKGROUND: The intestinal microbiota, composed of complex bacterial populations, is host-specific and affected by environmental factors as well as host genetics. One important bacterial group is the lactic acid bacteria (LAB), which include many health-promoting strains. Here, we studied the genetic variation within a potentially probiotic LAB species, Lactobacillus johnsonii, isolated from various hosts. RESULTS: A wide survey of 104 fecal samples was carried out for the isolation of L. johnsonii. As part of the isolation procedure, terminal restriction fragment length polymorphism (tRFLP) was performed to identify L. johnsonii within a selected narrow spectrum of fecal LAB. The tRFLP results showed host specificity of two bacterial species, the Enterococcus faecium species cluster and Lactobacillus intestinalis, to different host taxonomic groups while the appearance of L. johnsonii and E. faecalis was not correlated with any taxonomic group. The survey ultimately resulted in the isolation of L. johnsonii from few host species. The genetic variation among the 47 L. johnsonii strains isolated from the various hosts was analyzed based on variation at simple sequence repeats (SSR) loci and multi-locus sequence typing (MLST) of conserved hypothetical genes. The genetic relationships among the strains inferred by each of the methods were similar, revealing three different clusters of L. johnsonii strains, each cluster consisting of strains from a different host, i.e. chickens, humans or mice. CONCLUSIONS: Our typing results support phylogenetic separation of L. johnsonii strains isolated from different animal hosts, suggesting specificity of L. johnsonii strains to their hosts. Taken together with the tRFLP results, that indicated the association of specific LAB species with the host taxonomy, our study supports co-evolution of the host and its intestinal lactic acid bacteria.