Immunogenicity of standard and extended dosing intervals of BNT162b2 mRNA vaccine.

Extension of the interval between vaccine doses for the BNT162b2 mRNA vaccine was introduced in the United Kingdom to accelerate population coverage with a single dose. At this time, trial data were lacking, and we addressed this in a study of United Kingdom healthcare workers. The first vaccine dose induced protection from infection from the circulating alpha (B.1.1.7) variant over several weeks. In a substudy of 589 individuals, we show that this single dose induces severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibody (NAb) responses and a sustained B and T cell response to the spike protein. NAb levels were higher after the extended dosing interval (6-14 weeks) compared with the conventional 3- to 4-week regimen, accompanied by enrichment of CD4+ T cells expressing interleukin-2 (IL-2). Prior SARS-CoV-2 infection amplified and accelerated the response. These data on dynamic cellular and humoral responses indicate that extension of the dosing interval is an effective immunogenic protocol.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time.

Interval timing, which operates on timescales of seconds to minutes, is distributed across multiple brain regions and may use distinct circuit mechanisms as compared to millisecond timing and circadian rhythms. However, its study has proven difficult, as timing on this scale is deeply entangled with other behaviors. Several circuit and cellular mechanisms could generate sequential or ramping activity patterns that carry timing information. Here we propose that a productive approach is to draw parallels between interval timing and spatial navigation, where direct analogies can be made between the variables of interest and the mathematical operations necessitated. Along with designing experiments that isolate or disambiguate timing behavior from other variables, new techniques will facilitate studies that directly address the neural mechanisms that are responsible for interval timing.

Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform.

In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from the rodent medial prefrontal cortex [J. Henke et al., eLife10, e71612 (2021)] during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.

Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2.

Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection-for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3.2 d vs. 4.4 d) and serial interval (3.5 d vs. 4.1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8 to 4.5 d) for both variants but a shorter mean generation interval for the Omicron variant (3.0 d; 95% CI: 2.7 to 3.2 d) than for the Delta variant (3.8 d; 95% CI: 3.7 to 4.0 d). The differences in estimated generation intervals may be driven by the "network effect"-higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant.

Spatially explicit effective reproduction numbers from incidence and mobility data.

Current methods for near real-time estimation of effective reproduction numbers from surveillance data overlook mobility fluxes of infectors and susceptible individuals within a spatially connected network (the metapopulation). Exchanges of infections among different communities may thus be misrepresented unless explicitly measured and accounted for in the renewal equations. Here, we first derive the equations that include spatially explicit effective reproduction numbers, ℛk(t), in an arbitrary community k. These equations embed a suitable connection matrix blending mobility among connected communities and mobility-related containment measures. Then, we propose a tool to estimate, in a Bayesian framework involving particle filtering, the values of ℛk(t) maximizing a suitable likelihood function reproducing observed patterns of infections in space and time. We validate our tools against synthetic data and apply them to real COVID-19 epidemiological records in a severely affected and carefully monitored Italian region. Differences arising between connected and disconnected reproduction numbers (the latter being calculated with existing methods, to which our formulation reduces by setting mobility to zero) suggest that current standards may be improved in their estimation of disease transmission over time.

High-intensity interval training improves hypothalamic inflammation by suppressing HIF-1α signaling in microglia of male C57BL/6J mice.

Repeated bouts of high-intensity interval training (HIIT) induce an improvement in metabolism via plasticity of melanocortin circuits and attenuated hypothalamic inflammation. HIF-1α, which plays a vital role in hypothalamus-mediated regulation of peripheral metabolism, is enhanced in the hypothalamus by HIIT. This study aimed to investigate the effects of HIIT on hypothalamic HIF-1α expression and peripheral metabolism in obese mice and the underlying molecular mechanisms. By using a high-fat diet (HFD)-induced obesity mouse model, we determined the effect of HIIT on energy balance and the expression of the hypothalamic appetite-regulating neuropeptides, POMC and NPY. Moreover, hypothalamic HIF-1α signaling and its downstream glycolytic enzymes were explored after HIIT intervention. The state of microglia and microglial NF-κB signaling in the hypothalamus were also examined in vivo. In vitro by using an adenovirus carrying shRNA-HIF1ß, we explored the impact of HIF-1 signaling on glycolysis and NF-κB inflammatory signaling in BV2 cells. Food intake was suppressed and whole-body metabolism was improved in exercised DIO mice, accompanied by changes in the expression of POMC and NPY. Moreover, total and microglial HIF-1α signaling were obviously attenuated in the hypothalamus, consistent with the decreased levels of glycolytic enzymes. Both HFD-induced microglial activation and hypothalamic NF-κB signaling were significantly suppressed following HIIT in vivo. In BV2 cells, after HIF-1 complex knockdown, glycolysis and NF-κB inflammatory signaling were significantly attenuated. The data indicate that HIIT improves peripheral metabolism probably via attenuated HFD-induced microglial activation and microglial NF-κB signaling in the hypothalamus, which could be mediated by suppressed microglial HIF-1α signaling.

Beta rebound reduces subsequent movement preparation time by modulating of GABAA inhibition.

Post-movement beta synchronization is an increase of beta power relative to baseline, which commonly used to represent the status quo of the motor system. However, its functional role to the subsequent voluntary motor output and potential electrophysiological significance remain largely unknown. Here, we examined the reaction time of a Go/No-Go task of index finger tapping which performed at the phases of power baseline and post-movement beta synchronization peak induced by index finger abduction movements at different speeds (ballistic/self-paced) in 13 healthy subjects. We found a correlation between the post-movement beta synchronization and reaction time that larger post-movement beta synchronization prolonged the reaction time during Go trials. To probe the electrophysiological significance of post-movement beta synchronization, we assessed intracortical inhibitory measures probably involving GABAB (long-interval intracortical inhibition) and GABAA (short-interval intracortical inhibition) receptors in beta baseline and post-movement beta synchronization peak induced by index finger abduction movements at different speeds. We found that short-interval intracortical inhibition but not long-interval intracortical inhibition increased in post-movement beta synchronization peak compared with that in the power baseline, and was negatively correlated with the change of post-movement beta synchronization peak value. These novel findings indicate that the post-movement beta synchronization is related to forward model updating, with high beta rebound predicting longer time for the preparation of subsequent movement by inhibitory neural pathways of GABAA.

Impact of Using Pre- and Postbronchodilator Spirometry Reference Values in a Chinese Population.

Rationale: Spirometry reference equations that are derived from a large, nationally representative general population are warranted in China, and the impact of using prebronchodilator (pre-BD) and post-BD spirometry reference values has yet to be assessed in Chinese populations. Objectives: To present the pre-BD and post-BD spirometry reference values for Chinese adults using the China Pulmonary Health (CPH) Study. Methods: A reference population of 17,969 healthy, nonsmoking participants in the CPH Study was used to calculate the pre- and post-BD reference values for FEV1, FVC, and FEV1/FVC ratio. Pre- and post-BD reference values were applied to the entire CPH population (N = 50,991) to illustrate the divergence between the use of different references in determining disease prevalence and severity grading. Measurements and Main Results: The prevalences of airflow limitation were 5.36% using the pre-BD reference and 8.02% using the post-BD reference. Individuals who had a post-BD FEV1/FVC ratio lower than the post-BD reference value but higher than the pre-BD reference value were found to have significantly higher rates of self-reported respiratory symptoms and significantly lower values on spirometry indicators than those whose post-BD FEV1/FVC ratio was greater than the post-BD reference value. An additional 3.51% of participants were identified as having grade II-IV chronic obstructive pulmonary disease using the post-BD FEV1 predicted values. Conclusions: This study generated and applied pre- and post-BD spirometry reference values in a nationally representative Chinese adult population. Post-BD reference values may serve as an additional criterion in identifying individuals at risk for obstructive pulmonary diseases, and their diagnostic and prognostic values should be further investigated.

Comparing methods for statistical inference with model uncertainty.

Probability models are used for many statistical tasks, notably parameter estimation, interval estimation, inference about model parameters, point prediction, and interval prediction. Thus, choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process. Here we focus on one such choice, that of variables to include in a linear regression model. Many methods have been proposed, including Bayesian and penalized likelihood methods, and it is unclear which one to use. We compared 21 of the most popular methods by carrying out an extensive set of simulation studies based closely on real datasets that span a range of situations encountered in practical data analysis. Three adaptive Bayesian model averaging (BMA) methods performed best across all statistical tasks. These used adaptive versions of Zellner's g-prior for the parameters, where the prior variance parameter g is a function of sample size or is estimated from the data. We found that for BMA methods implemented with Markov chain Monte Carlo, 10,000 iterations were enough. Computationally, we found two of the three best methods (BMA with g=√n and empirical Bayes-local) to be competitive with the least absolute shrinkage and selection operator (LASSO), which is often preferred as a variable selection technique because of its computational efficiency. BMA performed better than Bayesian model selection (in which just one model is selected).

Time Scales of Human Mpox Transmission in The Netherlands.

Mpox has spread rapidly to many countries in nonendemic regions. After reviewing detailed exposure histories of 109 pairs of mpox cases in the Netherlands, we identified 34 pairs where transmission was likely and the infectee reported a single potential infector with a mean serial interval of 10.1 days (95% credible interval, 6.6-14.7 days). Further investigation into pairs from 1 regional public health service revealed that presymptomatic transmission may have occurred in 5 of 18 pairs. These findings emphasize that precaution remains key, regardless of the presence of recognizable symptoms of mpox.

From flesh to bones: Multi-omics approaches in forensic science.

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Effects of Hallucination Proneness and Sensory Resolution on Prior Biases in Human Perceptual Inference of Time Intervals.

Bayesian models of perception posit that percepts result from the optimal integration of new sensory information and prior expectations. In turn, prominent models of perceptual disturbances in psychosis frame hallucination-like phenomena as percepts excessively biased toward perceptual prior expectations. Despite mounting support for this notion, whether this hallucination-related prior bias results secondarily from imprecise sensory representations at early processing stages or directly from alterations in perceptual priors-both suggested candidates potentially consistent with Bayesian models-remains to be tested. Using modified interval timing paradigms designed to arbitrate between these alternative hypotheses, we show in human participants (16 females and 24 males) from a nonclinical population that hallucination proneness correlates with a circumscribed form of prior bias that reflects selective differences in weighting of contextual prior variance, a prior bias that is unrelated to the effect of sensory noise and to a separate index of sensory resolution. Our results thus suggest distinct mechanisms underlying prior biases in perceptual inference and favor the notion that hallucination proneness could reflect direct alterations in the representation or use of perceptual priors independent of sensory noise.SIGNIFICANCE STATEMENT Current theories of psychosis posit that hallucination proneness results from excessive influence of prior expectations on perception. It is not clear whether this prior bias represents a primary top-down process related to the representation or use of prior beliefs or instead a secondary bottom-up process stemming from imprecise sensory representations at early processing stages. To address this question, we examined interval timing behaviors captured by Bayesian perceptual-inference models. Our data support the notion that excessive influence of prior expectations associated with hallucination propensity is not directly secondary to sensory imprecision and is instead more consistent with a primary top-down process. These results help refine computational theories of psychosis and may contribute to the development of improved intervention targets.

A novel approach to the analysis of Overall Survival (OS) as response with Progression-Free Interval (PFI) as condition based on the RNA-seq expression data in The Cancer Genome Atlas (TCGA).

BACKGROUND: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. RESULTS: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. CONCLUSIONS: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

Identifying vital nodes for yeast network by dynamic network entropy.

BACKGROUND: The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast. RESULTS: Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor. CONCLUSIONS: It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

Transcriptomic signatures of human single skeletal muscle fibers in response to high-intensity interval exercise.

The heterogeneous fiber type composition of skeletal muscle makes it challenging to decipher the molecular signaling events driving the health- and performance benefits of exercise. We developed an optimized workflow for transcriptional profiling of individual human muscle fibers before, immediately after, and after three hours of recovery from high-intensity interval cycling exercise. From a transcriptional point-of-view, we observe that there is no dichotomy in fiber activation, that could refer to a fiber being recruited or non-recruited. Rather, the activation pattern displays a continuum with a more uniform response within fast versus slow fibers during the recovery from exercise. The transcriptome-wide response immediately after exercise is characterized by some distinct signatures for slow versus fast fibers, although the most exercise-responsive genes are common between the two fiber types. The temporal transcriptional waves further converge the gene signatures of both fiber types towards a more similar profile during the recovery from exercise. Furthermore, a large heterogeneity among all resting and exercised fibers was observed, with the principal drivers being independent of a slow/fast typology. This profound heterogeneity extends to distinct exercise responses of fibers beyond a classification based on myosin heavy chains. Collectively, our single-fiber methodological approach points to a substantial between-fiber diversity in muscle fiber responses to high-intensity interval exercise.

Molecular regulators of exercise-mediated insulin sensitivity in non-obese individuals.

Insulin resistance is a significant contributor to the development of type 2 diabetes (T2D) and is associated with obesity, physical inactivity, and low maximal oxygen uptake. While intense and prolonged exercise may have negative effects, physical activity can have a positive influence on cellular metabolism and the immune system. Moderate exercise has been shown to reduce oxidative stress and improve antioxidant status, whereas intense exercise can increase oxidative stress in the short term. The impact of exercise on pro-inflammatory cytokine production is complex and varies depending on intensity and duration. Exercise can also counteract the harmful effects of ageing and inflamm-ageing. This review aims to examine the molecular pathways altered by exercise in non-obese individuals at higher risk of developing T2D, including glucose utilization, lipid metabolism, mitochondrial function, inflammation and oxidative stress, with the potential to improve insulin sensitivity. The focus is on understanding the potential benefits of exercise for improving insulin sensitivity and providing insights for future targeted interventions before onset of disease.

Interleukin-15 and high-intensity exercise: relationship with inflammation, body composition and fitness in cancer survivors.

Pre-clinical murine and in vitro models have demonstrated that exercise suppresses tumour and cancer cell growth. These anti-oncogenic effects of exercise were associated with the exercise-mediated release of myokines such as interleukin (IL)-15. However, no study has quantified the acute IL-15 response in human cancer survivors, and whether physiological adaptations to exercise training (i.e. body composition and cardiorespiratory fitness) influence this response. In the present study breast, prostate and colorectal cancer survivors (n = 14) completed a single bout of high-intensity interval exercise (HIIE) [4×4 min at 85-95% heart rate (HR) peak, 3 min at 50-70% HR peak] before and after 7 months of three times weekly high-intensity interval training (HIIT) on a cycle ergometer. At each time point venous blood was sampled before and immediately after HIIE to assess the acute myokine (IL-15, IL-6, IL-10, IL-1ra) responses. Markers of inflammation, cardiorespiratory fitness and measures of body composition were obtained at baseline and 7 months. An acute bout of HIIE resulted in a significant increase in IL-15 concentrations (pre-intervention: 113%; P = 0.013, post-intervention: 102%; P = 0.005). Post-exercise IL-15 concentrations were associated with all other post-exercise myokine concentrations, lean mass (P = 0.031), visceral adipose tissue (P = 0.039) and absolute V ̇ O 2 ${{\dot{V}}_{{{{\mathrm{O}}}_{\mathrm{2}}}}}$ peak (P = 0.032). There was no significant effect of 7 months of HIIT on pre- or post-HIIE IL-15 concentrations (P > 0.05). This study demonstrates HIIE is a sufficient stimulus to increase circulating IL-15 and other myokines including IL-6, IL-10 and IL-1ra which may be clinically relevant in the anti-oncogenic effect of exercise and repetitive exposure to these effects may contribute to the positive relationship between exercise and cancer recurrence. KEY POINTS: Exercise has been demonstrated to reduce the risk of cancer recurrence. Pre-clinical murine and in vitro models have demonstrated that exercise suppresses tumour and cancer cell growth, mediated by exercise-induced myokines (IL-6 and IL-15). High-intensity interval exercise significantly increased myokines associated with the anti-oncogenic effect of exercise and the magnitude of response was associated with lean mass, but training did not appear to influence this response. Given IL-15 has been implicated in the anti-oncogenic effect of exercise and is being explored as an immunotherapy agent, high-intensity interval exercise may improve outcomes for people living beyond cancer through IL-15-mediated pathways. Interventions that increase lean mass may also enhance this response.

Monocarboxylate transporter 4 deficiency enhances high-intensity interval training-induced metabolic adaptations in skeletal muscle.

High-intensity exercise stimulates glycolysis, subsequently leading to elevated lactate production within skeletal muscle. While lactate produced within the muscle is predominantly released into the circulation via the monocarboxylate transporter 4 (MCT4), recent research underscores lactate's function as an intercellular and intertissue signalling molecule. However, its specific intracellular roles within muscle cells remains less defined. In this study, our objective was to elucidate the effects of increased intramuscular lactate accumulation on skeletal muscle adaptation to training. To achieve this, we developed MCT4 knockout mice and confirmed that a lack of MCT4 indeed results in pronounced lactate accumulation in skeletal muscle during high-intensity exercise. A key finding was the significant enhancement in endurance exercise capacity at high intensities when MCT4 deficiency was paired with high-intensity interval training (HIIT). Furthermore, metabolic adaptations supportive of this enhanced exercise capacity were evident with the combination of MCT4 deficiency and HIIT. Specifically, we observed a substantial uptick in the activity of glycolytic enzymes, notably hexokinase, glycogen phosphorylase and pyruvate kinase. The mitochondria also exhibited heightened pyruvate oxidation capabilities, as evidenced by an increase in oxygen consumption when pyruvate served as the substrate. This mitochondrial adaptation was further substantiated by elevated pyruvate dehydrogenase activity, increased activity of isocitrate dehydrogenase - the rate-limiting enzyme in the TCA cycle - and enhanced function of cytochrome c oxidase, pivotal to the electron transport chain. Our findings provide new insights into the physiological consequences of lactate accumulation in skeletal muscle during high-intensity exercises, deepening our grasp of the molecular intricacies underpinning exercise adaptation. KEY POINTS: We pioneered a unique line of monocarboxylate transporter 4 (MCT4) knockout mice specifically tailored to the ICR strain, an optimal background for high-intensity exercise studies. A deficiency in MCT4 exacerbates the accumulation of lactate in skeletal muscle during high-intensity exercise. Pairing MCT4 deficiency with high-intensity interval training (HIIT) results in a synergistic boost in high-intensity exercise capacity, observable both at the organismal level (via a treadmill running test) and at the muscle tissue level (through an ex vivo muscle contractile function test). Coordinating MCT4 deficiency with HIIT enhances both the glycolytic enzyme activities and mitochondrial capacity to oxidize pyruvate.

Conversion or reversion of interferon gamma release assays for Mycobacterium tuberculosis infection: a systematic review and meta-analysis.

BACKGROUND: Interferon-gamma release assays (IGRA) are widely used for diagnosis of latent tuberculosis infection. However, with repeat testing, IGRA transformation (conversion or reversion) may be detected and is challenging to interpret. We reviewed the frequency of and risk factors for IGRA transformation. METHODS: We screened public databases for studies of human participants that reported the frequency of IGRA transformation. We extracted study and subject characteristics, details of IGRA testing and results. We calculated the pooled frequency of IGRA transformation (and transient transformation) and examined associated risk factors. RESULTS: The pooled frequency of IGRA conversion or reversion from 244 studies was estimated at 7.3% (95% CI 6.1-8.5%) or 22.8% (20.1-25.7%), respectively. Transient conversion or reversion were estimated at 46.0% (35.7-56.4%) or 19.6% (9.2-31.7%) of conversion or reversion events respectively. Indeterminate results seldom reverted to positive (1.2% [0.1-3.5%]). IGRA results in the borderline positive or negative range were associated with increased risk of conversion or reversion (pooled OR: conversion, 4.15 [3.00-5.30]; reversion, 4.06 [3.07-5.06]). BCG vaccination was associated with decreased risk of conversion (0.70, 0.56-0.84), cigarette smoking with decreased risk of reversion (0.44, 0.06-0.82), and female sex with decreased risk of either conversion or reversion (conversion, 0.66 [0.58-0.75]; reversion, 0.46 [0.31-0.61]). CONCLUSIONS: IGRA conversion is less common than reversion, and frequently transient. Research is needed to determine whether individuals with reversion would benefit from tuberculosis preventive treatment. Re-testing of people with indeterminate results is probably not indicated, since indeterminate results seldom revert to positive.