The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni‐ and cross‐modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri‐modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross‐modal conditional dependencies. We found modality specific signatures of MMN (~100–200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross‐modal signature of mismatch processing in the P3a time range (~300–350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross‐modal conditional probability, suggesting sensitivity to multimodal  predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross‐modal conditional dependencies. Using a novel tri‐modal version of the roving stimulus paradigm we elicit modality specific mismatch negativity and modality independent P3 responses in auditory, somatosensory, and visual modalities which were source localized to sensory‐specific cortices and shared frontal sources. Across modalities, we found sensitivity of P3 responses to cross‐modal predictability. Using model comparison, our results suggest that single trial mismatch responses reflect signatures of Bayesian inference tracking stimulus transitions and cross‐modal conditional dependencies. The brain is subjected to such environmental regularities by a multimodal stream of sensory inputs ultimately constructing a perceptual representation of the world. The sensory system is thought to capitalize on statistical regularities to efficiently guide interaction with the world enabling anticipation and rapid detection of sensory changes Bregman, .Neuronal responses to deviations from sensory regularities can be valuable windows into the brain's processing of statistical properties of the environment and corresponding sensory predictions. The presentation of rare deviant sounds within a sequence of repeating standard sounds induces well known mismatch responses (MMRs) that can be recorded with electroencephalography (EEG), such as the mismatch negativity , recruited from the student body of the Freie Universität Berlin and the general public, participated for monetary compensation or an equivalent in course credit. The study was approved by the local ethics committee of the Freie Universität Berlin and written informed consent was obtained from all participants prior to the experiment.2.2Each trial consisted of three bilateral stimuli  that were presented simultaneously by triggering three instantaneous outputs of a data acquisition card  every 1150 ms .81.43±1.22dB; mean of the high intensity stimulus: 93.02±0.98dB).Auditory stimuli were presented via in‐ear headphones  to both ears and consisted of sinusoidal waves of 500 Hz and 100 ms duration that were modulated by two different amplitudes. The amplitudes were individually adjusted with the participants to obtain two clearly distinguishable intensities (mean of the low intensity stimulus: 3.97±0.84mA) was set in proximity to the detection threshold yet high enough to be clearly perceivable (and judged to be the same intensity on both sides). The high intensity level (mean: 6.47±1.33mA) was determined for each participant to be easily distinguishable from the low intensity level yet remaining non‐painful and below the motor threshold.Somatosensory stimuli were administered with two DS5 isolated bipolar constant current stimulators  via adhesive electrodes  attached to the wrists of both arms. The stimuli consisted of electrical rectangular pulses of 0.2 ms duration. To account for interpersonal differences in sensory thresholds, the two intensity levels used in the experiment were determined on an individual basis. The low intensity level  that were determined to be clearly perceivable and distinguishable prior to the experiment. Participants were seated at a distance of about 60 cm to the screen such that the LED's were placed within the visual field at a visual angle of about 67°.Visual stimuli were presented via light emitting diodes (LEDs) and transmitted through optical fiber cables mounted vertically centered to both sides of a monitor. The visual flashes consisted of rectangular waves of 100 ms duration that were modulated by two different amplitudes  via foot pedals. In six trials randomly placed within each run the fixation cross changed to one of the letters A, T, or V followed by a question mark. This prompted participants to report if the most recent stimulus (directly before appearance of the letter) in the auditory (letter A), somatosensory (letter T for “tactile”), or visual (letter V) modality was presented with low or high intensity. The right foot was used to press either a left or a right pedal, and the pedal assignment (left = low/right = high or left = high/right = low) was counterbalanced across participants.It should be noted that our MMR paradigm in form of an attended roving stimulus sequence with relatively long ISI (1.15 s) differs from the classic oddball protocol for MMN elicitation in which participants are engaged in a primary task, often attending a separate modality. Since this is not easily possible with our paradigm , we used catch‐trials in each modality to ensure that attentional resources were distributed largely equally across the simultaneous stimulus streams. The ISI at the upper end of the range used for MMN elicitation was set during piloting such that the perceptually demanding tri‐modal bilateral stimulation was deemed not to be overwhelming in terms of sensory overload.2.3s at time t evolving according to a Markov chain pstst−1 with each state deterministically emitting a combination of three binary observations conditional on the preceding observation combination poA,toS,toV,toA,t−1oS,t−1oV,t−1. For example, a transition expressed as [100|000] indicates a unimodal auditory change oA,t=1oA,t−1=0 with repeating somatosensory and visual modalities . For each stimulus modality, in each state, the other two modalities form either congruent observations ([00] and [11]), or incongruent observations ([01] and [10]), which was used to manipulate the predictability of transitions in the sequences in different runs of the experiment.Each of the three sensory modalities  were presented as binary (low/high) stimulus sequences originating from a common probabilistic model. The model consists of a state t−1 to t, S and V were congruent with p100|000=p000|100=p111|011=p011|111=0.025 and S and V were incongruent with p101|001=p001|101=p110|010=p010|110=0.15). The second setting defines lower change probability if the other two modalities are incongruent . The third setting defines equal change probability if the other two modalities are congruent or incongruent .Three types of stimulus sequences, depicted in Figure In each of six experimental runs, the stimulus sequence was defined by one of the three different probability settings. Each probability setting was used twice during the experiment and the order of the six different sequences was randomized. Participants were unaware of the sequence probabilities and any learning of sequence probabilities was considered to be implicit and task irrelevant.predicted , mispredicted  or unpredictable . For each modality, repetitions are more likely p=.825 than changes p=.175 regardless of the type of probability setting and stimulus, resulting in classic roving standard sequences for each modality .Following the nomenclature suggested by Arnal and Giraud , the res2.4Data were collected using a 64‐channel active electrode EEG system  at a sampling rate of 2048 Hz, with head electrodes placed in accordance with the extended 10–20 system. Individual electrode positions were recorded using an electrode positioning system .Preprocessing of the EEG data was performed using SPM12  and in‐house MATLAB scripts . First, the data were referenced against the average reference, high‐pass filtered (0.01 Hz), and downsampled to 512 Hz. Subsequently, eye‐blinks were corrected using a topographical confound approach  for standards  and deviants  in each modality, as well as four global standard and four global deviant regressors. The binning was chosen such that trials were roughly balanced across the bins, corresponding to, on average, around 100 deviant trials and around 500 standard trials in each category for each modality The global regressors captured the train length  of standards and deviants regardless of their modality, meaning that trials in which standards occurred in all three modalities were coded as global standards, whereas trials in which a deviant occurred in any of the three modalities were coded as global deviants.For further analyses, GLMs were set up as implemented in SPM12, which allows defining conditions on the single trial level. To test for effects of stimulus repetitions on standards, deviants, and MMRs (deviants minus standards), a Predictability model was defined that consisted of 37 regressors: an intercept regressor and 18 regressors coding standards and deviants of each modality for each of the three conditions described above: unpredictable , predicted , mispredicted . On the single‐participant level, these were coded for congruent and incongruent trials separately resulting in 36 regressors. By definition, the number of trials in regressors with mispredicted trials was lowest, on average corresponding to around 60 deviant trials and 800 standard trials per modality.To test for the implicit effect of cross‐modal predictability based on the different conditional probability setting in the sequence, a P3‐Conjunction model was specified that consisted of seven regressors: an intercept regressor and six regressors coding all standards and deviants for each of the three modalities. This model was used to apply SPM's second level conjunction analysis, contrasting standards and deviants across modalities in search of common P3 effects across modalities.Finally, a β‐parameter estimates for each model regressor over  space and time, which were subsequently analyzed at the group level. Second level analyses consisted of a mass‐univariate multiple regression analysis of the individual β scalp‐time images with a design matrix specifying regressors for each condition of interest as well as a subject factor. Second level beta estimates were contrasted for statistical inference and multiple comparison correction was achieved with SPM's random field theory‐based FWE correction  was source localized within a time window of 100–200 ms. For the P3, the average MMR at 330 ms was chosen for source localization as this time point showed the strongest overlap of P3 responses between modalities (based on the results of the P3 conjunction contrast).t tests for each modality as well as  conjunction contrasts across modalities. The resulting statistical parametric maps were thresholded at the peak level with p < .05 after FWE correction. The anatomical correspondence of the MNI coordinates of the cluster peaks were identified via cytoarchitectonic references using the SPM Anatomy toolbox  model making different predictions for the single‐trial EEG data.c for a given modality by counting stimulus repetitions: ct=dt(ct−1+dt), where dt=1ot=ot−1 takes on the value 1 whenever the current observation ot is a repetition of the previous observation ot−1 and dt=0 resets the current train length to zero. To form single‐trial predictors of the EEG data, the model outputs values that increase linearly with train length and have opposite signs for standards and deviants:Theories on MMN generation hypothesize adaptation and memory‐trace dependent change detection to contribute to the MMN. With prior repetition of stimuli, the response to standard stimuli tends to decrease while the response to deviant stimuli tends to increase. We defined the TLCD model to reflect such reciprocal dynamics of responses to stimulus repetition and change without invoking assumptions of probabilistic inference. The model is defined for each modality separately and tracks the stimulus train lengths In addition to the TLCD model, different BL models were created to contrast the static train length based TLCD model with dynamic generative models tracking transition probabilities. The BL models consist of conjugate Dirichlet‐Categorical models estimating probabilities of observations read out by three different surprise functions: Bayesian surprise (BS), predictive surprise (PS), and confidence‐corrected surprise (CS).BSyt=KLpst−1yt−1…y1‖pstyt…y1. PS is based on . Therefore, we defined a cross‐modal Dirichlet‐Categorical model to address the question whether the conditional dependencies were used by the brain during sequence processing for prediction of stimulus change. The dependencies in the sequence were independent of stimulus identity but provide information about the probability of repetition or alternation (dt) conditional on the congruency of the other modalities. The cross‐modal model thus estimates alternation probabilities  with a set of hidden parameters si when other modalities are incongruent and sc when other modalities are congruent. Therefore, while the unimodal model learns the probability of stimulus transitions within modality, the cross‐modal model learns the probability of stimulus alternations within modality conditional on the congruency of the other modalities. As such, the cross‐modal model provides a minimal implementation of a Bayesian observer that captures the cross‐modal dependencies in the sequences.For each modality, the unimodal Dirichlet‐Categorical model considers a binary sequence with two possible stimulus identities (low and high) estimating transition probabilities with 2.7.1The technical details of the model fitting and subsequent Bayesian model selection (BMS) procedures are identical to Gijsen et al.  where thIn total, eight regression models were fit: A null model (offset only), a TLCD regression model and, for each of the three surprise read‐out functions, one regression model including only the unimodal regressors and one additionally including the cross‐modal regressors. The purely unimodal regression model will be called UM and the regression model including unimodal and, additionally, cross‐modal regressors will be called UCM. The design matrix of the TLCD regression model consisted of four regressors, an offset and the predicted parametric change responses for each of the three modality sequences . Similarly, the design matrix of the UM regression model consisted of four regressors, an offset and the surprise responses of the unimodal model for each of the three modalities. The UCM regression model was identical to the UM regression model but with an additional three regressors containing the cross‐modal surprise responses for each modality. Therefore, the UCM regression model is more complex and gets assigned higher model evidence than the reduced UM regression model only if the additional regressors contribute significantly to a better model fit  corresponding to integration windows with a 0.5 stimulus weighting at   stimuli, of which the parameter with the best model evidence was chosen.To allow for the possibility of different timescales of stimulus integration  and compared it to the TLCD model (TLCD family) and the null model (NULL family). Since the BL models had their tau parameter optimized, which was not possible for the TLCD model, we applied the same penalization method used in our previous study . This difference wave typically shows a negative deflection at fronto‐central electrodes and corresponding positivity at temporo‐parietal sites, ranging from around 100 to 250 ms . This effect was driven by a negative linear modulation of the deviant response  as well as a repetition positivity effect on the standards . Similarly, the later P3 MMR increased with standard repetitions  and this effect was driven by an increase of deviant responses  and a decrease of standard responses . Given the temporal difference between standard (around 200–350 ms) and deviant (200–600 ms) train length effects, the parametric modulation of the late MMR beyond 350 ms seems to be primarily driven by the increase in deviant responses.Within early and late auditory MMR clusters, the response to both standards and deviants was modulated by the number of standard repetitions. The auditory system is known to be sensitive to stimulus repetitions, particularly within the roving standard paradigm  temporal negativities, resulting primarily from increased N140 components . This effect was driven by an increase of deviant negativity  in combination with a positivization of the standard . Similarly, the later P3 MMR increases with repetition of standards , mutually driven by increasing deviant responses  and decreasing standard responses .Early and late somatosensory MMRs were significantly modulated by stimulus repetition. Bilateral electrodes within the sMMN cluster show an increase of the sMMN amplitude with repetition . However, it is worth noting that some electrodes in this cluster seemed to show a similar pattern of response increases and decreases as in the auditory and somatosensory modality, which became apparent at more lenient thresholds. The vMMN tended to become more negative with repetition of standards , with opposite tendencies of deviant negative increase  and standard decrease . Thus, although we cannot conclude a modulation by standard repetition of the vMMN with any certainty, the observed beta parameters are in principle compatible with the effects observed in the auditory and somatosensory modalities .Within the significant visual MMN (vMMN) cluster, the linear contrast testing for repetition effects did not reach significance when correcting clusters for multiple comparisons (pfwe<.05), again constituted by an increase in deviant responses  and concomitant decrease in standard responses .Within the P3 MMR cluster, on the other hand, we find significant clusters of linear increase of the MMR  that comprised anterior central effects around 300–350 ms followed by more posterior effects from 400 to 600 ms  and the three‐level factor predictability condition . Separate ANOVAs were applied to deviants and standards. We hypothesized that the cross‐modal P3 MMR might be sensitive to multisensory predictive information in the sequence, as the P3 has been shown to be sensitive to global sequence statistics  and standards  show significant differences between predictability conditions. No significant interaction of predictability condition with modality was observed.To investigate the modulation of the P3 MMR by predictability, we used two‐way ANOVAs with the three‐level factor t tests were applied to the peak beta estimates to investigate the differences between the three pairs of conditions. For the ANOVA concerning the deviant trials, post hoc t tests show a significant difference for mispredicted > predicted , mispredicted > unpredictable  and no significant difference between unpredictable > predicted conditions . Similarly, for the ANOVA concerning the standard trials, post hoc t tests show that there is a significant difference for mispredicted > predicted , mispredicted > unpredictable  and unpredictable > predicted conditions .Post hoc Taken together, this result suggests that stimuli which were mispredicted based on the predictive multisensory configuration resulted in increased responses within the common P3 cluster compared to predicted or unpredictable stimuli, regardless of their role as standards or deviants in the current stimulus train.For completeness, we also tested the effect of predictability in the earlier MMN cluster, but we did not observe any significant modulations here (results not shown).3.3The source reconstruction analysis resulted in significant clusters of activation for each modality's MMN as well as the P3 MMR. The results are depicted in Figure pfwe<.05; left cluster: peak t = 6.20; right cluster: peak t = 7.64) corresponding to auditory cortex and in inferior temporal areas . The somatosensory MMN shows highest source activation in postcentral gyrus  corresponding to primary somatosensory cortex. Similarly, the vMMN shows highest source activation in the occipital cortex , around the occipital pole, corresponding to visual areas (V1–V4). Lowering the threshold to puncorr<.001  and lateral occipital cortex (fusiform gyrus) for vMMN . In addition to the sensory regions, common frontal sources with dominance on the right hemisphere were identified using a conjunction analysis for the MMN of all three modalities. In particular, significant common source activations were found in the right inferior frontal gyrus  and right middle frontal gyrus . Additional significant common sources include frontal pole , left inferior temporal gyrus  and right inferior parietal lobe .For each modality, the MMN was localized to source activations in the respective modality's sensory cortex and frontal cortex. Source localization of the auditory MMN shows the strongest activation in bilateral superior temporal areas  and bilateral (pre‐)frontal cortex . Additional significant sources were found in left inferior temporal gyrus , left and right lateral occipital cortex  and right precentral gyrus .For the late P3 MMR, a wide range of sources was expected to contribute to the EEG signal Linden, . To iden3.4As described in the previous sections, the responses of standards and deviants show specific sensitivity to (1) stimulus repetition and (2) cross‐modal conditional probability. To investigate the computational principles underlying these response profiles, eight different models capturing various learning strategies were fit to the single‐trial EEG data and compared via family‐wise BMS. A summary of the modeling results is depicted in Figure φ>0.95 (corresponding to expected posterior probability r>.7) from ~70 ms onward. On the other hand, the TLCD model did not exceed φ>0.95 for any electrode or time point. Therefore, the TLCD model was disregarded at this point, and we focused further investigation on the different BL models.The first model comparison aimed to further investigate observation (1) and the question whether the observed parametric modulation of standard and deviant EEG responses merely reflects a combination of neuronal adaptation and change detection dynamics or if the observed response patterns are indicative of an underlying generative model engaged in probabilistic inference. To this end, we ran family wise BMS which is schematically depicted in Figure φ>0.95, corresponding to r>.7).The second comparison set out to investigate observation (2) and evaluate the contribution of stimulus alternation tracking conditional on multimodal configurations beyond unimodal transition probability inference. Within the electrodes and time‐points with sufficient evidence for BL signatures (as established by the first model comparison), a comparison of a purely unimodal model family with a cross‐modally informed model family (UCM) was performed. Those electrodes and time‐points where the additional inclusion of cross‐modal regressors (UCM models) provided better model fits than purely unimodal models are highlighted in Figure Inspection of the beta estimates of auditory, somatosensory, and visual regressors of the UCM regression models shows that the beta maps of the unimodal predictors of the model resemble the ERP mismatch topographies of the respective modalities  around 5 to 25 stimuli . Overall, the same range of stimulus integration was found for the UM and UCM regression models and CS models tended to have higher integration windows (~10–20 stimuli) compared to Bayesian surprise models (~5–10 stimuli).In a final analysis, the optimal observation integration parameter 4The present study set out to compare mismatch signals in response to tri‐modal sequence processing in the auditory, somatosensory, and visual modalities and to investigate influences of predictive cross‐modal information. We found comparable but modality specific signatures of MMN‐like early mismatch processing between 100 and 200 ms in all three modalities, which were source localized to their respective sensory specific cortices and shared right lateralized frontal sources. An additional cross‐modal signature of mismatch processing was found in the P3 MMR for which a common network with frontal dominance was identified. With exception of the vMMN, both mismatch signals (MMN and P3) show parametric modulation by stimulus train length driven by reciprocal tendencies of standards and deviants across modalities. Strikingly, standard and deviant responses within the cross‐modal P3 cluster were sensitive to predictive information carried by the tri‐modal stimulus configuration. Comparisons of computational models indicated that BL models, tracking transitions between observations, captured the observed dynamics of single‐trial responses to the roving stimulus sequences better than a static model reflecting TLCD. Moreover, a BL model which additionally captured cross‐modal conditional dependence of stimulus alternation outperformed a purely unimodal BL model primarily at central electrodes. The comparison of different read‐out functions for the BL models provides tentative evidence that the early MMN may reflect dynamics of CS whereas later P3 MMRs seem to reflect dynamics of BS.4.1By using a novel tri‐modal roving stimulus sequence originating from an underlying Markov process of state transitions, we were able to elicit and extract unique EEG signatures in each of the three sensory modalities .Of the EEG mismatch signatures, the auditory MMN is one of the most widely researched responses to deviation from an established stimulus regularity  reported in the current study shows negative polarity at bilateral temporal electrodes and corresponding central positivity. The sMMN likely reflects an enhanced N140 component, as suggested by Kekoni et al. . HoweverWhile the auditory and somatosensory MMN's in the current study were found to be highly comparable in their signal strength, their hypothesized counterpart in the visual modality showed a comparatively weaker response. Nevertheless, we found a significant vMMN at occipital electrodes extending to temporal electrodes within a time window of 100–200 ms poststimulus, with corresponding (fronto‐) central positivity. This observation is in line with previous research reporting posterior  and secondary (S2) somatosensory cortices with additional frontal generators in right IFG and MFG. This finding is in accordance with previous research showing a combined response of S1 and S2 to underlie the sMMN  and additional frontal activations in IFG and MFG as the neuronal generators underlying the vMMN. Previous studies have shown similar combinations of visual and prefrontal areas . While somatosensory deviant responses have previously been shown to decrease with increasing stimulus probability  central electrodes and is thought to be elicited regardless of sensory modality  and global  sequence properties could be captured by signatures of Bayesian inference. To this end, we compared a simple TLCD model to families of BL models capturing different aspects of the sequence statistics. In light of the literature discussed above, we hypothesized that BL models would outperform the TLCD model in explaining the recorded mismatch responses.Overall, the BL models outperformed the static TLCD model in all electrodes in the MMN and P3 clusters indicating that these responses reflect dynamics beyond the basic repetition effects observed in the ERP analyses. This result provides evidence to suggest that the MMN and P3 MMR capture the trial‐to‐trial dynamics of Bayesian inference and are thus markers of probabilistic sequence processing in the brain.Within the family of BL models, we found that a cross‐modally informed model (UCM), tracking cross‐modal conditional dependencies between modalities in addition to unimodal transitions, outperformed a purely unimodal transition probability model (UM) at central electrodes within an early and a late time‐window. The cross‐modal effects in the late time‐window are directly in line with the sensitivity of the P3 cluster to cross‐modal predictability discussed above and support an interpretation of P3 mismatch responses to reflect signatures of cross‐modal Bayesian inference. Given that cross‐modal learning was not explicitly instructed or task‐relevant, the results are compatible with the view that the brain is sensitive to cross‐modal information by default  and tends to carry signatures of multimodal inference and model updating (Bayesian modeling results), we suggest that the P3 likely reflects model updates with respect to the multimodal context. Although unnoticed by participants, given that the statistical regularities changed across experimental runs, the generative model continuously required updating. As such, our results might be reflective of a volatile sensory environment and relate to previous findings which indicate that later MMRs, such as the P3, reflect belief updates about the volatility of the underlying (hidden) statistics governing sensory observations  differences. The inclusion of three read‐out functions primarily served the purpose of avoiding bias in the comparison of the BL models by prior choice of the read‐out. To this end, the most prominent surprise read‐out functions used in the literature were included. Research would benefit from future studies specifically designed to compare different surprise measures without the manipulation of other aspects of the underlying models. A valuable overview and suggested experiments for that purpose have been recently provided by Modirshanechi et al. .5With the current study, we provide evidence for modality specific and modality independent aspects of mismatch responses in audition, somatosensation, and vision resulting from a simultaneous stream of tri‐modal roving stimulus sequences. Our results suggest that responses to stimulus transitions in all three modalities are based on an interaction of hierarchically lower, modality specific areas with hierarchically higher, modality independent frontal areas. We show that similar dynamics underlie these mismatch responses which likely reflect predictive processing and Bayesian inference on unimodal and multimodal sensory input streams.http://www.mind-and-brain.de/home/), and Deutscher Akademischer Austauschdienst . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This work was supported by Berlin School of Mind and Brain, Humboldt Universität zu Berlin (MG and SG, The authors declare that no competing interests exist.


There is growing interest in health, developmental and survival outcomes of children who are born HIV‐free to women living with HIV (children born HIV‐free). To date, the research agenda has been largely determined by researchers, funders and policy makers, with limited involvement of parents, who are key stakeholders. Researchers at UCL Great Ormond Street Institute of Child Health in partnership with community‐based organisation 4M Network of Mentor Mothers conducted two workshops with parents in March 2022 to establish research priorities for children born HIV‐free, and key considerations for methodological approaches both to research and engagement with the affected communities.When exploring research on children born HIV‐free, we consider the following: what aspects of current research are aligned with women and parents’ priorities, what is missing and what approaches would be preferred. A holistic approach to research on children born HIV‐free should be prioritised, focussing on a breadth of outcomes and how they intersect. Secondary use of existing data sources should be maximised to facilitate this, with a view of monitoring the long‐term effects of fetal antiretroviral drug exposure alongside other key health and developmental outcomes. Involving and engaging with parents, and children where possible, must be at the heart of research design to maximise relevance and impact of findings for the affected communities. Potential barriers to engaging with individuals who were children born HIV‐free include parental disclosure and individuals not identifying as a child born HIV‐free to a mother living with HIV. Stigma‐free language must be incorporated into the vocabulary of researchers and other stakeholders, avoiding reference to exposure; we propose the term “children born HIV‐free.”Mothers and parents living with HIV should be involved in research about their children born HIV‐free and are key in identifying research priorities so that findings may translate into an impact on their children's health and wellbeing. Meaningful involvement of women living with HIV through trusted community partners is an effective mechanism by which to elicit views on research about their children. In paraThe first 1000 days of life (conception to approximately 24 months) is a critical period of development for children, where factors relating to the maternal and external environment can play an important role in modifying life‐long risk of health and developmental outcomes . GrowingThe multilevel response to improve outcomes for children born HIV‐free have included interventions to reduce mortality, support programmatic scale‐up and effectiveness of prevention of VT programmes, and minimise health and developmental disparities within a framework where children are alive, HIV‐free and thriving . With prThere is growing recognition of the importance of including pregnant people and parents living with HIV at all stages of research, beyond being research participants, in accordance with equitable research principles and UNAIDS policy for greater involvement of people living with HIV , 22. SucResearchers at UCL conducting research on children born HIV‐free in the UK and 4M Network of Mentor Mothers convened two workshops in March 2022 with six biological mothers of children born HIV‐free from Black ethnic backgrounds to explore components for a research agenda in the UK and globally. Building on the outcomes of these discussions, we provide key learnings for stakeholders involved in research on children born HIV‐free.2In our workshops, six parents living with HIV established themes for research on children born HIV‐free, including priorities, an infrastructure for utilising existing data source, language preferences and approaches Figure .2.1There has been long‐standing recognition of the need to monitor the longer‐term health and development of children born HIV‐free in the UK, in the context of routine discharge from clinical care after confirmation of HIV‐free status at approximately 24 months of age , 29. TheSub‐optimal immune system development and increased risk and severity of infectious diseases have been reported in cohorts of children born HIV‐free both in high‐income and low‐income settings, although not specifically in the UK , 36. Par2.2When considering data collection methods, secondary use of administrative data (information generated when interacting with public services ) was desUtilising existing data sources to explore health and developmental outcomes among children born HIV‐free minimises the burden on the affected communities to contribute to research as participants. Currently, much of the data from this population come from prospective observational cohort studies that require follow‐up visits and are associated with costs in both time and money to participants and their families. In addition, the perceived burden of follow‐up may act as a barrier to recruitment, threatening the feasibility of studies  that are2.3The use of language for people living with HIV has evolved, with greater recognition of its power to perpetuate stigma and discrimination . There i2.42.4.1Exploring outcomes of children born HIV‐free within the context of the woman and mother could offer an alternative perspective on inequalities within and between populations:For a healthy pregnancy, birth and child, a woman must be physically and mentally well.”“Maternal mental health in the context of HIV in pregnancy is an underdeveloped area of research in the UK. Women living with HIV are disproportionately affected by mental health difficulties , 44, dri2.4.2Parents identified children born HIV‐free as experts‐by‐experience who should be involved in research. However, engaging children born HIV‐free is associated with several challenges.Firstly, parents need to talk about their HIV status to their children, but rates of parental disclosure are low globally , 52. WhiFurthermore, those who are HIV‐free and aware of their HIV‐free status may not perceive themselves as different from children who were born to women without HIV. Engaging a population that do not consider themselves as at risk of adverse outcomes or do not identify as “HIV‐exposed” is a significant barrier to the co‐production of research on children born HIV‐free. A driving factor for children and young adult's dissociation from being “HIV‐free” could be related to HIV‐associated stigma and fear of bullying if their parent(s)’ status became common knowledge. Researchers must also consider the capacity for children and young adults to engage with and become involved with research. Some will also be navigating social and economic disadvantage which can be prevalent in communities affected by HIV , 56, as There are likely to be varying perspectives on being “HIV‐free” among children and young adults born to women living with HIV, highlighting the importance of involving them in discussions around the research agenda from inception, as their views on research and its relevance to their lives may differ to their parents .2.5Research on children born HIV‐free can be poorly disseminated to communities which minimises impact. When considering stakeholder groups for dissemination of research findings, children and/or young adults born HIV‐free should be prioritised along with their parent(s) living with HIV. Children born HIV‐free should be given access to information on research at an appropriate age of comprehension  using sp3In this commentary, we draw upon findings from workshops with six parents living with HIV who provided feedback on priorities and approaches for research on children born HIV‐free. We have highlighted how parents and children affected by HIV have unique and important insights into research on children born HIV‐free. It is vital that academics, clinicians, funders and other stakeholders centre their voices to increase relevancy and maximise the impact of research. In collaboration with parents living with HIV, we have identified a set of research priorities and recommendations for the research agenda that are based on selecting a broad range of outcomes; using and linking existing data; using stigma‐free language; and employing a holistic, life‐course approach that values involving parents and children. We propose an extension to existing frameworks to decolonise global health that foreground the need for equitable research partnerships , by inclCT has received funding from ViiV Healthcare via the PENTA Foundation for a pharmacovigilance study on drug use and safety in pregnancy. ST has previously received speaker honoraria and funding for the preparation of educational materials from Gilead Sciences.LLB and AN conceived concept of the project. LLB, AN, CT and ST designed workshop. AN, MN, PC, GL, MB and EN contributed to workshop content. LLB wrote the manuscript. AN, CT and ST reviewed the manuscript.


Individuals with a body mass index (BMI) of ≥ 30 kg/m2 experience weight stigma when interacting with the healthcare system. There is limited data on how weight stigma impacts patient’s experience of obstetric care. This study aims to assess perceived control over the birth process and compare patients with BMI ≥ 30 to those with BMI < 30.We performed a secondary analysis of a cross-sectional study of term patients. Postpartum, participants completed the Labour Agentry Scale (LAS), a validated tool to assess perceived control over labor/birth. Continuous LAS scores were compared between patients with BMI < 30 and BMI ≥ 30.There was no difference in LAS between those with BMI ≥ 30 and BMI < 30. When stratified by World Health Organization (WHO) class of BMI, those with BMI ≥ 40 had a significantly lower LAS scores than those with BMI < 30 , however, this finding was no longer significant after controlling for length of labor and cesarean birth.Only participants with the highest BMI experienced decreased control over labor, and this finding was no longer significant after controlling for mode of delivery and length of labor. Further research is necessary into how weight stigma influences birthing people’s experience. Weight stigma permeates the medical field across all specialties1. Prior research has demonstrated that providers spend less time, establish less rapport and engage in less health education with patients living in larger bodies2. Additionally, the patient experience of weight stigma varies, and increases with increasing body mass index(BMI), particularly for those who have a BMI ≥ 303. This stigma leads patients to experience psychological distress, avoid seeking care and ultimately have poor health outcomes including increased rates of diabetes and cardiovascular disease2.Weight stigma is defined as the social rejection and systematic devaluation of individuals living in larger bodies that do not conform to societal norms of body weight or shapeAlthough weight stigma has been demonstrated to impact many aspects of care and patient experience, the potential effect of weight stigma among birthing people is less well understood. To our knowledge, there have been no studies to date which specifically evaluate patient experience of control over labor based on their weight. This study aims to assess perceived control over the birth process and compare patients with BMI ≥ 30 to those with BMI < 30.4. This study was approved by the institutional review board (#1691795). Eligible participants were nulliparous, English-speaking, and had singleton, non-anomalous pregnancies at gestational age ≥ 37 weeks. Participants were ineligible if they were scheduled for cesarean delivery (CD) or had a contraindication to labor. In the parent study,We performed a secondary analysis of a cross-sectional survey study of patients admitted to the labor and delivery unit at a single academic medical center, during the months of June and July 20215. Trained researchers then performed a detailed chart review. For this secondary analysis, the primary outcome was LAS score, and participants were stratified by body mass index (BMI) at the time of delivery admission – calculated by stated or measured weight kg divided by height in meters-squared). BMI was first dichotomized into two groups—participants with BMI ≥ 30 kg/m2 versus those with BMI < 30 kg/m2—and then into multiple groups, and BMI < 30 was compared to each WHO class of obesity 6.After obtaining written consent, participants filled out a questionnaire which included the Labour Agentry Scale (LAS), a validated 29-item instrument that assesses childbirth control2. Multiple linear regression was performed controlling for a priori confounders of mode of delivery and labor length.Data were analyzed using STATA v.15 . Fisher’s exact test was performed for categorical variables and Wilcoxon Rank-sum for continuous variables. The primary outcome of score on the LAS was compared between those BMI < 30 and those with BMI ≥ 30 as well as those without obesity to those with WHO class of obesity as well  compared with BMI < 30 kg/mOf 149 participants in the original study, 87 (58.4%) reported a BMI ≥ 30. There were no differences in maternal age at delivery or participant stated race/ethnicity with the majority of participants identifying as white between those with a BMI ≥ 30 and those with BMI < 30 . There wThe majority of participants underwent spontaneous vaginal delivery  and there were no differences in mode of delivery between groups. However, those with BMI ≥ had significantly longer median labor time from admission to delivery than those with BMI < 30 . There was no difference in rate of NICU admission or need for neonatal therapy between groups .There were no significant differences in scores on the LAS for those who had BMI < 30 compared with BMI ≥ 30 . When analyzed by BMI category with BMI < 30 as the reference, those with BMI ≥ 40 had significantly lower LAS scores  . HoweverIn this study, there was no association of LAS score between those with BMI < 30 and those with BMI ≥ 30. For those with the highest BMI (≥ 40), lower scores on the LAS compared to those with BMI < 30 could be accounted for by the impact of cesarean delivery.9. This may be a type II error due to our small sample size. As cesarean delivery could potentially explain the difference in LAS scores between patients with BMI > 40 compared to BMI < 30 a larger study is needed to confirm these findings. Additionally, interventions to decrease the rate of cesarean delivery, particularly among parturients with the highest BMIs could lead to improved patient experience.Contrary to prior literature, we did not detect a difference in mode of delivery among those with BMI ≥ 30 compared to patients with BMI < 3010, and report feeling like they are being “blamed” for the fetal risks of elevated BMI in pregnancy12. All of this culminates in patients reporting that the concern over their weight took the joy from their pregnancy12.To our knowledge this is the first study to assess perceived control over labor based on BMI as measurement of weight stigma. Weight stigma has been previously studied in primary care and adult medicine fields; however, data is lacking in obstetrics. Qualitative studies performed in the perinatal population in the UK and Australia have reported that patients experience negative attitudes and demeaning comments from providers and staff in the antepartum, intrapartum and postpartum period13. Nevertheless, this study has many strengths. To our knowledge, this if the first study to assess patient experience of control over labor and how that interacts with patient BMI. The detailed chart review allowed us to assess and control for differences between those with an elevated BMI and those without elevated BMI.Our study was limited by small sample size, which may increase the likelihood of type II error when analyzing differences between study groups. BMI was also calculated from patient stated weight and height on admission to labor and delivery, which may have led to measurement error as patients regularly underestimate/underreport weightFuture qualitative studies are planned to assess both patient experience of weight stigma in pregnancy and provider attitudes toward those living in larger bodies with the ultimate goal of creating more inclusive obstetric care models.Patients with a BMI > 40 kg/m2 experienced less control over their labor, however this finding was no longer seen after controlling for cesarean delivery and length of labor. Future qualitative studies on obstetric patient experience of weight stigma and how it effects their labor is necessary.


TERC). Telomerase re‐activation or ectopic expression promotes survival and permits unlimited proliferation in tumours and healthy non‐malignant cells. TERT gene therapies improve health and lifespan in ageing mice and mouse models of age‐related diseases. The extra‐telomeric functions of telomerase are critical to ageing. These include protection against oxidative stress, orchestration of chromatin modifications and transcription, and regulation of angiogenesis and metabolism . Given these biological functions are key adaptations to endurance training and the recent meta‐analytical findings that indicate exercise up‐regulates TERT and telomerase, a comprehensive discussion on the implications of the canonical and extra‐telomeric roles of telomerase is warranted. This review highlights the therapeutic benefits of telomerase‐based treatments for idiopathic and chronic diseases that are linked to ageing. Discussion on the canonical and extra‐telomeric roles of telomerase are presented, followed by a detailed summary of the evidence on how exercise influences telomerase. Finally, the potential cell signalling underpinning the exercise‐induced modulation of telomerase are discussed with directions for future research.Telomerase preserves genomic integrity by maintaining and protecting the telomeres. Seminal findings from 1985 revealed the canonical role of telomerase and motivated investigations into potential therapeutic strategies to combat one of the hallmarks of ageing—telomere attrition. Since then, the field of telomere biology has rapidly expanded, with telomerase serving essential roles in cancer and cell development through its canonical function. However, telomerase also exerts critical extra‐telomeric functions through its protein  and RNA components  and RNA template of telomerase also perform extra‐telomeric functions, including transcriptional regulation, angiogenesis, and control of metabolism and mitochondrial function. Considering endurance exercise up‐regulates TERT expression and telomerase activity, regular training could improve healthy biological ageing through the canonical and extra‐telomeric functions of TERT. AAVadeno‐associated virusALTalternative lengthening of telomereseNOSendothelial nitric oxide synthaseGDF11growth differentiation factor 11HDAChistone deacetylaseHUVECshuman umbilical vein endothelial cellsPBMCsperipheral blood mononuclear cellsRNPribonucleoproteinROSreactive oxygen speciesTERCtelomerase RNA componentTERTtelomerase reverse transcriptaseVEGFvascular endothelial growth factor1Ageing is a complex biological process involving a dynamic network of cellular and molecular activity incompletely understood. Since the early 1960s, it was clear that cells have a finite ability to undergo mitosis before entering a senescent state, although the underlying mechanisms were unclear  complexes capable of telomere elongation through its reverse transcription activity  or the major catalytic protein (Tert−/−) are viable despite the absence of telomerase activity. Whilst maintaining embryonic viability, Tert−/− or Terc−/− mice possess no obvious abnormalities in early generations    were revealed and established as a vital protein component of telomerase   recapitulate the accelerated ageing phenotypes  observed in patients with telomere syndromes  compared to smaller animals to reduce their risk of cancer  telomeres on specific chromosomes to circumvent replicative senescence, as well as exert its extra‐telomeric functions. This would seem unlikely when catalytically inert TERT or TERC are used in experiments, as they cannot lengthen telomeres. It should also be noted that the mechanisms by which TERT exerts its non‐canonical functions remain incompletely understood in most circumstances. This next section focuses on the non‐canonical functions of telomerase with a focus on those relevant to physiological adaptations to endurance training , endothelial nitric oxide synthase (eNOS) and ERK1/2 signalling pathways  via ERK1/2 signalling and Sp1  Terc−/− and G4 Tert−/− mice exhibit marked reductions in Pgc1α/β and downstream metabolic genes, and enforced expression of Tert or Pgc1α restored the dysregulated transcriptional profile in the heart and liver  is challenging. That the metabolic compromise is much worse in G4 versus G1 Tert−/− mice , telomerase and metabolism, which is undoubtedly supported by the extra‐telomeric effects of TERT in the mitochondria.Mice already vulnerable to oxidative stress undergo rapid vascular ageing after 4.32O2), protects mtDNA from lesions and superoxide generation, and boosts mitochondrial membrane potential in cultured fibroblasts  where it protects against oxidative stress and mtDNA damage. Given that telomeres are absent from the circular mitochondrial genome, the localisation of TERT and telomerase activity in the mitochondria was an exciting finding. Elevated reactive oxygen species (ROS) triggers the nuclear export of TERT from the nucleus into the cytosol , heart (1544) and the liver (1357) compared to wild‐type controls in genes related to oxidative phosphorylation, oxidative stress and gluconeogenesis  and heart (n = 1754)  knockdown of TERC downregulated genes involved in myelopoiesis  in human HL60 and U937 cells (neutrophil and monocyte progenitors) that relied on the recruitment of RNA polymerase II  and subsequently serves as an RNA polymerase to produce double‐stranded RNAs in cancer cell lines   cells  in neonatal cardiac ventricles from Wistar rats  promoter and genome‐wide DNA methylation in hepatocellular carcinoma cell lines  3 lysine (K) 9 di‐methylation and H4K12 acetylation, and increased H3 K9 acetylation  TERT expression after exercise  that regulate telomerase . For example, a single bout of cycling at 50% or 75% of VOTERT expression , which were absent in second generation (G2) TERT expression was increased after 12 weeks of resistance training with an emphasis on high repetitions and low load performed twice a week . Since its discovery in 1985, several extra‐telomeric functions of TERT have been uncovered outside its canonical role of protecting and maintaining the telomeres. These include metabolism, angiogenesis and cell survival . Based on the overlapping signalling pathways regulated by exercise and TERT, it is possible that the healthy biological ageing elicited by regular—lifelong—exercise may not be limited to the canonical roles of TERT at the telomeres. On the contrary, numerous extra‐telomeric functions are likely responsible and link telomere biology to other hallmarks of ageing  could be crucial to prolonging human health span (and lifespan) and may help address the seamless progressive burden of chronic disease and our ageing population. Given that such therapies are currently not on the horizon and are possibly decades from forming standard clinical practice, one should be encouraged to regularly engage in endurance exercise training to promote healthy biological ageing.Therefore, future work should seek to elucidate whether the extra‐telomeric functions presented in this review are involved in physiological adaptations to endurance training. The signalling molecules controlling JD drafted the manuscript, prepared the figures, reviewed and approved the final version of the work.This work was not financially supported.None declared.


Despite similar incidence rates among Black and White women, breast cancer mortality rates are 40% higher among Black women. More than half of the racial difference in breast cancer mortality can be attributed to triple negative breast cancer (TNBC), an aggressive subtype of invasive breast cancer that disproportionately affects Black women. Recent research has implicated neighborhood conditions in the etiology of TNBC. This study investigated the relationship between cumulative neighborhood‐level exposures and TNBC risk.This single‐institution retrospective study was conducted on a cohort of 3316 breast cancer cases from New Castle County, Delaware (from 2012 to 2020), an area of the country with elevated TNBC rates. Cases were stratified into TNBC and “Non‐TNBC” diagnosis and geocoded by residential address. Neighborhood exposures included census tract‐level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards. An overall cumulative risk score was calculated based on tract‐level exposures.Univariate analyses showed each tract‐level exposure was associated with greater TNBC odds. In multivariate analyses that controlled for patient‐level race and age, tract‐level exposures were not associated with TNBC odds. However, in a second multivariate model that included patient‐level variables and considered tract‐level risk factors as a cumulative exposure risk score, each one unit increase in cumulative exposure was significantly associated with a 10% increase in TNBC odds. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents.Cumulative exposure to neighborhood‐level risk factors that disproportionately affect Black communities was associated with greater TNBC risk. In a multilevel model that adjusted for patient age at diagnosis and race, a one‐unit increase in the cumulative exposure to census tract‐level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards was associated with a 10% increase in the odds of triple negative breast cancer (TNBC) relative to other subtypes of invasive breast cancer. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents, which may help to explain racial disparities in breast cancer mortality. Breast cancer is the leading cause of both cancer incidence and mortality among Black women,Tumor biology may underpin Black‐White differences in breast cancer mortality not solely attributable to healthcare access. In particular, triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that is twice as prevalent among Black women.Efforts to improve the prevention of TNBC have been stymied by a lack of progress in identifying risk factors that have been validated among Black women.Building on these efforts, we previously considered the role of residential segregation for TNBC incidence. Given the well‐documented and pervasive pattern of residential segregation in the USThe objective of this study was to extend this research and conduct a comprehensive assessment of contextual exposures that may help to explain the relationship between neighborhood factors and TNBC risk. We considered all known domains of contextual exposures that have been shown to impact patient‐level exposure to TNBC risk factors, including unhealthy alcohol use, metabolic dysfunction , and reproductive factors .22.1This single‐institution retrospective study was conducted with data from two patient cohorts from New Castle County, Delaware. New Castle County is the catchment area for the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), which is a part of the Christiana Care Health System.The first patient cohort (“Breast Cancer Cohort”) was composed of breast cancer patients treated at the HFGCCRI. The HFGCCRI provides cancer care to more than 600 analytic breast cancer cases annually, including 85% of all breast cancer cases in New Castle County.The second patient cohort  was used to generate census tract‐level prevalence estimates of alcohol use disorder and diabetes in New Castle County. Patient records came from electronic health record (EHR) data from the Christiana Care Health System, which provides 88% of all non‐veteran adult acute care in New Castle County.This study was reviewed and approved by the Christiana Care Health System Institutional Review Board and was conducted in accordance with the US Common Rule. The need for consent was waived for both patient cohorts.2.2Patient measures for the Breast Cancer Cohort included age at diagnosis, race, residential address, and breast cancer subtype markers, which were abstracted from the HFGCCRI cancer registry. Breast cancer cases were classified as “TNBC” when ER, PR, and HER2 were all known negative; all other cases were classified as “Non‐TNBC.”A total of 19 census tract‐level measures were initially considered as proxies for each of the four domains of contextual exposures under consideration: unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental factors. These were reduced to a final measure for each domain based on both statistical and substantive considerations  prevalence and diabetes prevalence estimates generated from the General Hospital Cohort were selected as the proxy measures for contextual unhealthy alcohol use and metabolic dysfunction, respectively. Tract‐level “percentage of single female‐headed households with children” was selected as the proxy measure for breastfeeding initiation. This measure was obtained from the US Census Bureau's American Community Survey.Tract‐level Risk Screening Environmental Indicators (RSEI) cancer risk scoreFinally, an overall cumulative risk score was calculated based on methods used in other studies of cumulative exposures and health.2.3Spatial data management and statistical analyses were performed in the R Statistical Computing Environment.Two multilevel logistic regression models were used to examine the odds of TNBC (vs. Non‐TNBC). Both models adjusted for individual (level 1) variables and either included census tract (level 2) variables individually or summarized in the cumulative exposure risk score. Individual‐level variables included age at diagnosis and race. Tract‐level variables included AUD prevalence, diabetes prevalence, percentage single female‐headed households with children, and the RSEI cancer score. A census tract‐level random effect was tested for inclusion in both models to account for the clustering of patients within tracts.The spatial covariation of the cumulative exposure risk score and TNBC risk was visualized with a boxplot and a choropleth map. Superimposed on the choropleth map are New Castle County TNBC hot and cold spots that were previously detected and described elsewhere.The spatial covariation between the cumulative exposure risk score and census tract racial composition was visualized with a bivariate choropleth map,3Of the 3316 cases in the Breast Cancer Cohort, 453 (13.6%) were classified as TNBC and 2863 (86.3%) as Non‐TNBC .Estimating cumulative exposure to contextual risk factors is consistent with a This study has several important limitations. First, the single‐site, cross‐sectional design precludes drawing causal and generalizable inferences. Second, we did not have access to residential histories or patient‐level measures of relevant exposures , which may introduce potential exposure misclassification. Third, while our prior research suggests that the area‐level exposure estimates generated from EHR data are strongly correlated with small‐area estimates provided by the CDC,Beyond the replication and validation of our findings with similar methods, we wish to highlight the example and methodological recommendations offered by a few key researchers to further advance research that considers structural racism, residential segregation, and health disparities.In conclusion, the results from this study extend prior research that has documented a relationship between general neighborhood characteristics  and TNBC riskScott Siegel: Conceptualization (lead); project administration (lead); resources (lead); writing – original draft (lead). Madeline Brooks: Data curation (lead); formal analysis (lead); visualization (lead); writing – original draft ; writing – review and editing . Jesse Berman: Conceptualization ; methodology ; writing – review and editing (supporting). Shannon Lynch: Conceptualization ; writing – review and editing . Jennifer Sims‐Mourtada: Conceptualization ; data curation ; writing – review and editing (supporting). Zachary Schug: Conceptualization (supporting); writing – review and editing (supporting). Frank Curriero: Conceptualization ; methodology (lead); supervision ; writing – original draft (supporting); writing – review and editing .This project was supported by NIGMS (P20 GM103446) from the NIH and the State of Delaware (to S.D. Siegel).The authors declare that they have no competing interests.Figure S1–S2:Click here for additional data file.Table S1–S4:Click here for additional data file.


No poverty.Zero hunger.Good health and well‐being.Quality education.Gender equalityClean water and sanitation.Affordable and clean energy.Decent work and economic growth.Industry, innovation, and infrastructure.Reduced inequalities.Sustainable cities and communities.Responsible consumption and production.Climate action.Life below water.Life on land.Peace, justice and strong institutions.Partnership for the goals.The 17 sustainable developmental goals (SDGs) established by the United Nations (UN) General assembly in 2015 are the following:All these goals are interconnected, and there are many ways that microbial technologies have impacted each of them. Therefore, the examples of microbial technologies that may impact SDG that I include in this ‘Burning Questions’ editorial do not wish to be exhaustive, but to highlight the enormous potential of microbial biotechnology. Furthermore, recently an editorial was published which addressed how microbial technologies have contributed to the UN SDG and thus have played an important role in diminishing the risk of social conflicts; how microbial technologies are important to reach peace  and has been incorporated in different products. This food ingredient is rich in both insoluble fibre and protein and is produced from the fermentation of a fungus called The intensification of agriculture is a need for producing enough food for the rapidly growing population. In this respect, I want to comment on two aspects of microbial biotechnology (for a comprehensive review consult Anand et al., In the last 60 years, we have witnessed a considerable increase in agriculture yield, mainly in high‐income countries, that has been called the green revolution. This technology‐intensive agriculture relies on the use of inorganic fertilizers, chemically synthetized insecticides and other supplies that are out of reach for smallholder farmers in low‐income countries, a situation that causes profound inequities. In addition, the use of fertilizers and pesticides has caused severe water and soil pollution Baron, .Bacillus thuringiensis, virus and fungi which are non‐toxic and highly specific for certain pests, but they only constitute around 2% of the insecticidal market (Soberón et al., An alternative for the use of chemically synthetized insecticides is the use of bioinsecticides, which include bacteria, mainly Rhizobium‐legume interaction has been widely studied in the light of sustainable production of food (Lindström & Mousavi, Rhizobium‐based inoculants that reduce the use of these fertilizers with legume crops. Therefore, making symbiotic nitrogen fixation available to non‐legume crops would represent a major breaking point in agriculture and in the possibility of ending hunger in the world.As stated above, highly technologized agriculture is dependent on fertilization, markedly by nitrogen. The use of nitrogen fertilizers accounts for high greenhouse emissions, water pollution and eutrophication. However, symbiotic nitrogen fixation by the Rhizobium interaction with legumes, forming specialized root nodules where nitrogen fixation takes place. There are still some crucial points that are not fully understood, but it is a promising field with great scientific advances (Jhu & Oldroyd, Intensive research is being carried out to engineer nitrogen fixation in target crops based on the study of B. thuringiensis toxins and thus produce bioinsecticides (Soberón et al., Transgenic plants have been developed and are commercialized by global companies, which express The threat of pollution impacts several SDGs, specifically SDG 12 which is about ensuring sustainable consumption and production patterns. Microbial biotechnologies are at the centre of pollution treatment, and we are facing problems that have not been met during the evolution of life in our planet. This is the case of plastic pollution and the formation of microplastics that are nearly ubiquitous and affect the environment and all living creatures (Schmid et al., Pseudomonas and sophorolipids produced by the yeast Starmerella bombicola (Zibek & Soberón‐Chávez, Biosurfactants comprise compounds produced by diverse types of microorganisms like bacteria and fungi, which, even having different chemical structures have the common ability to act as surfactants, having an amphiphilic nature (Baccile & Poirier, Summing up, in my opinion microbial technologies have a great potential to contribute to reaching UN SDG in several areas as has been reviewed recently (Anand et al., The conceptualization and writing is responsibility of GSCh.No funding information provided.The author declares that she does not have any conflict of interest.


The hygiene hypothesis, according to which the recent reduction of exposure to infectious agents in the human species would be the origin of various diseases, including autoimmune diseases and cancer, has often been proposed but not properly tested on animals. Here, we evaluated the relevance of this hypothesis to cancer risk in mammals in an original way, namely by using information on zoo mammals. We predicted that a higher richness of parasitic cohorts in the species' natural habitat would result in a greater occurrence of evolutionary mismatch due to the reduction of parasites in captive conditions. This, in turn, could contribute to an increased risk of developing lethal cancers. Using a comparative analysis of 112 mammalian species, we explored the potential relationship between cancer risk and parasite species richness using generalized phylogenetic least squares regressions to relate parasite species richness to cancer risk data. We found no strong evidence that parasite species richness increased cancer risk in zoo mammals for any of the parasite groups we tested. Without constituting definitive proof of the irrelevance of the hygienic hypothesis, our comparative study using zoo mammals does not support it, at least with respect to cancer risks. The hygiene hypothesis suggests that reduced exposure to infectious agents may lead to various diseases, including autoimmune diseases and cancer. To test this hypothesis in relation to cancer risk, a comparative analysis of 112 mammalian species, specifically zoo mammals with reduced exposure to infectious agents, was conducted. The study did not find strong evidence to support the idea that higher parasite species richness increases cancer risk in zoo mammals, indicating that the hygiene hypothesis may not apply to cancer risks at least in this context. They estimated the cancer mortality risk as the ratio between the number of cancer‐related deaths and the total number of individuals whose postmortem pathological records were entered in the database, which can be used to perform complete case analysis. Incidence of cancer mortality, which is a metric of cancer mortality risk eliminating potential biases due to disregarding left truncation  and right censoring , was computed using a Kaplan–Meier estimator . Those three estimates allow us to account for the effect of sampling effort on parasitic species richness. Based on extensive simulations ; a database compiling information on the diversity of parasites found in wild ungulates (artiodactyls and perissodactyls), carnivores and primates . Missing information on the diet of one species, the Californian sea lion  regressions to quantify the effect of the parasite species richness on the cumulative cancer mortality risk of mammal species following a two‐step protocol . We first investigate the potential effect of parasite species richness to increase the probability of detecting cancer and then quantify their effect on the CMR and ICM of a species. Vincze et al.  showed tTo account for the lack of independence between species due to shared ancestry, we reused the phylogenetic tree from Vincze et al. . This trAll analyses were performed using R , life expectancy  and diet  on the probability of detecting cancer mortality. Thus, they were not included in subsequent binomial analyses.The screening of potential confounding variables revealed no significant effect on the body mass , carnivore (n = 26 species) or artiodactyl (n = 37 species) groups, or were restricted to mammalian species with ≥2 publications in the Global Mammalian Parasite Database  and incidence of cancer mortality  with species primarily eating vertebrates being under increased risk for both metrics. This was not the case for body mass  and life expectancy . Thus, diet was included as a confounding variable in all the subsequent analyses.The screening of potential confounding variables indicated that diet had a significant effect on the cancer mortality risk . Secondly, it is also possible that only few parasite species are actively involved in the hygiene hypothesis so considering the entire parasitic richness adds statistical noise and prevents us from detecting existing effects. While being a great resource, the data coverage of the global mammals' parasite database lacks the resolution to explore individual species of pathogens . It is not uncommon to observe spatiotemporal variability in cancer prevalence in wildlife populations ; data curation ; formal analysis ; investigation ; methodology ; project administration ; resources ; software ; validation ; visualization ; writing – original draft ; writing – review and editing . Jérémy Jeanjean: Validation (supporting); writing – review and editing (supporting). Orsolya Vincze: Data curation ; validation ; writing – review and editing (supporting). Mathieu Giraudeau: Writing – review and editing (supporting). Jean‐François Lemaître: Conceptualization ; validation ; writing – review and editing . Pascal Pujol: Writing – review and editing (supporting). Beata Ujvari: Funding acquisition ; writing – review and editing (supporting). Frédéric Thomas: Conceptualization ; funding acquisition ; project administration ; writing – original draft ; writing – review and editing .This work was funded by the MAVA Foundation and the following grants: ANR TRANSCAN (ANR‐18‐CE35‐0009), a CNRS International Associated Laboratory Grant and an Alfred Deakin Postdoctoral Research Fellowship (AMD). OV is supported by the National Scientific Research Fund (OTKA K143421) and by a grant of the Romanian Ministry of Research, Innovation and Digitization .The authors declare no conflict of interest.Data S1Click here for additional data file.


Endoscopic ultrasonography‐guided fine‐needle aspiration/biopsy (EUS‐FNA/B) is considered to be a first‐line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)‐based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS‐FNA cytology specimens.n = 33) and PDAC (n = 39) prepared using a liquid‐based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization  was used to visualize the regions of important classification features identified by the model.HSI images were captured of pancreatic EUS‐FNA cytological specimens from benign pancreatic tissues  when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF‐Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.An HSI‐based model was developed to diagnose cytological PDAC specimens obtained using EUS‐guided sampling. Under the supervision of experienced cytopathologists, we performed multi‐staged consecutive in‐depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC. Accurate pathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) poses a challenge for cytopathologists. Therefore, we aimed to develop a hyperspectral imaging (HSI)‐based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic cytology specimens obtained using endoscopic ultrasonography‐guided fine‐needle aspiration/biopsy (EUS‐FNA/B). A total of 1913 HSI images were obtained from 33 benign pancreatic tissue samples and 39 PDAC specimens, and the model was trained on these images. In this article, we developed an HSI‐based convolution neural network (CNN) algorithm and compared the classification accuracy for RGB images and HSI images. The aim is to improve the diagnosis of pancreatic EUS‐FNA/B cytology specimens prepared using a liquid‐based cytology (LBC) method and to increase and advance the knowledge of pathologists.22.1In order to construct deep learning model and evaluate its diagnostic performance, the archives of the department of cytology were queried for pancreatic FNA specimens accessioned between January 2020 and January 2022 at a single medical center . Furthermore, the pancreatic FNA specimens accessioned between November 2022 and March 2023 were also collected as additional test cases to prove the generalization ability of the model. The complete demographic data of all cases are displayed in Appendix 2.2The hematoxylin–eosin‐stained LBC slides were scanned with an Olympus DP73 camera (Olympus Corporation) scanner, and the images were analyzed by two pathologists who have more than 10 years of professional experience. According to the Papanicolaou Society of Cytopathology guidelines proposed by Pitman et al., pathologists classified cases as PDAC or benign and manually delineated the ROIs on these images for further collection of hyperspectral images.The image information of the labeled slides was collected for hyperspectral pathology imaging using a homemade medical hyperspectral imaging (MHSI) system at the Shanghai Key Laboratory of Multidimensional Information Processing, MHSI system is shown in Figure The transmission of optics in the microscope and the detection sensitivity of the CCD camera might interfere with the hyperspectral images and cause data redundancy and noise. We used principal component analysis to select the components with the largest amount of information as the reference band. We first manually marked the whole cell of benign and PDAC in the HSI images, and a blank hyperspectral image was used as the reference image to eliminate any electronic instrument noise. Then, the nucleus of benign and PDAC were extracted and segmented by using a few‐shot GAN network in order to obtain the manual annotation masks and generate the characteristic spectra of benign and PDAC cells.2.3We adapted the ResNet architecture that can be directly applied to analyze HSI images and RGB images and has good generalization abilities for benign pancreatic tissues and PDAC.In general, a two‐phase deep learning algorithm was required to process the data. In phase one, we used the blank HSI image as the reference image to eliminate noisy data.−05. The maximum training epoch was 200 for each model and the training step stopped when training loss of test set no longer decreased.The second phase involved designing suitable architectures to handle natural RGB images and spatial‐spectral data cubes that are distinct from natural images. In the process, a vanilla CNN‐like model was applied as our backbone by adjusting the first convolutional layer. Then, a multilayer perceptron was set to map the features extracted from the CNN model to the final prediction. We set the initial learning rate to 1eOn this basis, SimSiam algorithm was added into the HSI‐based CNN model to perform self‐supervised learning of image representations to further improve the model training efficiency and generalization ability. It achieved a higher accuracy without using negative sample pairs, when used in combination with ResNet architectures.2.4Attribution Guided Factorization Visualization  is a technique to visualize regions of important classification features that are identified by deep learning models.2.5To evaluate the classification performance of the CNN model, the primary outcome measures included sensitivity, specificity, false‐negative rate (FNR), false‐positive rate (FPR), precision, recall, and the smoothness of receiver operating characteristic (ROC) curves, area under the ROC curves (AUC). The samples were divided reasonably by selecting the appropriate threshold and calculating the corresponding precision and recall to indicate how many model predictions are actually correct. AUC‐ROC curves are used as performance measurements for classification problems at various threshold settings. The ROC is a probability curve, and the circle on the ROC curve corresponds to a point with a threshold of 0.5; the area on the lower right of the ROC curve represents the AUC, which measures the separability, with a larger AUC indicating better performance. All statistical analyses were performed using Sklearn package in Python software .33.1The aim of our study was to train a binary classification model to differentiate between benign pancreatic cells and PDAC cells. The study included 72 patients and the baseline characteristics are presented in Table 3.2The manual annotation masks of benign pancreatic ductal/acinar cells and PDAC cells were generated by using a few‐shot GAN network to process the pseudo‐color HSI images and the representative spectrum features of benign cells and PDAC cells are shown in Figure 3.3By utilizing these image patches, we compared the performance between the RGB‐based CNN and the HSI‐based CNN model. Among the expanded datasets for the construction of the model, 890 images obtained from PDAC cases and 829 images from benign cases. The scenes of HSI images and their matching RGB images were first separated into three independent datasets, 1341 scenes for the training set, 177 for the validation set, and 201 for the test. With repeated input of HSI data and RGB data into the multilayer CNN, the training, and validation accuracies of CNN models were improved. For the validation set, the accuracy of ResNet18‐RGB was 91.72%, which was lower than ResNet18‐HSI with an accuracy of 93.22% Table . For the3.4We further employed SimSiam algorithm for ResNet18‐HSI model to analyzing existing sets of data augmentation, and evaluated the diagnostic performance of the HSI‐based models and calculated their relative metrics. Table In addition, nine atypical cases were included in our study and five cases were eventually confirmed as PDAC. Our ResNet18‐HSI‐SimSiam model is also used to predict the benign or malignant outcomes of the atypical cases in all test sets. The accuracy of the ResNet18‐HSI‐SimSiam model for the images of the atypical cases was 83.02%, with its sensitivity and specificity were greater than 80% Table  and S3.3.5In order to prove the generalization ability of our ResNet18‐HSI‐SimSiam model, we used ResNet18‐HSI‐SimSiam model to classify the additional test set. For the additional test set, a total of 194 images were collected. Overall, the diagnostic accuracy of the ResNet18‐HSI‐SimSiam model was 92.27%, which was similar to the 92.04% accuracy of the test set. Notably, the value of AUC of our model on the additional test was 0.9683, and it achieved a comparable result to the AUC of 0.9625 on the test set Tables  and S4. 3.6Our ResNet18‐HSI‐SimSiam model could process the validation and test sets with an average speed of 0.144 s/frame. This translates to an approximate read rate of 6.94 frames per second.3.7The representative class activation maps generated by AGF‐Visualization are shown in Figure 4Accurate pathological diagnosis of pancreatic specimens requires professional cytopathologists with sufficient experience; moreover, the rising incidence of pancreatic cancer is increasing the workload of pathologists.In recent years, LBC in cytological diagnosis has become increasingly popular as it reduces debris/lubricant contamination and the presence of blood compared with conventional smear cytology.Owing to the improvement of deep‐learning technologies and computing power in recent years, AI has made remarkable progress in recognizing complex cytopathological images, and some technologies have matured into commercial products.Based on the above limitations, the development of an ideal CNN model that can accurately distinguish PDAC and non‐cancerous cases at the cytological level might help to improve the ability of cytopathologists to diagnose atypical pancreatic cancer cells. Therefore, we designed a proof‐of‐concept study that aimed to explore the potential of an HSI‐based AI model for cytologically diagnosing PDAC. We first used HSI images to generate the transmittance curves of benign and PDAC cells. These two curves seem similar and the major difference seeking to be that the PDAC curve is shifted up Figure . We hypoOur study contains several highlights, and the primary one is that this is the first study on the application of an HSI‐based CNN model for image analysis of pancreatic diseases at the cellular level. We found that the typical spectral features of benign pancreatic ductal/acinar cells and PDAC cells are obviously different in the wavelength range of 530–620 nm. Second, we confirm the superiority of the HSI‐based CNN model by comparing HSI images with RGB images which captured by traditional optical scanning. Meanwhile, it also supports that the spectral features obtained by hyperspectral imaging techniques are helpful in pathological classification and could provide more information for identification and differentiation. Third, our model possesses the advantages of high accuracy, sensitivity, and specificity. We conducted a patient‐based analysis, adopted cross‐validation , and achieved good results, which confirmed the effectiveness of the prediction model. Fourthly, our algorithm has excellent performance in image processing, with a reading rate of about 6.94 frames per second. This means that our model could be valuable in helping cytopathologists make more consistent diagnoses and reducing the workload. Overall, our model could support medical centers that lack experience in the diagnosis of cytological specimens of pancreatic lesions obtained using EUS‐FNA.However, our work also has several limitations that should be considered. The first is that our model could only differentiate between benign pancreatic cells and PDAC cells. It cannot identify other pancreatic diseases such as pancreatic neuroendocrine tumor, pancreatoblastoma, solid pseudopapillary neoplasms, pancreatic acinar cell carcinoma, intrapancreatic metastases from other primary tumors, and other rare solid pancreatic lesions. In a follow‐up study, we aim to improve the model by increasing the types of diseases that can be identified. The second limitation is the small number of cases in our study. However, this limitation was partly offset because we were able to obtain 1913 HSI and RGB images through data augmentation. The third is that our study was a single‐center retrospective study, and a well‐designed multi‐center study is needed in the future to further evaluate the new technology to ensure that the collected data are representative and could enhance the credibility of the experimental evidence.In conclusion, we developed and validated an HSI‐based model for diagnosing cytological pancreatic EUS‐FNA/B specimens. Under the supervision of experienced pathologists, we performed multi‐staged consecutive in‐depth learning of the model. AGF‐Visualization allow for human scrutiny to detect undesirable behavior in AI, and the increasing diagnostic performance of our model could help pathologists to identify PDAC and lay the foundation for further exploration of AI in this field in the future.Xianzheng Qin: Conceptualization ; data curation ; formal analysis ; investigation ; supervision ; validation ; writing – original draft . Minmin Zhang: Conceptualization ; data curation ; formal analysis ; investigation ; supervision ; writing – original draft . Chunhua Zhou: Conceptualization ; data curation ; formal analysis ; supervision ; writing – review and editing . Taojing Ran: Data curation ; formal analysis ; supervision ; writing – original draft . Yundi Pan: Data curation ; formal analysis ; supervision ; writing – original draft . Yingjiao Deng: Data curation ; formal analysis ; methodology (supporting); software ; validation ; visualization . Xingran Xie: Data curation ; formal analysis ; methodology (supporting); software ; validation ; visualization . Yao Zhang: Data curation ; formal analysis ; supervision . Tingting Gong: Formal analysis (supporting); supervision ; writing – review and editing . Benyan Zhang: Formal analysis ; supervision ; validation ; writing – review and editing . Ling Zhang: Formal analysis ; supervision ; writing – review and editing ; writing – review and editing . Yan Wang: Formal analysis ; methodology ; writing – review and editing . Qingli Li: Formal analysis ; methodology ; writing – review and editing . Dong Wang: Formal analysis ; supervision ; writing – review and editing . Lili Gao: Conceptualization ; data curation ; formal analysis ; supervision ; writing – review and editing . Duowu Zou: Conceptualization ; data curation ; funding acquisition ; investigation ; project administration (lead); resources ; supervision ; writing – review and editing .This work was supported by grants from the Science and Technology Commission of Shanghai Municipality (No. 21S31903500 and No. 21Y11908100).The authors made no disclosures.This study protocol was approved by the ethics committee of Shanghai Jiao Tong University School of Medicine (No. (2022) Linlun‐213th) and was conducted in accordance with the World Medical Association (Declaration of Helsinki). Written consent was obtained from each participant before enrollment into this study.Table S1.Click here for additional data file.Table S2.Click here for additional data file.Table S3.Click here for additional data file.Table S4.Click here for additional data file.Appendix S1.Click here for additional data file.


In this case report we present a very rare case of intramuscular cavernous hemangioma in the temporalis muscle which was successfully managed with surgical excision with no evidence of recurrence in follow‐up. Intra‐operative image showing the lesion in the caudal aspect of the temporalis muscle. The most common types of vascular malformations are capillary telangiectasias, cavernous malformations, venous malformations, and arteriovenous malformations.2A 51‐year‐old female was admitted to the oral and maxillofacial surgery department of a tertiary university hospital in August 2019, complaining about the presence of a mass in her left temporal area  scan of the lesion showed a well‐defined soft tissue mass in the temporal region with no bony erosion or invasion Figure . SubsequAfter acquiring informed consent, the patient underwent full surgical excision of the lesion under general anesthesia. Al‐Kayat Bramley incisionGross pathologic examination showed one piece of lobular encapsulated grayish‐brown soft to elastic tissue measuring 3 × 2.5 × 1.5 cm with variable‐sized cystic spaces filled by fragile brown material. Histologic examination of the lesion revealed a vascular lesion consisting of numerous large endothelium‐lined vessels with intraluminal papillary projections, sinusoidal, and anastomosing vessels in a fibrotic stroma. Intravascular thrombosis, adipose tissue, and patchy inflammatory cell infiltration were also seen Figure . There wThe patient was followed up periodically for up to 2 years with no sign of recurrence and no limitation in mandibular movements.3There are very few reported cases of intramuscular hemangioma in the temporalis muscle with the first cases dating back to the 1950s.Unlike the common subcutaneous types of hemangioma, intramuscular hemangioma is not present at birth, is usually detected in the second or third decades of life, tends to grow slowly and progressively, and does not show spontaneous regression. Although the masseter muscle is the most common site of intramuscular hemangioma of the head region,Hemangiomas are divided into five common categories based on their predominant histologic vasculature: capillary , cavernous (large vessel), arteriovenous, venous, and mixed.In a literature review by Alqahtani et al.These lesions can easily be diagnosed by MRI with typical hypersignal images on T2 and hypo‐to‐iso signal images on T1 sequences.Although many consider surgery as the best treatment option for intramuscular hemangiomas of the temporalis muscle,Following the experience with the present case and a few others reported in the literature, it is recommended to consider intramuscular hemangioma in the differential diagnosis of isolated pathologic lesions of facial muscles when radiologic and clinical symptoms are in accord with a benign neoplasm of vascular origin. Surgical treatment is generally successful with very low rates of recurrence.4In this article, we reported a rare case of intramuscular cavernous malformation in the temporalis muscle; an unusual site with very few cases reported in the literature. The patient was successfully treated with complete surgical excision of the lesion with preservation of the facial nerve branches. There is no evidence of disease recurrence or treatment‐related adverse events with 2 years of followMohammad Jafarian: Conceptualization; data curation; project administration; supervision; validation; writing – original draft; writing – review and editing. Mohammad Amir Alizadeh Tabrizi: Conceptualization; data curation; project administration; resources; writing – original draft; writing – review and editing. Fatemeh Mashhadi Abbas: Data curation; resources; supervision; writing – original draft; writing – review and editing. Zahra Sadat Torabi: Conceptualization; data curation; investigation; project administration; resources; writing – original draft; writing – review and editing.None.None.Our study was performed in line with the principles of the Declaration of Helsinki. The subject signed informed consent and patient anonymity was preserved.Written informed consent was obtained from the patient to publish this report in accordance with the journal's patient consent policy.