A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal’s external drivers and shines a light on the likely external sources contributing to the BOLD signal’s non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain’s time-varying functional dynamics. Over the past few decades, functional MRI has widened our understanding of the functional organization of intrinsic brain networks and their role in cognition and behavior. Classical univariate  analyses of fMRI signal  have been instrumental in probing the specialized function of brain regions. More recent approaches using functional connectivity and network neuroscience portray a complex and multi-scale set of interactions between brain structures. Following this view, a wide array of graph theoretical and complex systems tools have been used to describe BOLD dynamics –3.Despite these efforts, we still lack a unified mechanistic framework that overcomes three key limitations. First, the features of the BOLD signal that are important for neural activity are unclear. Several prior studies demonstrate a relation between BOLD and slow amplitude features of cortical activity –6, and bTo address these three limitations, we develop a generative framework that explicitly includes exogenous input , and provide evidence that the brain’s activity can be fruitfully understood in the context of its natural drivers. Specifically, we use a multivariate autoregressive model with unknown inputs to capture the spatiotemporal evolution of the BOLD signal driven by extra-cortical inputs. These models have been used to characterize and predict the evolution of several synthetic and biological systems –19. For unknown parameters of a linear time-invariant (LTI) system, which we estimate following recent developments in linear systems theory  at sampling point k = 0, …, T. A total of n = 100 regions are considered and the collection of these signals is captured by the vector k = 0, …, T, which we refer to as the state of the system . The evolution of the system’s state is mainly driven by (i) the cross-dependencies of the signals in different regions (not necessarily adjacent), and (ii) the external inputs that are either excitation noise or inputs arriving from the environment surrounding the regions captured by the state of the system .Each region k. Notice that parameters of the system  assuming A is given by the initial approximation, we provide a sparse low-rank structure to matrix B and we find an approximation to both z[0] and z[0], …, z[T] subsequently, B. The process consists of executing step (ii) and (iii) iteratively. Our experiments reveal that the estimated parameters converge after a few iterations in both synthetic and fMRI time series , the optimization objective is rather given by λ > 0.—See section SI1 in Subsequently, a first step towards modeling the evolution of the system’s state is:xk+=Ax[k]+Bure known . Thus, sre known , we perfA. Therefore, in a modified version of this algorithm, in step (i) we estimate A from x′[k] measured during an extended window without external stimulation . Next, we repeat steps (ii) and (iii) iteratively—as detailed above. Since we did not know the true dimensionality of the external inputs, we approximated the dimensions of the input matrix B by performing principal component analysis on the residuals of the models. As seen in S19 Fig in p = 25 for input matrix B to estimate the inputs from task fMRI time series.We will demonstrate in the following results section that unaccounted external inputs result in error in estimation of system matrix eigenmode decomposition. Briefly, consider the n eigenmodes  associated with A. Each eigenmode corresponds to an eigenvalue-eigenvector pair  satisfying Avi = λivi, and it describes the oscillatory behavior for a specific direction vi.Provided an LTI description of the system dynamics , the autλi represented in polar coordinates , we have that it captures the frequency characterized asδt corresponds to the sampling frequency, and the time scale given bydamping rate.Specifically, for any given eigenvalue V =  and λ = diag are the matrices of eigenvectors and eigenvalues. Subsequently, we can apply a change of variable as z[k] = V* x[k], where V* is the transpose conjugate, which implies that ith eigenvector associated with the ith eigenvalue. Hence, this can be understood as the spatial contributions of the n ROIs at a given  frequency fi. Additionally, we can revisit the damping rate of the process in such direction vi by reasoning as follows: first, we can recursively obtain |zi[k]| = |λi|t|zi[0]|. Therefore, we have the following three scenarios: (i) |λi|<1; (ii) |λi|>1; and (iii) |λi| = 1. In case (i) and (ii), we can readily see that |zi[k]| → 0 and |zi[k]| → ∞ as k → ∞, respectively. Lastly, in scenario (iii), or practically, when |λi|≈1, we have that the process oscillates between stability and instability, and therefore these dynamics are refer to as meta-stable.In particular, we can re-write z(k) describes the spatiotemporal brain BOLD signal evolution. Specifically, the timescales are encoded in the eigenvalues and the spatial contributions of the different ROIs are described by the eigenvectors with a spatiotemporal timescale described by the associated eigenvalues.In summary, the dynamical process We used data from the Human Connectome Project (HCP). As part of the HCP protocol, subjects underwent two separate resting-state scans along with seven task fMRI scans, both of which included two sessions. All data analyzed here came from these scans and was part of the HCP S1200 release. The fMRI protocol (both resting-state and task) includes a multi-band factor of 8, spatial resolution of 2 mm isotropic voxels, and a TR of 0.72 sec (for more details see ). SubjecN = 100 parcels that maximizes the similarity of functional connectivity within each parcel [Head-motion artifacts result in significant error in the functional connectivity estimates . Therefoh parcel . Next toh parcel , 40 resth parcel , which uh parcel , with fih parcel .t-test and Welch’s t-test [t−test, p < 0.05, FDR). In addition we also generated phase-randomized null time series from each subjects’ BOLD times series for the task time series. We select the phase-randomized null model since it maintains most of the statistical properties of multivariate time series  [t−test, p < 0.05, FDR).We performed student’s s t-test  to test s t-test  were utis t-test , as wells t-test . To idenariance) . Next, fU) concatenated over all subjects. Next, we identified a single input with the highest absolute principal component (PC) loading for every component. We then multiplied the selected inputs with negative PC loadings by −1. Next, we separately fitted a multiple linear regression model for each PC’s inputs (U) using the known task-regressors. We created task-regressors for different conditions by assigning every sample to baseline (0) or one of six events  based on their temporal proximity to events’ onsets and offsets. We repeated this analysis by shifting task-regressors by different lags (0–12 TRs) to identify the lag that produces the best fit  for each region. Finally, we performed t−tests on estimated coefficients at the group-level to identify task conditions similarly echoed in estimated inputs associated with each PC across participants. We also identified brain regions that correspond to the identified inputs by performing group-level region-wise t−tests on input matrix B elements that correspond to inputs U identified by PCs.To identify estimated inputs that display changes that correspond to different task conditions in the motor paradigm, we first performed a principal component analysis (PCA) on all estimated inputs  to establish the statistical significance of the measured non-stationarities across patients. Traditionally, researchers have commonly used the 0.05 as the statistical significance level, though the choice is largely subjective. Therefore to convey the probabilistic nature of the statistical analysis and the proper interpretation of statistical test results, in the manuscript, we refer to results of the commonly accepted statistical threshold of 0.05 as “significant” and the more conservative thresholds of 0.0005 or lower as “highly significant”.We examined the estimated inputs’ non-stationarity using two methods. First, we used a sliding window approach to examine temporal fluctuations of estimated inputs’ means over resting-state scans for all brain regions, measured from the windowed-means’ standard deviation. Second, we used the nonlinear non-stationarity index introduced by , with α All subject recruitment procedures and written informed consents were approved by the Washington University Institutional Review Board (IRB). For more details see .We use the proposed method to explicitly model the contributions of internal system dynamics and external inputs on the BOLD signal during rest and task. To build intuition, we begin by estimating the internal system parameters and unknown inputs using data simulated from a synthetic LTI model  with foup < 0.0001) changes in the input matrix B’s loading for estimation windows overlapping the external stimulation periods, reveals the unknown external inputs’ spatial profile   . Togethet−test, p < 0.05, p = 6.6 × 10−65 and p = 2.9 × 10−66 for 1000 TR- and 250 TR-long estimation windows, respectively) increase in the similarity between the ground-truth and estimated inputs  task. We confirm that similar to the low-dimensional example in d inputs . The nott-values) at various recording and internal noise levels show how different noise levels can lead to seemingly similar outputs.So far, we have examined the LTI system’s response in a low recording noise level . Next, we examine the accuracy of the retrieved model and input parameters at different recording and internal noise levels. The contributions of the recording and internal noise to the BOLD signal, for the most part, are unknown quantities. However, they play an essential role in our ability to capture external inputs accurately. Simulating the system’s response magnitude and variance .We begin by showing that the estimated system parameters during resting-state reliably capture and reproduce known brain functional organization. Further, because these parameters reside within a quantitative dynamical model, we simultaneously capture both spatial (regions that are co-active) and temporal (oscillation frequency) information through the k-means clustering analysis. We used the elbow method , Calinski-Harabasz, Davies-Bouldin, and Silhouette criteria  to identify the optimal clustering resolution—for details, see SI5 and S3 Fig in k = 2) and finer scale (k = 4) clusters in S2A Fig and n = 50, 000, p < 0.05) fail to find any difference between cluster’s means. To test the spatial inhomogeneity in the frequency and damping of these clustered eigenvectors, we performed a pairwise comparison between the distribution of eigenvalues corresponding to the eigenvectors in each of the clusters . We found significant differences in the frequencies and damping rates between all cluster pairs, except for the comparison between the frequencies in clusters 3 and 4). Together, these findings highlight the spatial heterogeneity in the frequency and damping profiles of brain oscillations.To capture the spatial and temporal patterns of activity, we use our method to estimate the internal system parameters from the resting-state time series (1200 TR ≈14.5 min). The high stability of the  low-frequency eigenvalues as seen in Up to now, we provided evidence that the system dynamics can capture the spatial and temporal behavior of resting-state brain networks. Next, we try to assess if the task-induced dynamics are driven by the external inputs, retrieved by the proposed method. The sensory inputs to the brain are some of the major drivers of cortical dynamics. Therefore, we hypothesize that the external inputs to the subjects’ brains, as estimated by the proposed method, will mirror real-time changes present in these task regressors  reveal highly significant unique peaks, matching the expected external task-specific frequencies . Then, we compare the average estimated inputs’ frequency spectrum for each task. Statistical tests  to all brain regions  averaged across all subjects during the motor task. These results highlight the brain-wide significant task-specific changes in the estimated inputs when system parameters are estimated from the resting-state time series B’s dimension . Comparisons against the phase-randomized null time series also provide converging observations  improved fit , compared to system parameters estimated from the motor task  and the spatial  profiles of the external inputs (estimated using resting-state system parameters), to demonstrate how the estimated inputs reveal the dimensionality and the spatiotemporal dependencies of the task-related inputs. Prior works using univariate and multivariate analyses of HCP task datasets have demonstrated that activation induced by the hand, foot, and tongue movements can be localized over the somatomotor network. Therefore, we expect the dimensionality of the external inputs to roughly match or exceed those of task conditions . As mentioned in the Materials and Methods section, the principal component analysis reveals that in all HCP task conditions, principal components (PCs) 1–25 explain more than 80% of the variance in the model’s average residuals. Therefore, we choose p = 25 as the input matrix B dimension in Next, we examine the temporal  concatenated across all subjects to identify the input patterns similarly identified over the group. R2 values for PCs 1–9. R2 values) identified in We performed principal component analysis on external inputs estimated temporal profiles  higher R2 values. B values corresponding to PCs 2, 4, and 6 reveal the same regions identified in the somatomotor cortices using general linear model analysis of BOLD time series for hand, foot, and tongue movements.Next, we examine spatiotemporal profiles of subject-level estimated inputs associated with these components to understand their relationship to the external stimuli. B also captures the spatiotemporal relationship between the inputs across different conditions. For instance, S9A Fig in The input matrix R2 values of these PCs than other PCs in Finally, in R2 value of the linear regression) of the estimated inputs  from the temporal fluctuations  of external inputs’ means, measured using a sliding window. t-test, p < 0.05, Bonferroni corrected for multiple comparisons) higher non-stationarity of external inputs to identified DMN regions relative to several other resting-state networks  cannot be fully attributed to linear stochastic processes [system and provide evidence of system nonlinearities [Can the brain during resting-state scans be fully described as a rocesses –76, and rocesses . Additioearities –80.Model-based approaches such as work by , 81 haveStationary signals are characterized by time-invariant statistical properties, such as mean and variance . To dateNotably, the presence of non-stationarity in the outputs does not directly imply the underlying system’s non-stationarity. An LTI system’s outputs, for instance, while receiving non-stationary external inputs, can also display time-varying properties. As mentioned earlier, using internal system parameters of an LTI system estimated over resting-state scans enables more accurate identification of exogenous inputs’ spatiotemporal profile task scans. These results suggest that a large-scale stationarity model of the brain with time-varying external inputs can, in theory, account for a large portion of the observed task-related changes in cortical dynamics. It is worth noting that any possible task-related changes in the underlying system parameters are also captured as external inputs in an LTI framework. Therefore, from the system identification and model-fitting perspective, it is likely that a linear switching system with higher degrees of freedom would improve the fit. Beyond the goodness-of-fit of the model, care should be taken in interpreting the epiphenomenal large-scale models’ parameters and their changes at the micro-scale biophysical level.system from its outputs over resting-state by examining estimated inputs’ non-stationarity. Our results show that external inputs’ non-stationarity over resting-state scans are spatially inhomogeneous, with identified DMN regions showing the highest levels consistently across different analyses. These observations are in line with prior reports of higher dynamic functional connectivity of these brain structures over rest [However, the impetus for this work is to highlight the estimates’ notable sensitivity to the unknown, and thus, unaccounted external inputs. More practically, when simulated with a wideband unknown external inputs, our results suggest that an open LTI model estimated during resting-state allows us to uncover the influence of these unknown drivers of BOLD dynamics. Nevertheless, participants’ cortices receive external stimulation even during resting-state scans, contributing to estimation inaccuracy and the system’s outputs’ non-stationarity. In this work, we aim to disentangle the non-stationarity of the ver rest . The idever rest , 90, ourver rest  and contver rest  work havHistorically, a narrow band of slow frequencies between 0.01 to 0.1 Hz was thought to contain information relevant to underlying neural activity, and that the higher frequency (> 0.1 Hz) BOLD activity considered mainly as an artifact , 93. OurHowever, the HRF plays another critical role in biophysical models where it enables the approximation of the latent neural states from the BOLD signal. This is one of the main limitations of our simplified model, as it incorrectly assumes that the BOLD signal in one region  can cause changes in the BOLD signal in the connected regions. This assumption for spatially inhomogeneous HRF functions can, in theory, lead to incorrect identification of the external inputs’ focus and error in the direction and speed of the interactions within functional networks. We believe the overlapping patterns of inputs and the task activation maps identified using the conventional univariate general linear model analysis suggest that the above-mentioned error is likely tolerable. To improve the estimated unknown inputs’ accuracy, future work should leverage the formulated quantitative spatiotemporal , 96–98 mStructured recording noise such as autocorrelated noise can negatively impact the modeled system , 90, andOne of the current limitations of our proposed framework is that the estimated inputs’ accuracy depends on the internal and recording noise levels. We show that group-level analysis and repeated measurement designs are effective strategies to increase signal-to-recording noise and to increase the estimated inputs’ accuracy. In addition, although we can not accurately tease out the contributions of internal noise from other sources of noise, our simulations and experimental results suggest lower levels of internal noise relative to external drivers in task fMRI. We draw this conclusion based on the relatively large input estimation errors associated with system parameters identified during external stimulation.We used individual subjects’ resting-state datasets to identify the system parameters for uncovering the unknown inputs from the BOLD signal. Although beyond the scope of our current work, it is critical to comprehensively examine the identifiability of the estimated system parameters across different scanning sessions, types, and individuals. Subsequently, it would be interesting to explore further the extent to which our proposed system identification framework can highlight the shared features across subjects or increase the accuracy of the subject-specific mapping of spatiotemporal dynamics.data-driven methods such as our proposed framework and the hypothesis-driven methods such as DCM [Finally, it is worth highlighting that model-based h as DCM  are comph as DCM .open system. Broadly, our approach provides a framework for understanding the brain’s large-scale functional dynamics and non-stationarities, mechanistically via the modeled system and its time-varying drivers.We show that the proposed framework provides an avenue to uncover the structure of the unknown drivers of BOLD signal fluctuations and shines light on factors that contribute to its apparent non-stationarities. However, more significantly, our results highlight the importance of modeling and interpreting the brain’s dynamic functional connectivity as an S1 File(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Chemotaxis signaling pathways enable bacteria to sense and respond to their chemical environment and, in some species, are critical for lifestyle processes such as biofilm formation and pathogenesis. The signal transduction underlying chemotaxis behavior is mediated by large, highly ordered protein complexes known as chemosensory arrays. For nearly two decades, cryo-electron tomography (cryoET) has been used to image chemosensory arrays, providing an increasingly detailed understanding of their structure and function. In this mini-review, we provide an overview of the use of cryoET to study chemosensory arrays, including imaging strategies, key results, and outstanding questions. We further discuss the application of molecular modeling and simulation techniques to complement structure determination efforts and provide insight into signaling mechanisms. We close the review with a brief outlook, highlighting promising future directions for the field. Bacterial chemotaxis pathways couple the sensing of environmental chemical gradients to changes in cellular motility and are the best-understood biological signaling systems owing to over half a century of investigation . MoreoveEscherichia coli, owing to its relative tractability in terms of the number of signaling components. An overview of the E. coli chemosensory pathway is provided in The study of chemotaxis pathways has historically largely been conducted in the model organism The structures and signaling mechanisms of chemoreceptors and CheA have been studied intensely ,6. Both Central to the chemotaxis response is the clustering of thousands of copies of chemoreceptors, CheA, and CheW into large, transmembrane signaling complexes known as chemosensory arrays  3,7]. T,7. T3,7]Despite impressive results using techniques such as X-ray crystallography and NMR spectroscopy, high-resolution information on the CSU has mostly been limited to individual domains and sub-complexes due to its large size and dynamic nature. Cryo-electron tomography (cryoET) provides a powerful means to study large and heterogeneous biomolecular complexes such as chemosensory arrays that are intractable using other structure determination techniques . In thisE. coli . Wh. Wh23]. E. coli  and C. cescentus  were obtescentus ,29, suggin vitro reconstituted system has also been developed, which involves the use of purified recombinant E. coli Tar cytoplasmic fragments (TarCF), CheA, and CheW to form the array lattice on a 2D lipid monolayer . Agmodeling  analogouopy data .E. coli transmembrane CSU was presented  achieving near-atomic resolution structures of the E. coli CSU and larger array and (ii) characterizing array structure and dynamics in distinct signaling states. Continued improvements in hardware and image processing combined with the in vitro monolayer system, potentially stabilized via strategic mutation, has the potential to provide near-atomistic resolution structures  and the extended array. A comprehensive molecular picture of signaling, however, has so far remained elusive, owing to the lack of high-resolution descriptions of array structure and dynamics in distinct signaling states.We now have a basic understanding of the structure of the in situ and in vitro imaging contexts to provide increasingly detailed images of the CSU in varying signaling states, hopefully achieving near-atomic resolution in certain cases. Molecular modeling and simulation techniques will provide a complementary and independent tool to access high-resolution structural and dynamic detail. Down the road, the development of time-resolved cryoET methodologies should provide millisecond-resolved structural information on native arrays in experimentally controlled signaling states.Future cryoET applications will seek to modify existing


In the present work, we developed and evaluated the performance of a new flow cytometry-based single platform, referred to as “FC-Duplex IgG1 (HTLV-1/2)”, for universal and differential serodiagnosis of HTLV-1/2 infection. The proposed technology employs a system for detection of IgG1 antibodies in a single competitive immunofluorescence platform by flow cytometry using fluorescently labeled MT-2/MoT cell line mix coupled to a highly sensitive development system (Biotin/Streptavidin/Phycoerythrin). The stability of fluorescent labeling and the antigenicity of MT-2 and MoT cell lines were confirmed upon storage at −20°C for 2, 6, and 12 months. The anti-HTLV-1/2 IgG1 reactivity, expressed as percentage of positive fluorescent cells (PPFC), was evaluated for each target antigen along the titration curve of test serum samples . Upon selection of target cell line and serum dilutions with higher segregation score between groups, the performance of “FIX” and “FIX & PERM” protocols was evaluated. The “FIX” protocol presented excellent performance indices  for the universal (HTLV-1/2 vs. NI) and differential (HTLV-1 vs. HTLV-2) diagnosis of HTLV-1 infection, respectively. Optimization of the “FIX” protocol using the principle of synchronous and asynchronous pairwise analysis further improved the performance of “FC-Duplex IgG1 (HTLV-1/2)”, using the “FIX” protocol for differential diagnosis of HTLV-1 and HTLV-2 infections (Se = 100%/Sp = 100%/AUC = 1.00). In conclusion, the “FC-Duplex IgG1 (HTLV-1/2)” method represents an innovation in the biotechnology segment with the potential to compose a serological kit for differential diagnosis of HTLV-1/2 infection for reference laboratories and blood centers. The human T-cell lymphotropic virus (HTLV) is a retrovirus with a global distribution. The HTLV is classified into four distinct types, HTLV-1, HTLV-2, HTLV-3, and HTLV-4  , 6, in aThe diagnostic methods for HTLV1/2 infection include an initial screening test, usually enzyme-linked immunosorbent assay (ELISA), chemiluminescence enzyme-linked immunoassay (CLEIA), or particle agglutination (PA), followed by a confirmatory test, including Western blot (WB), innogenetics line immunoassay (INNO-LIA), as well as qualitative and/or quantitative polymerase chain reaction (PCR). In general, the serological diagnosis of HTLV infection is based on the detection of specific antibodies to different HTLV antigens , 13. HowThe differential diagnosis between HTLV-1 and HTLV-2 infections can be achieved by molecular methods. However, molecular approaches are expensive, time-consuming, and labor-intensive, requiring specialized laboratory infrastructure. In this sense, antibody tests are the best option for routine diagnostic purposes . The avaIn this scenario, there is still no gold standard method for differential diagnosis of HTLV-1 and HTLV-2 infections to meet the needs of blood centers and clinical laboratories . In genePrevious studies from our group have demonstrated the applicability and accuracy of flow cytometry-based methods for detecting specific anti-HTLV-1 IgG1 antibodies , 20. AimThe Interdisciplinary Research Group on HTLV—GIPH—is an open cohort, started in March 1997, which is a world reference to evaluate the natural history of HTLV infection.n = 114) and non-infected controls . The HTLV-1/2 group was composed of patients of both genders, aged from 18 to 75 years old, under medical care by one of us (AS) at the Hospital das Clínicas of the Universidade Federal de Minas Gerais—UFMG. The HTLV-1/2 patients with positive serology for hepatitis B or C, Chagas disease, syphilis, and HIV infection, and drug use history in the preceding 6 months or in the use of immunomodulatory therapy were excluded. Based on molecular diagnosis, the HTLV-1/2 group was further classified as HTLV-1 (n = 88) and HTLV-2 (n = 26) patients with confirmatory diagnosis by RT-qPCR. During clinical and neurological examinations, the HTLV-1 group was further categorized into three subgroups, considering the HAM diagnosis criteria according to De Castro-Costa et al. (n = 27); HTLV-1 with putative HAM status that did not fulfill the De Castro-Costa criteria, i.e., EDSS and OSAME scores = 1 to 2 ; and patients with definite diagnosis of HTLV-1-associated myelopathy/tropical spastic paraparesis, with EDSS and OSAME scores higher than 2 .The present investigation included 135 subjects from the GIPH cohort, comprising patients with positive serology for HTLV-1/2 . All participants have signed a written informed consent and the study followed the guidelines and standards for research involving human beings, according to the resolution 466/2012 from the Brazilian National Health Council.Whole blood samples (10 ml) were collected without anticoagulant from each participant to obtain serum samples. Serum was aliquoted and stored in −80°C until processing by FC-Duplex IgG1 (HTLV-1/2) assay.Stocks of MT-2 and MoT cell lines, permanently infected by HTLV-1 and HTLV-2, respectively, were used as antigen support for Flow Cytometric “FC-Duplex IgG1 (HTLV-1/2)” assay. The MT-2 cell line was obtained from the Bio-Manguinhos, FIOCRUZ/RJ. This cell line was originally obtained from co-culture of cells from a patient with adult T-cell leukemia/lymphoma (ATLL) and umbilical cord lymphocytes , 25. TheThe MoT cell line was kindly provided by Dr. Steven Jacobson from the National Institutes of Health . This cell line was originally obtained from a patient infected with the HTLV-2 with hairy cell leukemia .6 cells/ml) into RPMI medium supplemented with 20% of fetal bovine serum (FBS) using 75-cm2 tissue culture bottles. The cultures were maintained at 37°C, 5% CO2, 95% in a humidified incubator for 72 h. At each passage, cell viability was estimated by Trypan Blue staining, using the automated cell counter . The cultures in log phase were maintained by continuous passages to obtain bulk batches (1.0 × 109 cells). The cells were homogenized and submitted to differential centrifugation at 80 × g for 10 min at room temperature to eliminate cell clumps and recover the single-cell suspension in the supernatant. The single-cell suspensions were washed twice with phosphate buffered saline (PBS) supplemented with 0.5% FBS (PBS 0.5% FBS) by centrifugation at 400 × g for 10 min at 4°C. The cell suspension was adjusted to 1.0 × 106 cells/ml in PBS.Both cell lines were maintained and cryopreserved in liquid nitrogen at Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais. All procedures were performed in Level 2 biosecurity laboratory. Bulk-culture batches were carried out for large-scale production of MT-2 and MoT antigen supports for Flow Cytometric “FC-Duplex IgG1 (HTLV-1/2)” assay. For this purpose, MT-2 and MoT aliquots cells were thawed and seeded (approximately 1.0 × 106 cells/ml) were mixed with equal volume of FACS Fix Solution  and incubated overnight at 4°C. The “FIX” cell suspensions were washed once with PBS  and maintained at 4°C until use in the “FIX” protocol.The MT-2 and MoT single-cell suspensions were submitted to the “FIX” protocol, as previously described by Coelho-Dos-Reis et al. , modifie6 cells/ml in PBS) were centrifuged and resuspended in equal volume of PBS supplemented with 0.5% bovine serum albumin plus 0.1% sodium azide, 0.5% saponin (PBS-P) and incubated for 10 min at room temperature. Following the “FIX & PERM”, cells were washed once  with PBS supplemented with 0.5% bovine serum albumin plus 0.1% sodium azide (PBS-W) and maintained at 4°C until use in the “FIX & PERM” protocol.In parallel, aliquots of MT-2 and MoT single-cell suspensions were submitted to the “FIX & PERM” protocol, as previously described by Coelho-Dos-Reis et al. , modifieThe morphometric profiles of MT-2 and MoT cell lines, submitted to both protocols (“FIX” and “FIX & PERM”) were monitored by flow cytometry, using the size [Forward Scatter (FSC)] and granularity [Side Scatter (SSC)] parameters.6 cells/ml in PBS) were incubated for 30 min, at 37°C with FITC  or Alexa Fluor 647  in serum-free PBS. The dye concentrations were determined by pre-testing several concentrations (data not shown). No pre-activation of dyes with an amine or thiol group was required. After incubation, the stained cell suspensions were washed once with serum free PBS  and submitted to quality control assessment by flow cytometry.The MT-2 and MoT cell lines used in both protocols, “FIX” and “FIX & PERM”, were previously submitted to a protocol of differential fluorescent staining using distinct concentrations of one fluorescent dye: either fluorescein isothiocyanate-FITC  or Alexa Fluor 647 Triethylammonium Salt . For this purpose, aliquots of “FIX” and “FIX & PERM” cells  were monitored by flow cytometry as quality control, using the FL1 (FITC) and FL4 (Alexa Fluor 647) parameters. Immediately prior to use in the “FC-Duplex IgG1 (HTLV-1/2)” assay, a 1:1 mixture of FITC or Alexa Fluor 647-labeled MT-2 and MoT cell lines (Mix MT-2/MoT) was prepared with 50% proportion and monitored by flow cytometry using the FL1 (FITC) and FL4 (Alexa Fluor 647) parameters. Autofluorescence of unstained MT-2 and MoT cell lines was monitored prior to the fluorescent staining procedure. 4 cells/well), accordingly for the “FIX” and “FIX & PERM” protocols. The plate was homogenized with circular movements and incubated for 30 min at 37°C. After incubation, the MT-2/MoT mix was washed with 100 μl and subsequently with 150 μl of PBS-3% PBS , the supernatant was discarded, and the pellet was homogenized. Thereafter, 50 μl of biotin-labeled human anti-IgG1, clone 8c/6-39 , plus 10 μl of Phycoerythrin-labeled Streptavidin-SAPE (1: 400) were added as secondary reagents to each well, previously diluted with PBS-3% FBS or PBS-P for the “FIX” and “FIX & PERM” protocols, respectively. The plates were incubated for 30 min at 37°C, and the MT-2/MoT mix was washed twice, as described above. After centrifugation, the supernatant was discarded, the pellet was homogenized, and 200 μl of FACS Fix Solution was added to each well. The plates were kept at 4°C, protected from light, up to 24 h prior to acquisition in the flow cytometer. Internal controls of nonspecific binding of secondary reagents were carried out for each experimental batch. In a pre-analytical step, serum aliquots maintained at −80°C were thawed at 37°C, inactivated at 56°C for 30 min, and centrifuged at 14,000 rpm at 4°C for 5 min to remove debris. Fifty 50-μl aliquots of heat inactivated serum were transferred to “U” bottom 96-well plates  and submitted to twofold serial dilution  in PBS-3% FBS. Subsequently, each well received 50 μl of FITC or Alexa Fluor 647-labeled MT-2/MoT mix . A total of 10,000 events were acquired per sample and data were stored using the CellQuest™ software package .Following the standard operating procedure for the “FC-Duplex IgG1 (HTLV-1/2)” assay, the MT-2 and MoT Mix incubated with the test serum samples was acquired in a FACSCalibur Flow Cytometer  equipped with Argon (488 nm) and HeNe (633 nm) lasers using previously defined instrument settings for FSC (size) and SSC (granularity), both in linear scales, as follows: FSC = E00; SSC = 300. The threshold was set on FSC = 200 to minimize noise interference during acquisition. The fluorescence parameters  were set on 6 mixed cells per vial. The fluorometric profile of differential labeling was monitored at 2, 6, and 12 months after freezing. For that, cells were thawed at water bath at 37°C and resuspended in PBS 1× for flow cytometric acquisition. In parallel, the antigenicity stability of MT-2 and MoT was also monitored using standard operating procedure for the “FC-Duplex IgG1 (HTLV-1/2)” assay.The stability of differential fluorescent labeling and antigenicity of MT-2 and MoT cell lines were monitored under storage at −20°C for 2, 6, and 12 months, as a quality control criterion of reagent storage for the “FC-Duplex IgG1 (HTLV-1/2)” Assay. For this purpose, cells were fixed (“FIX” protocol) or fixed and permeabilized (“FIX & PERM” protocol), stained, and then stored. In detail, aliquots of Alexa Fluor 647-labeled MT-2 and MoT, prepared using the “FIX” or “FIX & PERM” protocols were mixed in a 1:1 ratio and immediately frozen in cryovials at −20°C, without addition of any cryopreservation solutions, at the maximum concentration of 10 × 10The performance of the “FC-Duplex IgG1 (HTLV-1/2)” assay using the “FIX” and “FIX & PERM” protocols was assessed along the titration curves  using individual samples from NI vs. HTLV-1/2 for universal diagnosis as well as HTLV-1 vs. HTLV-2 for differential diagnosis. Distinct approaches were employed to define specific serum dilutions and cutoff values as well as to determine the performance indices , including scatter plot distribution of individual PPFC values, Two-Graph Receiver Operating Characteristic plot (TG-ROC), and ROC curve analysis. Optimized performance of the “FC-Duplex IgG1 (HTLV-1/2)” assay was further assessed using synchronous and asynchronous strategies of pairwise serum dilutions. The MedCalc version 7.3.0.0  was employed for performance analysis. Prisma GraphPad software version 5.0  was used for graphical arts.The stability of fluorescent stained MT-2 and MoT cell lines was evaluated upon selective gating strategies as described in methods ” assay was assessed for universal diagnosis of HTLV-1/2 infection using both “FIX” and “FIX & PERM” protocols Figure 4Scatter plot distribution of individual values of IgG1 reactivity, TG-ROC parameters, and ROC curve analysis indicated that a single pair of attributes “serum dilution/cutoff” (MoT 1:32/PPFC = 20%) using the “FIX” protocol presented excellent performance indices  for the universal diagnosis of HTLV-1 infection (The performance of the “FC-Duplex IgG1 (HTLV-1/2)” assay was further assessed for differential diagnosis of HTLV-1 from HTLV-2 infections using both “FIX” and “FIX & PERM” protocols Figure 5The analysis of scatter plots of individual IgG1 reactivity together with TG-ROC and ROC curve profiles indicated that a single pair of attributes “serum dilution/cutoff” (MT-2 1:32/PPFC = 40%) using the “FIX” protocol presented outstanding performance indices  for the differential diagnosis of HTLV-1 and HTLV-2 infections (Aiming at further optimizing the performance of the “FC-Duplex IgG1 (HTLV-1/2)” assay using the “FIX” protocol for differential diagnosis of HTLV-1 from HTLV-2 infections, the overall profile of IgG1 reactivity was calculated as the differential reactivity between anti-MT-2 and anti-MoT cell lines (Delta PPFC = %MT-2 – %MoT) along the titration curves , using synchronous and asynchronous pairwise serum dilutions Figure 6Aiming at defining the best pairwise condition to segregate HTLV-1 from HTLV-2 subgroups, the performance of selected synchronous [MT-2 1:32 – MoT 1:32] and asynchronous pairwise  and  was further evaluated based on the scatter distribution of individual values, considering the number of samples confined into the 10% gray zone of selected cutoff values Figure 7Considering the relevance of antibody quantification as a prognosis biomarker for HTLV-1 clinical progression from asymptomatic cases (HAC) towards HAM status, the performance of the “FC-Duplex IgG1 (HTLV-1/2)” assay and that of both “FIX” and “FIX & PERM” protocols were evaluated, and the data are presented in The HTLV-1/2 is a public health problem, representing a silent global challenge with estimates of at least 20 million people infected with HTLV-1 around the globe , 4. BesiNoteworthy is the importance of these viruses not only due to the distinct epidemiological and the clinical features, but also by the fact that the differential diagnosis of these viral infections still represents a challenge for clinical laboratories. Currently, the diagnosis of HTLV-1/2 infection is primarily based on detecting specific IgG antibodies in serum using ELISA and chemiluminescent assays such as CLIA or ECLIA –28. SeveIn this sense, only WB and qualitative and/or quantitative PCR are able to perform a type-specific diagnosis of HTLV-1 and HTLV-2 infections . AlthougConsidering these impairments in HTLV diagnostic methods, various countries that perform universal screening and differential diagnosis of HTLV-1/2 infections are raising the debate in and around the cost-effectiveness of such complex and stepwise diagnostic approaches. In general, the strategies proposed for differential diagnosis of HTLV-1 and HTLV-2 infections require the use of multiple assays using distinct serological and molecular platforms. Recently, Ji and collaborators performed a study that aimed at evaluating screening and confirmatory assays for the diagnosis of HTLV-1/2 infection . SeveralIn the present study, we have developed an innovative flow cytometry assay, referred to as “FC-Duplex IgG1 (HTLV-1/2)” for universal and differential diagnosis of HTLV infections. The method herein proposed is a single-step optimized serological procedure for both universal and differential diagnosis of HTLV-1 and HTLV-2 infections. Our results demonstrated the high accuracy of the method in identifying HTLV-1 and HTLV cases using an automated high-throughput screening method.To achieve the single competitive immunofluorescence platform by flow cytometry, a differential fluorescent labeling protocol was designed for HTLV-1 (MT-2) and HTLV-2 (MoT) cell lines as a specific antigenic solid support, using FITC and Alexa Fluor 647 dyes. Differential fluorescent labeling protocol with Alexa Fluor 647 or FITC free dyes can be achieved by using distinct concentrations since the fluorescence intensity emitted by any of these fluorescent dyes is proportional to the number of molecules bound to the target cells. Alexa Fluor 647 and FITC usually bind to proteins leading to stable staining. In the present protocol, we have used two different concentrations of the same fluorescent dye to label the MT-2 and MoT cell lines that could be further identified by the resultant fluorescent brightness, i.e., one cell line (MT-2) displayed low fluorescence intensity and the other (MoT) exhibited higher fluorescence intensity. The antigenicity of MT-2 and MoT cell lines was also evaluated and remained stable up to 12 months at −20°C. Differential staining of antigenic support with FITC or Alexa Fluor 647, on a single flow cytometric platform has already been reported by our group to accomplish the differential serological diagnosis of Chagas disease and leishmaniasis, based on species-specific anti-Trypanosomatidae IgG1 reactivity . In thatAnother relevant aspect of the “FC-Duplex IgG1 (HTLV-1/2)” resides on its design coupled with a high sensitivity development system (Biotin/Streptavidin/Phycoerythrin) that guarantee higher detection of IgG1 binding by flow cytometry, even using high serum dilutions.Two parallel protocols of “FC-Duplex IgG1 (HTLV-1/2)” have been tested, referred to as “FIX” and “FIX & PERM”. Previous studies from our group, originally describing the innovative approach to detect anti-HTLV-1 IgG1 antibodies by flow cytometry, have tested the performance of a simplex protocol, using only the MT-2 cell line, applied to the diagnosis of HTLV-1 infection . The simUltimately, the use of synchronous and asynchronous pairwise analysis has improved the performance of “FC-Duplex IgG1 (HTLV-1/2)” for differential diagnosis of HTLV infection. These pairwise analyses, especially in the asynchronous pairs using a detuned assay, have contributed to obtain the selective reactivity to MT-2 and MoT cell lines at specific serum dilutions, leading to higher specificity.Detuned serological approaches based on algorithms employing distinct serum dilutions have previously been proposed for serological diagnosis of infectious diseases –39. The Flow cytometry is a paramount technique in deep cell analysis, and it has been employed for many years in clinical practice for the diagnosis and monitoring of HIV and cancer patients as well as other autoimmune diseases. As flow cytometry has progressed, more applications and methods have been created for the benefit of diagnosis and clinical management of chronic diseases. From simple white blood cell counts to advanced flow-based techniques, flow cytometric methods can identify and unravel critical disease-related features in a fast-track manner, thereby allowing for early prevention and anticipation of patient needs . Flow cyIn conclusion, the present study has provided a proof of concept of the “FC-Duplex IgG1 (HTLV-1/2)” method using 135 well-characterized serum samples from patients with positive serology for HTLV-1/2 and non-infected controls. The proposed method presented an outstanding overall accuracy of 100% correct results for HTLV-1 and HTLV-2 differential diagnosis. In future experiments, we will validate the performance of “FC-Duplex IgG1 (HTLV-1/2)”, which is currently under investigation to compare the performance of intra- and inter-laboratorial analysis as well as the reproducibility using the proposed protocol and data generated by independent analysts.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Research Ethics Committee of the René Rachou Institute - FIOCRUZ/MG (C.A.A.E. N° 15047313.8.0000.5091). The patients/participants provided their written informed consent to participate in this study.LP, JC-d-R, VP-M, BT, JM, LA, AC-P, ES, KB, AT-C, and OM-F designed the study, performed the experiments, and analyzed the data. LP, JC-d-R, VP-M, KB, AT-C, and OM-F wrote and reviewed the full manuscript. All authors contributed to the article and approved the submitted version.The study has been supported by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (PPSUS/FAPEMIG Grant # APQ-00821-20). MS, AT-C, JC-d-R, and OM-F received PQ Fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq. AT-C and OM-F are also grateful to the FAPEAM  for the research fellowship program. JM received PIBIC fellowship from CNPq.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study is undertaken to explore the relationship between obstructive sleep apnea-hypopnea syndrome (OSAHS) and osteoporosis, including the relationship between OSAHS and osteoporosis incidence, lumbar spine bone mineral density (BMD), and lumbar spine T-score.Cochrane Library, PubMed, Embase, Web of Science, and other databases are searched from their establishment to April 2022. Literature published in 4 databases on the correlation between OSAHS and osteoporosis,lumbar spine BMD,lumbar spine T-score is collected. Review Manager 5.4 software is used for meta-analysis.A total of 15 articles are selected, including 113082 subjects. Compared with the control group, the OSAHS group has a higher incidence of osteoporosis , the lumbar spine BMD is significantly lower , and the lumbar spine T-score is significantly decreased .Compared with the control group, the OSAHS group has a higher incidence of osteoporosis and decreased lumbar spine BMD and T-score. In order to reduce the risk of osteoporosis, attention should be paid to the treatment and management of adult OSAHS, and active sleep intervention should be carried out. OsteopoAccording to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) statement  and the 2) and/or T-score were measured by dual energy X-ray densitometer, and osteoporosis was defined as BMD and T-score < -2.5 SD (Inclusion criteria (1): the subjects of the study were adults over 18 years of age (2); the article types were cohort studies, case-control studies, and cross-sectional studies, observing BMD and T-score in patients with sleep apnea or obstructive sleep apnea, evaluating the incidence or prevalence of osteoporosis, and comparing them with the control group (3); OSAHS was diagnosed by polysomnography or portable sleep monitor, and the severity was evaluated by the apnea hypopnea index (AHI), which is the sum of the average number of apnea and hypopnea events per hour  (4); lum -2.5 SD  (5); basExclusion criteria (1): languages other than English (2); studies without a control group (3); studies where the effect size cannot be extracted or calculated (4); studies for which the authors did not respond to contact or could not provide meta-analysis data (5); application of glucocorticoids or other drugs that affect BMD.Two researchers independently searched, extracted and screened the literature, checked each other’s work, and provided literature with differences to the third researcher for analysis to decide whether or not it should be included. The methodological quality of the included literature was assessed. The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS) . Only hi2) and/or T-score of the subjects were obtained by dual-energy X-ray densitometry, and OSAHS was diagnosed by polysomnography or portable sleep monitor. The incidence of osteoporosis, BMD, and lumbar spine T-score in the OSAHS group and control group were used as outcome indicators. The difference in the incidence of osteoporosis between the OSAHS group and control group indicated the correlation between OSAHS and osteoporosis; the difference in BMD between the OSAHS group and control group indicated the effect of OSAHS on BMD; when the OSAHS group was compared with the control group, the level of lumbar spine T-score was different, indicating the influence of OSAHS.The lumbar spine BMD , so a random effect model was used. The results showed that compared with the control group, the OSAHS group had a higher incidence of osteoporosis .Three studies , 13, 14 2 = 7%), and there was no statistically significant heterogeneity among the male group  = 0.2, I2 = 38%), female group , so a fixed effect model was used. After gender subgroup analysis, the heterogeneity of the osteoporosis studies was significantly reduced, male , female , The incidence of osteoporosis in OSAHS group was higher and statistically significant; the combined final effect size of the gender subgroup analysis was ; The incidence of osteoporosis in OSAHS group is high and statistically significant. In the subgroup analysis of age, the combined heterogeneity of the two groups was , and there was slight heterogeneity in the statistics. The elderly (> 65 years old) group was  and the middle-aged (40~65 years old) group was , so a fixed effect model was used. After age subgroup analysis, the heterogeneity of the osteoporosis studies was significantly reduced. The elderly (> 65 years old) group was  and the middle-aged (40~65 years old) group was , so the OSAHS group had a higher incidence of osteoporosis, which was statistically significant. The combined final effect size of the age subgroup analysis was ; the OSAHS group had a higher incidence of osteoporosis, which was statistically significant.Two studies , 13 provThe forest plot analysis of OSAHS and the incidence of osteoporosis suggest that OSAHS is associated with the prevalence of osteoporosis and is a risk factor for the disease.2 = 66%, P = 0.002), so a random effect model was used. The elderly population is at increased risk for OSAHS . There was moderate heterogeneity in the statistics. In the subgroup analysis, the OSAHS diagnostic heterogeneity criteria of AHI > 5~10 events/h group was , and the grouping heterogeneity of AHI > 15 events/h was , so a random effect model was used. After the subgroup analysis of OSAHS diagnostic criteria AHI grouping, the correlation between OSAHS and lumbar spine BMD was different. The quality was significantly reduced. The results of subgroup analysis showed that compared with the control group, the lumbar spine BMD of the OSAHS group with AHI > 5~10 events/h was slightly lower , the lumbar spine BMD in the AHI > 15 events/h group was significantly decreased , and the difference was statistically significant. The effect size of lumbar spine BMD in the OSAHS group AHI > 15 events/h was higher than that of the OSAHS diagnostic criteria AHI > 5~10 events/h group, indicating that in patients grouped by OSAHS diagnostic criteria AHI > 15 events/h, compared with AHI > 5~10 events/h, the risk of lumbar BMD decline was higher, so the severity of OSAHS may be related to lumbar BMD. The combined effect size of the AHI group was . The OSAHS group had lower lumbar spine BMD, the results remained unchanged after excluding risk factors for osteoporosis, and the difference was statistically significant.In a subgroup analysis of AHI grouping with OSAHS diagnostic criteria (after the exclusion of osteoporosis-related risk factors) Figure 62 = 71%), indicating moderate statistical heterogeneity. In the subgroup analysis, the grouping heterogeneity in East Asia was , that in the Middle East was (P = 0.14), I2 = 49%), and that in Europe was , so a random effect model was used. The results of the subgroup analysis showed that, compared with the control group, the East Asian group was , Middle East group was . CI: -0.07~0.00, Z = 1.89, P = 0.06), and European group was , so the lumbar spine BMD was lower in each regional grouping, but the difference was not statistically significant. The combined effect size of the regional grouping was , indicating that the OSAHS group had lower lumbar spine BMD, and the subgroup analysis was performed after excluding osteoporosis risk factors when the results remained stable and the difference was statistically significant.In the regional subgroup analysis (after excluding osteoporosis-related risk factors), Figure 7The forest plot analysis of OSAHS and lumbar spine BMD studies suggested that OSAHS was associated with lumbar spine BMD, OSAHS was a risk factor for the decrease in lumbar spine BMD, and the severity of OSAHS may be related to lumbar spine BMD.2 = 87%, P < 0.001), so a random effect model was used. Similarly, risk factors such as old age, diabetes, hypertension, and some diseases that affect osteoporosis may affect the results of osteoporosis research, leading unstable results for the association between OSAHS and lumbar spine T-score. In order to further verify the relationship between OSAHS and lumbar spine T-score, and further reduce the clinical heterogeneity of the studies, we conducted a subgroup analysis after excluding risk factors related to osteoporosis, including subgroups grouped by AHI according to the OSAHS diagnostic criteria. The research population of Sforza2013 . %), and the grouping heterogeneity of AHI ≥ 15 events/h was , so a random effect model was used. After the OSAHS diagnostic criteria AHI grouping subgroup analysis, The heterogeneity in the correlation study between OSAHS and lumbar spine T-score was significantly reduced, and the heterogeneity of AHI ≥ 5~10 events/h group and AHI ≥ 15 events/h group were reduced to moderate heterogeneity. The results of the subgroup analysis showed that compared with the control group, the lumbar spine T-score in the OSAHS AHI ≥ 5~10 events/h group was decreased , the lumbar spine T-score in the AHI > 15 events/h group was significantly decreased , and the difference was statistically significant. The lumbar spine T-score effect size of the OSAHS diagnostic criteria AHI ≥ 15 events/h group was higher than that of the OSAHS diagnostic criteria AHI ≥ 5~10 events/h group, indicating that compared with AHI ≥ 5-10 events/h group, The risk of lumbar T-score decline was higher in the the patients in the OSAHS diagnostic criteria AHI ≥ 15 events/h group, and the severity of OSAHS may be related to lumbar T-score. The combined effect size of the AHI group , the lumbar spine T-score of the OSAHS group was also lower, excluding the risk factors related to osteoporosis. After group analysis, the results remained stable and the difference was statistically significant.In a subgroup analysis of AHI grouping according to OSAHS diagnostic criteria (after the exclusion of osteoporosis-related risk factors) Figure 92 = 76%), indicating a high degree of statistical heterogeneity. In the subgroup analysis, the grouping heterogeneity in East Asia was , that in the Middle East was . = 0.002, I2 = 80%), and that in Europe was , so a random effect model was used; after the regional subgroup analysis, the heterogeneity of OSAHS and lumbar spine T-score correlation analysis was lower than before, among which the heterogeneity of East Asian grouping was reduced to moderate heterogeneity. The results of the subgroup analysis showed that, compared with the control group, the OSAHS group had a statistically significant difference in the Middle East group ; in the East Asian group , lumbar spine T-score was lower, but the difference was not statistically significant. In the Europe group , the lumbar spine T-score was lower, and the difference was also not statistically significant. The combined effect size of regional grouping , the lumbar spine T-score of the OSAHS group was lower, After excluding the risk factors for osteoporosis, the results remained stable and the difference was statistically significant.In the regional subgroup analysis (after the exclusion of osteoporosis-related risk factors), , 20–23, The forest plot analysis of OSAHS and lumbar spine T-score studies indicated that OSAHS is associated with lumbar spine T-score, OSAHS is a risk factor for lumbar spine T-score reduction, and the severity of OSAHS may be related to the lumbar spine T-score.In this study, a sensitivity analysis was performed for the results with high heterogeneity. In the sensitivity analysis of the incidence of osteoporosis, lumbar bone mineral density, and lumbar spine T-score between OSAHS and the control group, the results and studies were combined after excluding any literature. There was no significant change in heterogeneity.The presence of publication bias was assessed using Egger’s method  and elderly people (> 65 years old), patients with OSAHS had osteoporosis. Although the incidence of osteoporosis is high, only three articles were included in the literature, so more research is needed to further stabilize the results; in addition, the study population included in the literature may have been combined with old age, hypertension, diabetes, cardiovascular disease, COPD, etc., which may have affected the osteoporosis risk factors, and there is currently no prevalence data to exclude the relevant risk factors; thus, further investigation and analysis cannot be carried out, and the results are not stable.BMD is an important indicator reflecting bone mineral content per unit area. It is mainly used to detect the osteoporosis degree, predict the risk of fracture, and provide a strong laboratory test basis for fractures caused by osteoporosis. BMD is clinically the gold standard for the diagnosis of osteoporosis . In recein vitro studies have shown that lower nighttime oxygen levels are a feature of OSAHS, while hypoxia promotes osteoclast formation and activity while inhibiting osteoblast function, thus determining bone resorption , osteopenia (T-score -1.0 to -2.5 SD) and osteoporosis (T-score < -2.5 SD) . In receIn the subgroup analysis of AHI grouping with OSAHS diagnostic criteria (after excluding osteoporosis-related risk factors), compared with the control group, OSAHS diagnostic criteria AHI > 5~10 events/h group and AHI > 15 events/h group, the lumbar spine T-score of all groups decreased, and the difference was statistically significant. The effect size of the lumbar spine T-score in the OSAHS diagnostic criteria AHI > 15 events/h group was higher than that in the OSAHS diagnostic criteria AHI > 5~10 events/h group, and the risk of lumbar spine T-score decline was higher than that in the AHI > 5~10 events/h group, indicating that the severity of OSAHS may be related to the lumbar spine T-score. In the regional subgroup analysis (after excluding risk factors related to osteoporosis), the results showed that compared with the control group, the OSAHS group had lower lumbar spine BMD in the East Asian group, Middle East group, and Europe group, but only the Middle East subgroup was statistically significant. In conclusion, compared with the control group, the OSAHS group had lower lumbar spine T-score levels in the OSAHS diagnostic criteria AHI > 5~10 events/h group, AHI > 15 events/h group and Middle East group. The heterogeneity and research bias can be further reduced, indicating that the conclusions of this study are more reliable. At present, there is a lack of research that clearly clarifies the relationship between T-score and BMD and osteoporosis. However, because T-score is scored according to BMD, the possible mechanism of the correlation between OSAHS and T-score reduction can also be understood.This study has certain limitations: first, the number of included studies on the relationship between OSAHS and osteoporosis was small, and combined with the related risk factors of osteoporosis, the results were not stable, so more research is needed to further stabilize the study. Second, the diagnostic methods and grading methods of OSAHS in each study were slightly different, and the study populations were from different ethnic groups, which may have led to greater heterogeneity in the results. Third, osteoporosis is more common in women , but theIn conclusion, the results of this study suggest that OSAHS patients have a higher incidence of osteoporosis, and both lumbar spine BMD and lumbar spine T-score are reduced. The severity of AHI may be related to lumbar spine BMD and lumbar spine T-score. Understanding the incidence of osteoporosis in patients with OSAHS and the effect of OSAHS on lumbar spine BMD and T-score provides medical evidence. However, a homogeneous and large-scale prospective study with further adjustment for factors such as age and related diseases affecting osteoporosis is still needed to clarify whether OSAHS is a risk factor for osteoporosis and whether OSAHS has an effect on lumbar spine BMD and T-score. Many drugs have been developed to treat osteoporosis , and patThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.JHL, CW, ZPZ, ZZZ, XC and YZ are the guarantor of the manuscript and take responsibility for the content of this manuscript. JHL, QY, RC and CW contributed to the design of the study. HC, JZ, JYL and HZL were involved in the data analysis. ZPZ, JYL, HWL and RC contributed to the acquisition of primary data. ZPZ, CW and RC wrote the initial draft of the manuscript. QY, RC and JHL contributed significantly to the revision of the manuscript. All authors read and approved the final manuscript.This study was funded by the Natural Science Foundation of Guangdong Province (2021A1515011373).We would also like to thank Professor Nanshan Zhong from State Key Laboratory of Respiratory Disease for the constructive advice he gave.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Inflammatory bowel disease (IBD) is a disorder, which involves the gastrointestinal (GI) tract consisting Crohn's disease (CD) and ulcerative colitis (UC). The etiology of this disease is not yet clear and, hence, there are numerous medications and treatments for patients with IBD, although a definite and permanent treatment is still missing. Therefore, finding novel therapeutic approaches are vital for curing patients with IBD. In the GI tract, there are various lineages of cells with different roles that their existence is necessary for the barrier function of intestinal epithelial cells (IECs). Therefore, signaling pathways, which manage the hemostasis of cell lineages in intestine, such as Wnt, Notch, and Hippo, could have crucial roles in regulation of barrier function in the intestine. Additionally, these signaling pathways function as a governor of cell growth, tissue homeostasis, and organ size. In patients with IBD, recent studies have revealed that these signaling pathways are dysregulated that it could result in depletion or excess of a cell lineage in the intestine. Moreover, dysregulation of these signaling pathways in different cell lineages of the immune system could lead to dysregulation of the immune system's responses in IBD. In this article, we summarized the components and signaling of Wnt, Notch, and Hippo pathways and their role in the intestine and immune system. Furthermore, we reviewed latest scientific literature on the crosstalk among these three signaling pathways in IBD. An overview of these three signaling pathways and their interactions in IBD could provide a novel insight for prospective study directions into finding efficient medications or treatments. Inflammatory bowel disease (IBD) is a regressive inflammatory condition, which occurs in the gastrointestinal tract . PatientVarious cell lineages arise by differentiation and proliferation of intestinal stem cells (ISCs), which are controlled by multiple signaling pathways, including Hippo, Notch, and Wnt –13. ThesIn IBD, the process of wound healing and mucus secretion is dysregulated that could lead to impaired barrier function of the GI tract and ultimately leaky gut. Herein, we briefly explain the barrier functionality of intestinal epithelial cells (IECs) and the process of wound healing in IBD. In addition, it is illustrated that some proliferation pathways, including Wnt, Notch, and Hippo, could have critical impacts on these processes and the immune system. We also summarize these three signaling pathways and their role in the intestine and immune system. Finally, we concisely discuss the interactions of these signaling pathways in IBD.The intestinal epithelial cells (IECs) establish a barrier, which is selectively permeable and sets apart luminal content from beneath tissues , 22. BasThe process of wound healing starts when a part of the intestinal epithelium gets injured. Intestinal wound healing depends on the accurate balance between migration, proliferation, and differentiation of the epithelial cells, which are nearby the wounded area . First, Many cell lineages are vital for maintenance of intestinal epithelial barrier integrity, which arise by differentiation and proliferation of intestinal stem cells (ISCs) . Crypt-bThe Wnt signaling pathways are a group of signaling pathways, which commence with proteins that transmit signals into a cell by cell surface receptors , 33. TheWnt signaling pathway plays a vital role in the intestinal epithelium, specifically in regulating the stem cells' behavior, proliferation, differentiation, and migration . This paOne of the important roles of Wnt pathway is in multiple layers of immune regulation. Presence or absence of Wnt proteins could have impact on different immune cells, such as dendritic cells, macrophages, CD8+ T cells, and CD4+ T cells , 50. BasHES genes to determine the fate of the cell  . The actThe Hippo pathway is a pathway, which is remained conserved throughout the evolution and it controls the size and homeostasis of an organ by regulating cell proliferation, survival, apoptosis, and stemness , 85. SpeIt is confirmed that YAP/TAZ enhances regeneration of tissues in the mammalian intestine . AccordiRecent studies have shown that the Hippo pathway plays an important role in the modulation of immune system. MST1/2 has a pivotal role in mediating, migration, adherence, and survival of T cells by its downstream effectors, such as LATS1/2, NDR1/2, and YAP . MST1/2 There is an interplay among the proliferation signaling pathways, including Hippo, Wnt, and Notch, in intestinal regeneration  and imbaOnce the Hippo pathway gets activated, it negatively affects the Wnt signaling pathway by cytoplasmic and phosphorylated YAP/TAZ; however, deactivation of the Hippo pathway has a positive effect on the expression of Wnt target genes by nuclear and dephosphorylated YAP . Additiovia phosphorylation and suppression of the YAP.In previous studies, it is reported that the Hippo pathway is able to regulate Notch signaling. In intestine, conditional knockout of MST1/2 leads to increased amount of NICD in nucleus . ReductiWnt and Notch signaling pathways are so intertwined that it has been suggested that they established an integrated signaling termed as “Wntch” . An accuIn this short article, we aimed to overlook on three proliferation pathways playing important roles in intestine and immune system. We also reviewed some of their impacts on pathogenesis of IBD. In the process of wound and mucosal healing in a healthy condition, adjacent cells to the lesion start to proliferate and migrate to retrieve the columnar polarity of the epithelial cells . This prThis study needs more investigation in different aspects. First, a precise analysis of the relationship among these three pathways needs to be more investigated. By doing so, we will be able to understand the impact of different molecules of signaling pathways on each other. In addition, current findings demonstrated the role of these pathways in several immune cells yet not all of them. More experiments could be done in finding their role in other immune cells. Finally, these proliferation pathways can be a potential target for medications. More studies are required to develop efficient drugs for triggering epithelial cells to regulate these pathways. The results of prospective studies can dwindle the morbidity and mortality linked to IBD, hence reduce the worldwide incidence of this disease.SMK: literature search and writing. SBG, MF, SS, HA, GS, and MZ: literature search. NK: drawing of figure. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Chlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, Toxoplasma gondii, and Plasmodium falciparum. The rapid screening test also did not show cross-reaction with potentially interfering substances. Strategies for HTLV diagnosis in non- and high-endemic areas can be improved with low-cost, rapid screening tests.Initial diagnosis of human T cell lymphotropic virus (HTLV) infections is mainly based by detecting antibodies in plasma or serum using laboratory-based methods. The aim of this study was to develop and evaluate a rapid screening test for HTLV-I antibodies. Our rapid screening test uses HTLV-I p24 antigen conjugated to gold nanoparticles and an anti-human IgG antibody immobilized to a nitrocellulose strip to detect human HTLV-I p24-specific IgG antibodies via immunochromatography. Performance of the rapid screening test for HTLV-I was conducted on a total of 118 serum specimens collected in Salvador, Bahia, the epicenter for HTLV-1 infection in Brazil. Using a Western blot test as the comparator, 55 serum specimens were HTLV-I positive, 5 were HTLV-I and HTLV-II positive, and 58 were negative. The sensitivity of the rapid screening test for HTLV-1 was 96.7% and the specificity was 100%. The rapid screening test did not show cross-reaction with serum specimens from individuals with potentially interfering infections including those caused by HTLV-II, HIV-I, HIV-II, hepatitis A virus, hepatitis B virus, hepatitis C virus, herpes simplex virus, Epstein–Barr virus, SARS-CoV-2,  Human T cell lymphotropic virus (HTLV) was the first retrovirus to be discovered in humans . While fHTLV is transmitted primarily via mother-to-child routes, sexual contact, and through contaminated needles shared among drug users ,13,14. HA number of laboratory-based kits that use synthetic peptides and/or viral lysates are commercially available for initial diagnosis of HTLV infections. These kits use various detection techniques including Western blot (WB), enzyme-linked immunosorbent assay (ELISA), or electrochemiluminescent immunoassay (ECLIA) and CLIA to capture HTLV-I/II antibodies in serum or plasma . HoweverThere is a need for low-cost, non-laboratory-based methods to detect HTLV infections. The aim of this study was to develop and evaluate a rapid screening test for HTLV-I antibodies. The evaluation included cross-reaction analysis using serum specimens from individuals with potentially interfering infections and other potentially interfering substances. Performance of the rapid screening test for HTLV-I was conducted using well-characterized serum specimens collected from individuals in Salvador, Bahia, the epicenter for HTLV-I infection in Brazil .A total of 118 serum specimens were collected from individuals in Salvador, Bahia, Brazil and initially characterized by a single person using the MP Diagnostics HTLV Blot 2.4 test , according to the manufacturer’s instructions. 55 individuals were confirmed HTLV-I positive, 5 were confirmed HTLV-I and HTLV-II positive, and 58 were confirmed negative. Of the 60 HTLV positive individuals, 36 were asymptomatic and 24 were symptomatic. The symptomatic individuals had mild to moderate neurological disease, and none were wheelchair-restricted.The primary studies under which the serum samples were collected received ethical clearance from the Maternidade Climério de Oliveira  Institutional Review Board (IRB) . Formal written consent was obtained from all participants, all excess samples were cryopreserved at −80 °C. Prior to analyses, samples were de-identified, thawed, blinded, then immediately processed and results called by a second person. This study received an exemption determination from the UFBA IRB.HTLV-I p24 antigen  was conjugated to gold nanoparticles  according to the manufacturer’s instructions. Briefly, the antigen was diluted to 0.2 mg/mL in supplied dilution buffer. Next, 12 μL of diluted antigen was mixed with 42 μL reaction buffer. 45 μL of the mix was then used to suspend the lyophilized gold nanoparticles. The antigen-nanoparticle mix was incubated for 15 min at room temperature, followed by the addition of 5 μL of supplied quencher solution to stop the coupling reaction. After adding the quencher solution, 100 μL of 1% Tween-20  in PBS  and 50 μL of 50% sucrose  in water were added to the gold conjugate prior to use in immunochromatography.Nitrocellulose membrane was cut into strips using a laser cutter  at 30% power and 90% speed. Strips were attached to a wick  with adhesive paper . For the control area, 0.33 μL of anti-HTLV-I p24 antibody  at 2 mg/mL was spotted on the control area. The test area on the nitrocellulose was generated by pipetting 0.33 μL of anti-human IgG at 6 mg/mL. Strips were air dried and stored in a desiccator at room temperature before use.Each immunochromatography strip was run in a separate microcentrifuge tube. The rapid test solution contained 40 μL of serum specimen, 10 μL of fetal calf serum, 5 μL of quencher solution, and 20 μL of conjugate gold nanoparticle mix. The strips were allowed to react with the mixture for 20 min and then dried and results were image captured using a mobile phone device.Chlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, Toxoplasma gondii, and Plasmodium falciparum infections were purchased . 40 μL of each serum specimen was processed according to the immunochromatography section in this manuscript.Serum samples from HTLV-I, HTLV-II, HIV-I, HIV-II, hepatitis A virus, hepatitis B virus, hepatitis C virus, herpes simplex virus, Epstein–Barr virus, SARS-CoV-2, 10 serum specimens were spiked with either sodium heparin , sodium citrate , EDTA , or ACD solution A , and 40 μL of each serum specimen was then processed according to the immunochromatography section in this manuscript.The 10 serum specimens from HTLV-I infections were processed in replicates of 5 according to the immunochromatography section in this manuscript.The rapid screening test results were machine-read by ImageJ (NIH) to quantify the HTLV-I p24 IgG signals. The average pixel intensity was quantified at the control, background, and test areas. The background-adjusted IgG signal was then normalized to the background-subtracted control area and expressed as % of control. Plots of signals were generated using Prism (version 9.0.0).The sensitivity, specificity, and positive and negative predictive values were calculated using the MedCalc Software  and are expressed as percentages. In short, sensitivity (or the true positive rate), is the probability that the rapid screening test for HTLV will be positive when the disease is present. While the specificity (or the true negative rate) is the probability that the rapid screening test will be negative when the disease is not present. Using results from both the MP Diagnostics HTLV Blot 2.4 test as the comparator and the rapid screening test for HTLV, we then calculated the sensitivity as the true positives/ and the specificity as true negatives/. Confidence intervals for sensitivity and specificity are “exact” Clopper-Pearson confidence intervals. Confidence intervals for the predictive values are the standard logit confidence intervals.To evaluate the rapid screening test for HTLV-I, we compared the sensitivity and specificity using serum specimens collected from individuals with or without HTLV-I and/or HTLV-II in Salvador, Bahia, Brazil. A total of 118 serum specimens were included in the analysis. Using the MP Diagnostics HTLV Blot 2.4 test as the comparator test, 55 serum specimens were confirmed HTLV-I positive, 5 were confirmed HTLV-I and HTLV-II positive, and 58 were confirmed negative . Among tThe rapid screening test had a sensitivity of 96.7% (58/60) and a specificity of 100.0% (58/58) , with poChlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, Toxoplasma gondii, and Plasmodium falciparum. The rapid screening test reacted to 10 individual serum specimens from HTLV-I infections, but the test did not react with any other serum specimens from other infections (To determine whether the rapid screening test for HTLV-I cross-reacted with antibodies against potentially interfering pathogens, we tested individual serum specimens from infections caused by HTLV-I, HTLV-II, HIV-I, HIV-II, hepatitis A virus, hepatitis B virus, hepatitis C virus, herpes simplex virus, Epstein–Barr virus, SARS-CoV-2, fections . Next, wfections . The 10 fections .HTLV-I seroprevalence data has largely been based on known endemic regions, with a scarcity of reliable estimates from highly populated countries. The most recent global estimates for the total number of people living with HTLV-I ranges from 5 to 10 million; however, infection counts are likely undercounted . The disUnder current testing strategies, to screen and diagnose HTLV-I infection requires an algorithm involving two to three different laboratory-based assays . Most ofFor this study, we developed a rapid screening test for HTLV-I antibodies. The test uses HTLV-I p24 antigen conjugated to gold nanoparticles and an anti-human IgG antibody immobilized to a nitrocellulose strip to detect human HTLV-I p24-specific IgG antibodies via immunochromatography . The curChlamydia trachomatis, Neisseria gonorrhoeae, Treponema pallidum, Toxoplasma gondii, and Plasmodium falciparum. We then conducted additional cross-reaction studies with potentially interfering substances including sodium heparin, sodium citrate, EDTA, or ACD solution A. Similarly, the test did not show any cross-reaction with serum spiked with the substances. To determine reproducibility, we ran 10 independent serum specimens from HTLV-I infections in replicates of 5. The rapid screening test reacted 100% of the time.To analytically validate the rapid screening test, we conducted cross-reaction studies with potentially interfering infections. The test did not show cross-reaction with potentially interfering infections including those caused by HTLV-II, HIV-I, HIV-II, hepatitis A virus, hepatitis B virus, hepatitis C virus, herpes simplex virus, Epstein–Barr virus, SARS-CoV-2, Next, we performed a clinical evaluation of the rapid screening test in serum specimens collected from individuals in Salvador, Bahia, Brazil. Using the FDA-approved MP Diagnostics HTLV Blot 2.4 test as the comparator test, 55 serum specimens were confirmed HTLV-I positive, 5 were confirmed HTLV-I and HTLV-II positive, and 58 were confirmed negative. When the serum specimens were processed via the rapid screening test, the sensitivity and specificity of the assay was 96.7% and 100%, respectively. The rapid test did not react in serum specimens in two HTLV-I positive individuals; one of the individuals was not HTLV p24 positive by the MP Diagnostics HTLB Blot 2.4 test, however, the other individual did have p24 antibodies. Our analytical and clinical results demonstrate high sensitivity and specificity, potentially enabling lower-cost, more efficient HTLV-I screening.There were several limitations to this study. Future work should broaden the evaluation to additional settings, sample types, and disease states. While we used serum as the primary sample type, a point-of-care test for HTLV should be optimized for use with fingerstick whole blood, which will further simplify the screening process. Our analysis included 36 asymptomatic and 24 symptomatic HTLV-infected individuals. Regardless of disease state, the rapid screening test performed with high sensitivity and specificity. However, testing should be expanded on HTLV-infected individuals with HAM/TSP. There were two HTLV-I positive samples that were negative by the rapid screening test. These samples demonstrated seroconversion by the comparator assay, where one sample had HTLV-I p24 antibodies, but the other did not. Performance testing on prospectively collected samples in larger cohorts will further corroborate preliminary findings; additionally, evaluations should include testing using seroconverter specimens. Finally, development of a rapid screening test for HTLV-II antibodies should be considered. While the rapid screening test did not cross-react with serum specimens from HTLV-II infections in our analytical studies, more robust testing is warranted.Without a vaccine and with limited available treatment options for HTLV-related diseases, it is critical to be able to easily identify those who are infected in both endemic and non-endemic regions. While current screening and diagnostic methods for HTLV infection have been shown to be relatively effective, there are no tests that can be performed outside of a laboratory, challenging widescale surveillance and seroprevalence estimates. Screening of HTLV infection, therefore, requires inexpensive, rapid, and accurate non-laboratory-based technology that can be scaled and performed globally. Immunochromatographic, serological-based tests fit these needs.


Experimental data favor the function of protein S-glutathionylation as a feedback loop for the inhibition of mitochondrial H2O2 production. Major conclusions: The glutathione pool redox state is linked to the availability of H2O2, making glutathionylation an ideal mechanism for preventing oxidative distress whilst playing a part in desensitizing mitochondrial redox signals. General Significance: The biological significance of glutathionylation is rooted in redox status communication. The present review critically evaluates the experimental evidence supporting its role in negating mitochondrial H2O2 production for cell signaling and prevention of electrophilic stress.Background: It has been four decades since protein S-glutathionylation was proposed to serve as a regulator of cell metabolism. Since then, this redox-sensitive covalent modification has been identified as a cell-wide signaling platform required for embryonic development and regulation of many physiological functions. Scope of the Review: Mitochondria use hydrogen peroxide (H This me2O2 generating capacity 2O2 avail UQ pool B. These  disease . Second, disease ,104. Fur disease ,42. Reve disease ,41. Simidistress . Taken t2O2 production in brain, muscle, and liver mitochondria [2O2 homeostasis, both enzymes were labeled “redox sensors” that control metabolism in response to changes in mitochondrial redox buffering capacity [2O2 production by these α-ketoacid dehydrogenases. Glutathionylation of the E2 lipoamide thiols in response to glutathione pool oxidation decelerates NADH and H2O2 production by both KGDH and PDH [2O2 genesis. The high H2O2-generating capacity of both α-ketoacid dehydrogenases has been suggested to fulfill some cell signaling functions. It has been suggested that both enzymes serve as redox sensors that may operate as platforms for H2O2-mediated cell communication [+, forming NADPH for antioxidant defenses like glutathione [2O2 production by PDH, which is quenched by NADPH-dependent antioxidants. This dissipates the Δp for NADPH production by NNT, increasing mitochondrial fuel combustion and respiration. This mechanism, however, was challenged because of valid concerns related to the stoichiometry for proton return and NADPH production by NNT [+ to be returned per NADPH produced by NNT [2O2 and NADPH availability, molecules that dependent on mitochondrial bioenergetics for production. PDH and KGDH are sources for H2O2, but also generate the NADH required to fuel NADPH production by NNT. The H2O2 is metabolized by the GSH pool, resulting in GSSG production and the propagation of signals through glutathionylation. Glutathione pool oxidation also drives the feedback inhibition of H2O2 genesis by KGDH and PDH (and other sources such as complex I, desensitizing the redox signal. By way of extension, NADPH formed by NNT activity restores the reducing capacity of the GSH pool driving protein deglutathionylation. The signaling function of KGDH and PDH could also be extracted from cancer studies. Indeed, targeted inhibition of both enzymes can interfere with cancer progression and cell division [2O2 genesis, which is a vital second messenger for proliferation and adaptive signaling in cancer cells.KGDH and PDH have high rates for Hchondria ,106,107.chondria . KGDH ancapacity ,110. Thrcapacity ,112,113. and PDH ,30. Thisnication . This sinication . Two stunication ,116. NNTtathione . The PDHn by NNT . The PDHd by NNT . As poind by NNT , it was d by NNT . Thus, adivision . This co2O2 signaling can also be extracted from a recent publication regarding its effect on DHODH [2O2 [2O2 signaling [2O2 production [2O2 production [2O2 induced proliferation signals.Insight into the potential regulatory role of glutathionylation in Hon DHODH . Mitochoon DHODH . OxidatiODH [2O2 . DHODH iignaling . Thus, toduction . Of inteoduction . Indeed,oduction . Indeed,oduction  which waoduction . The taroduction . DeactivThe cell redoxome is composed of diverse and reversible oxido-reduction reactions that occur on proteinaceous cysteines. It serves as a vital interface for changing cell responses to environmental cues. These cues include toxins, nutritional changes, exercise, acquired or inherited disorders and many other factors. Glutathionylation is a critical part of the redoxome and can affect many cell programs. Dysfunction in reversible glutathionylation due to the disabling of glutathione S-transferases and glutaredoxins, or prolonged oxidation of glutathione pools is associated with several pathologies and disruption of embryonic development.2O2 production. Most of the findings so far have demonstrated that glutathionylation is vital for inhibiting H2O2 genesis by mitochondria through the deactivation of sources such as complex I, KGDH, and PDH. The effect is to limit H2O2 formation for the reactivation of NADPH-dependent antioxidant defenses, thereby preventing oxidative distress. Further, the inhibition must be short-lived since glutathione pool reduction is rapid, resulting in the induction of deglutathionylation and reactivation of electron-transferring nutrient oxidation pathways required for energy transduction.Here, we have provided a historical perspective on cell and mitochondrial glutathionylation reactions, which we coupled to emerging evidence demonstrating that it is integral for decelerating H2O2 production for extended periods. In this case, we used empirical evidence demonstrating that glutathionylation of complex I subunit NDUFS1 correlates with mitochondrial dysfunction and oxidative distress in several pathologies. Recent findings have shown that this is related to the acceleration of complex I-mediated H2O2 genesis due to RET following the oxidation and metabolism of UQ linked substrates such as succinate, proline, or glycerol-3-phosphate. A similar effect may occur upstream from the UQ pool where blocking electron flow or mitochondrial proton return to the matrix increases ROS production by complex III.Coupled with this, we presented the most recent evidence showing that disruption of this pathway can have pathological consequences. On one side, prolonged glutathione pool oxidation due to oxidative distress may lead to the unwanted maintenance of mitochondrial energy transducing enzymes in an inactive state. This can lead to the inhibition of oxidative phosphorylation pathways and metabolic inflexibility, but also contribute to oxidative distress through the augmentation of H2O2 signals. This mechanism is highly effective given the rapid kinetics for glutathionylation/deglutathionylation reactions and their dependence on mitochondrial bioenergetics. These characteristics make glutathionylation ideal for quelling complex I signals and redox communication emanating from other H2O2 forming dehydrogenases. However, use of glutathionylation/deglutathionylation to modulate H2O2 signals demands that the addition and removal of GSH to and from proteins is efficient so as to avoid prolonged disruptions in mitochondrial energy transduction pathways.Hydrogen peroxide signals are mediated through disulfide relay reactions, either occurring through protein disulfide exchange reactions catalyzed by thioredoxins or glutathionylation. Here, we have presented evidence that glutathionylation is also a feedback loop to desensitize mitochondrial H2O2 and NADPH availability, products of mitochondrial respiration and nutrient metabolism. The elimination of H2O2 by glutathione and the reduction of GSSG in an NADPH-specific manner occurs rapidly, which is equally matched by the fast addition and removal of glutathionyl moieties to and from proteins by GRXs and GSTs. Here, we have discussed the role of glutathionylation in the prevention of oxidative distress in a historical context. This included elaborating its function in protecting thiols from irreversible oxidation and limiting H2O2 availability. We also discussed its importance in serving as a H2O2 signal amplifier for the regulation of many cell functions in the contexts of physiology and development and how disrupting protein glutathionyl moiety addition and removal reactions results in pathologies and aging. Coupled with this and using novel studies emerging from several groups, including our own, we have illuminated its other vital function: desensitizing H2O2 signals. This occurs with mitochondrial proteins such as KGDH, PDH, complex I, and, most recently identified, DHODH. Notably, the desensitization of H2O2 signals may be a cell-wide function of glutathionylation as well given that recent evidence also demonstrated that it deactivates ROS production by xanthine oxidoreductase in rat liver cytoplasm in a GST-dependent manner [The glutathionylation and deglutathionylation of cell proteins is linked directly to the oxidation and reduction of the glutathione pool. Cell glutathione availability, in turn, is directly influenced by Ht manner . Taken t