Deciphering the Dilemma: Choosing the Optimal Total Neoadjuvant Treatment Strategy for Locally Advanced Rectal Cancer.

Rectal cancer management has evolved significantly, particularly with neoadjuvant treatment strategies. This narrative review examines the development and effectiveness of these therapies for locally advanced rectal cancer (LARC), highlighting the historical quest that led to current neoadjuvant alternatives. Initially, trials showed the benefits of adding radiotherapy (RT) and chemotherapy (CT) to surgery, reducing local recurrence (LR). The addition of oxaliplatin to chemoradiotherapy (CRT) further improved outcomes. TNT integrates chemotherapy and radiotherapy preoperatively to enhance adherence, timing, and systemic control. Key trials, including PRODIGE 23, CAO/ARO/AIO 12, OPRA, RAPIDO, and STELLAR, are analyzed to compare short-course and long-course RT with systemic chemotherapy. The heterogeneity and difficulty in comparing TNT trials due to different designs and outcomes are acknowledged, along with their promising long-term results. On the other hand, it briefly discusses the potential for non-operative management (NOM) in select patients, a strategy gaining traction due to favorable outcomes in specific trials. As a conclusion, this review underscores the complexity of rectal cancer treatment, emphasizing individualized approaches considering patient preferences and healthcare resources. It also highlights the importance of interpreting impressive positive or negative results with caution due to the variability in study designs and patient populations.

EFMouse: a Matlab toolbox to model electric fields in the mouse brain.

Compared to the rapidly growing literature on transcranial electrical stimulation (tES) in humans, research into the mechanisms underlying neuromodulation by tES using in-vivo animal models is growing but still relatively rare. Such research, however, is key to overcoming experimental limitations in humans and essential to build a detailed understanding of the in-vivo consequences of tES that can ultimately lead to development of targeted and effective therapeutic applications of noninvasive brain stimulation. The sheer difference in scale and geometry between animal models and the human brain contributes to the complexity of designing and interpreting animal studies. Here we extend previous approaches to model intracranial electric fields to generate predictions that can be tested with in-vivo intracranial recordings. Although the toolbox has general applicability and could be used to predict intracranial fields for any tES study using mice, we illustrate its usage by comparing fields in a high-density multi-electrode montage with a more traditional two electrode montage. Our simulations show that both montages can produce strong focal homogeneous electric fields in targeted areas. However, the high-density montage produces a field that is more perpendicular to the visual cortical surface, which is expected to result in larger changes in neuronal excitability.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities.

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities.

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

[Myocardiopericarditis in adult still's disease]. / Miocardiopericarditis en la enfermedad de still del adulto.

Cardiac involvement in Adult-onset Still's Disease (AOSD) usually manifests as a pericardial disease, that has a benign course. The myocarditis is a rare complication with 7% of prevalence. The diagnosis of AOSD is based in the Yamaguchi or Fautrel criteria. The treatment with steroids and methotrexate is the first and second therapeutic lines, respectively, the combination is effective in 70% of cases. We report a case of AOSD with unusual presentation due to myocardial and pericardial commitment.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists' preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables-for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.

Determination of permeability data and 3-D modelling of the host rock and sinters from a geothermal field: Los Geysers, northern Trans-Mexican Volcanic Field.

This data article describes the connected pore cluster data from segmented nano-images of rocks related to a geothermal system. The collected samples include two (2) vesicle-amygdaloidal basalt (host rock) and four (4) horizons collected from a siliceous sinter mound (rock precipitated from hot waters). All the samples have undergone computed tomography scanning using a SkyScan 2211 multiscale X-ray nano-CT system (Bruker®), and the slices were analyzed using the Digital Rock Physics (DRP) approach. Pore volume and fluid permeability in the three directions were calculated with scripts of Python (v.3.9) and the visualizations of the 3D models were run with Paraview (v.5.10) software. The petrophysical properties, diagrams, and figures were produced by stacking the 2D projections (8-bit grayscale *.png images format) from the scanning. Raw data (images) were deposited in a repository, which has granted a persistent identifier (Mendeley Data: https://data.mendeley.com/datasets/srpxhpd37p/2). This article provides a study case to handle the data that test the interconnectivity and ability to transport fluids and/or exogenous matter carried during high-flow events in rocks outcropping at the surface level of a geothermal system.

Protocol for activity flow mapping of neurocognitive computations using the Brain Activity Flow Toolbox.

Traditional cognitive neuroscience uses task-evoked activations to map neurocognitive processes (and information) to brain regions; however, how those processes are generated is unknown. We developed activity flow mapping to identify and empirically validate network mechanisms underlying the generation of neurocognitive processes. This approach models the movement of task-evoked activity over brain connections to predict task-evoked activations. We present a protocol for using the Brain Activity Flow Toolbox (https://colelab.github.io/ActflowToolbox/) to identify network mechanisms underlying neurocognitive processes of interest. For complete details on the use and execution of this protocol, please refer to Cole et al., 2021.

Short-term effects of caffeine intake on binocular accommodative facility: a quantitative and qualitative analysis.

CLINICAL RELEVANCE: Caffeine intake has been demonstrated to influence several physiological measures, including some related to eye physiology. The ability to focus at different distances is of paramount importance in real-world situations, and thus, the possible impact of caffeine intake on accommodative facility may have important clinical implications. BACKGROUND: This placebo-controlled, double-blind, balanced crossover study aimed to assess the acute effects of caffeine ingestion on the frequency and precision of the binocular accommodative facility. METHODS: Twenty university students (21.9 ± 3.4 years) ingested a capsule of caffeine (4 mg/kg) or placebo (300 mg of corn-starch) on two different days and counterbalanced order. The binocular accommodative facility was objectively assessed, using the WAM-5500 binocular open-field autorefractometer, after 60 min of capsule ingestion (caffeine/placebo). Perceived levels of activation was also assessed in each experimental condition. RESULTS: The ingestion of a single administration of caffeine (~ 4 mg/kg) causes an increase in the number of cycles performed per minute (p = 0.023, Cohen's d = 0.55), whereas no effects were observed for the mean magnitude of accommodative change between the far and near targets (p = 0.794), and the percentage of incorrect cycles of accommodation and dis-accommodation (p = 0.271 and 0.396, respectively). Participants reported a perceived level of activation of 6.8 ± 1.5 and 7.6 ± 1.8 in the placebo and caffeine conditions, respectively (p = 0.059). CONCLUSION: Caffeine intake improves quantitative, but not qualitative, measures of accommodative facility. These results corroborate the impact of caffeine on visual function and suggest that this ergogenic effect of caffeine may be used to enhance visual performance in applied situations.

The Functional Relevance of Task-State Functional Connectivity.

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping-an approach for building empirically derived network models-to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.SIGNIFICANCE STATEMENT Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

Combining Multiple Functional Connectivity Methods to Improve Causal Inferences.

Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet, approaches that characterize relationships among neural time series-functional connectivity (FC) methods-are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives because they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (with functional MRI) and coherence (with electrophysiological methods) may be their simplicity. Thus, we sought to identify an FC method that was both simple and improved causal inferences relative to the most popular methods. We started with partial correlation, showing with neural network simulations that this substantially improves causal inferences relative to bivariate correlation. However, the presence of colliders (common effects) in a network resulted in false positives with partial correlation, although this was not a problem for bivariate correlations. This led us to propose a new combined FC method (combinedFC) that incorporates simple bivariate and partial correlation FC measures to make more valid causal inferences than either alone. We release a toolbox for implementing this new combinedFC method to facilitate improvement of FC-based causal inferences. CombinedFC is a general method for FC and can be applied equally to resting-state and task-based paradigms.

Acute Effects of Caffeine on Dynamic Accommodative Response and Pupil Size: A Placebo-controlled, Double-blind, Balanced Crossover Study.

OBJECTIVES: To evaluate the acute effect of caffeine consumption on the accuracy and variability of accommodation, as well as its impact on pupil size and perceived levels of activation. METHODS: 22 university students (21.68 ± 3.67 years old) ingested a capsule of caffeine (4 mg/kg) or placebo (300 mg of corn-starch) in two different days and counterbalanced order. After 30 min of capsule ingestion, we objectively measured the accuracy and variability of accommodation, and pupil size using the WAM-5500 binocular open-field autorefractometer for 2 min at each of the six viewing distances (5 m, 50 cm, 40 cm, 33 cm, 25 cm, and 20 cm). Subjective levels of activation to check the effectiveness of caffeine/placebo manipulation were also reported. RESULTS: We found that after 30 min of caffeine/placebo ingestion, participant perceived higher levels of activation in the caffeine condition (p = .047, Cohen´s d = 0.48). Caffeine consumption induced a statistically significant dilator effect on pupil size (p = .011, η2 = 0.271), and reduced variability of accommodative response (p = .027, η2 = 0.211). However, no differences were obtained for the accuracy of accommodation (p = .321). CONCLUSIONS: Our data suggest that caffeine consumption reduced the variability of accommodative response and induced pupil dilation. Nevertheless, the accuracy of accommodation was insensitive to caffeine intake. These findings may be explained by the bidirectional relationship between ocular functioning and the nervous system´s state of activation.

Advancing functional connectivity research from association to causation.

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods.

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images.

We describe two modifications that parallelize and reorganize caching in the well-known Greedy Equivalence Search (GES) algorithm for discovering directed acyclic graphs on random variables from sample values. We apply one of these modifications, the Fast Greedy Search (FGS) assuming faithfulness, to an i.i.d. sample of 1,000 units to recover with high precision and good recall an average degree 2 directed acyclic graph (DAG) with one million Gaussian variables. We describe a modification of the algorithm to rapidly find the Markov Blanket of any variable in a high dimensional system. Using 51,000 voxels that parcellate an entire human cortex, we apply the FGS algorithm to Blood Oxygenation Level Dependent (BOLD) time series obtained from resting state fMRI.

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.

We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose qualitative structure is known, we aim to extract from data a low-dimensional and interpretable representation of the main components of the changes. For this purpose we develop a novel kernel embedding of nonstationary conditional distributions that does not rely on sliding windows. Second, the embedding also leads to a measure of dependence between the changes of causal modules that can be used to determine the directions of many causal arrows. We demonstrate the power of our methods with experiments on both synthetic and real data.

Study of tamoxifen urinary metabolites in rat by ultra-high-performance liquid chromatography time-of-flight mass spectrometry.

Tamoxifen (TMX) is a nonsteroidal estrogen antagonist drug used for the treatment of breast cancer. It is also included in the list of banned substances of the World Anti Doping Agency (WADA) prohibited in and out of competition. In this work, the excretion of urinary metabolites of TMX after a single therapeutic dose administration in rats has been studied using ultra-high-performance liquid chromatography electrospray time-of-flight mass spectrometry (UHPLC-TOFMS). A systematic strategy based on the search of typical biotransformations that a xenobiotic can undergo in living organisms, based on their corresponding molecular formula modification and accurate mass shifts, was applied for the identification of TMX metabolites. Prior to UHPLC-TOFMS analyses, a solid-phase extraction step with polymeric cartridges was applied to urine samples. Up to 38 TMX metabolites were detected. Additional collision induced dissociation (CID) MS/MS fragmentation was performed using UHPLC-QTOFMS. Compared with recent previous studies in human urine and plasma, new metabolites have been reported for the first time in urine. Metabolites identified in rat urine include the oxygen addition, owing to different possibilities for the hydroxylation of the rings in different positions (m/z 388.2271), the incorporation of two oxygen atoms (m/z 404.2220) (including dihydroxylated derivatives or alternatives such as epoxidation plus hydroxylation or N-oxidation and hydroxylation), epoxide formation or hydroxylation and dehydrogenation [m/z 386.2114 (+O -H2 )], hydroxylation of the ring accompanied by N-desmethylation (m/z 374.2115), combined hydroxylation and methoxylation (m/z 418.2377), desaturated TMX derivate (m/z 370.2165) and its N-desmethylated derivate (m/z 356.2009), the two latter modifications not previously being reported in urine. These findings confirm the usefulness of the proposed approach based on UHPLC-TOFMS.

Cardiotrophin-1 decreases liver apoptosis through calpastatin induction.

BACKGROUND: Cardiotrophin-1 (CT1) has been used to prevent cell death in different models of liver injury in rats. D-galactosamine induces cell death in culture rat and human hepatocytes. The present study evaluated the cytoprotective effects of CT1 in an experimental model of apoptosis induced by D-galactosamine in hepatocytes. METHODS: DNA fragmentation, calpain activity and Western blots of caspase-3, calpastatin and Stat3, and Akt phosphorylation were measured. Stat3 and Akt inhibitors were used to analyze the mechanisms of action of CT1. RESULTS: CT1 caused an increase in Stat3 and Akt phosphorylation and a decrease of DNA fragmentation, calpain activity, and caspase-3 induced by D-galactosamine. The reduction of calpain activity by CT1 was associated with an increase of calpastatin (its endogenous inhibitor). The effects of CT1 were also dependent on the activation of Sta3 or Akt. CONCLUSIONS: CT1 decreases cell death through a mechanism related to Stat3 and Akt phosphorylation and activation of calpastatin in D-galactosamine-treated hepatocytes.