Studies have shown that people with intellectual disability (ID) show a greater severity of attention deficit hyperactivity disorder (ADHD) symptoms and atypical presentation, as well as having a greater risk of developing comorbidities, such as challenging behaviour, anxiety, tic disorders and sleep problems. It is estimated that 1.5% of patients with ID will have a clinical diagnosis of ADHD.The aim of this audit was to find whether individuals with ID and ADHD, who are prescribed medication for ADHD are adequately monitored and reviewed in accordance with the ADHD medication prescription guidance by NICE and the Royal College of Psychiatrists (RCPsych).This audit looked at ADHD medication prescription for the ID population within Greater Glasgow & Clyde NHS. This is the 6th audit cycle where electronic records (EMIS) were analysed between 28/9/19 to 09/10/20. (The 5th cycle data collection period ended on 28/9/19). We collected data on all patients aged over 18 years.An audit tool was developed to find whether the following were documented; patient demographics, physical health monitoring, symptom severity, medication dosage, side effects, need for ongoing treatment and frequency of review. 100% of patients should have all components on the ADHD audit tool documented, as per NICE/ RCPsych prescription guidance.32 patients were identified as being diagnosed with ADHD prescribed medication. One patient was impacted by the COVID-19 pandemic which meant that the required monitoring was not fully carried out. The age ranged from 18 to 56 years. 75% had mild intellectual disability, 19% had moderate and 6% had severe, with no cases of profound intellectual disability. Blood Pressure/pulse was recorded in 84% of patients. Height/weight/ BMI was recorded in 81% of patients. 97% of patients had ADHD symptom severity, medication dosage, side effects, need for ongoing treatment and frequency of review recorded.There is further scope for improvement in the monitoring and documentation of physical health observations, however there was a significant improvement compared to the previous cycle of the audit. Other aspects of monitoring and documentation appear to be recorded in almost 100% of patients. This finding emphasises the challenges of physical health monitoring and compliance in psychiatry as a whole. We need to continue to encourage awareness and education around the physical health risks to our patients, not only due to their comorbidities but also as a result of the psychotropic medications we prescribe them.


In the originally published version of this manuscript, there was an error in co-author Thomas K. Hoffmann's name. The full name should read: “Thomas K. Hoffmann” instead of “Thomas K. Hoffman”. This error has now been corrected online.


Arabidopsis thaliana, but studies in other species have not consistently shown the benefits of photons in the green region on leaf expansion and growth. Spectral effects can interact with total photon flux. Here, we report the effect of the fraction of photons in the blue (10 to 30%) and green (0 to 50%) regions at photosynthetic photon flux densities of 200 and 500 µmol m−2 s−1 in lettuce, cucumber and tomato. As expected, increasing the fraction of photons in the blue region consistently decreased leaf area and dry mass. By contrast, large changes in the fraction of photons in the green region had minimal effects on leaf area and dry mass in lettuce and cucumber. Photons in the green region were more potent at a lower fraction of photons in the blue region. Photons in the green region increased stem and petiole length in cucumber and tomato, which is a classic shade avoidance response. These results suggest that high-light crop species might respond to the fraction of photons in the green region with either shade tolerance (leaf expansion) or shade avoidance (stem elongation).The photon flux in the green wavelength region is relatively enriched in shade and the photon flux in the blue region is selectively filtered. In sole source lighting environments, increasing the fraction of blue typically decreases stem elongation and leaf expansion, and smaller leaves reduce photon capture and yield. Photons in the green region reverse these blue reductions through the photoreceptor cryptochrome in  In naturally shaded environments, irradiance/photon fluxes in the blue (400 to 500 nm) and red (600 to 700 nm) regions are relatively reduced while the fluxes of photons in the green (500 to 600 nm) and far-red (700 to 750 nm) regions are relatively enriched. In the photobiology literature, the term light is often used to refer to the photon flux, as in blue light, but this terminology does not describe the discrete nature of photons, which drive photobiological reactions. Additionally, light is closely connected to brightness in human perception of photons, thus photon is a preferable term. Here, the terms blue, green, red and far-red photons refer to photons in the regions that induce blue, green, red or far-red color perception.Plant developmental responses to a relative increase in far-red have been well studied ,2, with Elevated far-red commonly increases leaf area in controlled environments and this response is beneficial because it increases photon capture . IncreasLactuca sativa cv. “Waldmann’s Green” by 31% and increased shoot dry mass by 47% [Early light-emitting diode (LED) fixtures for horticultural applications supplied only blue and red photons. One of the first studies that investigated the effects of adding green to this type of spectrum found that increasing the fraction of green photons from zero to 24% increased leaf area in s by 47% . This eas by 47% ,7. Howevs by 47%  , suggesArabidopsis thaliana mutants have indicated that cryptochromes are the primary photoreceptor influencing the decrease in stem length [Cryptochromes are one of the two most well-studied families of blue photon receptors, and they primarily modulate plant growth through the control of gene expression. The other well studied family of blue photon receptors are phototropins, which primarily modulate plant growth through interactions with membranes ,10. Sunlm length ,12. Longm length . Phototrm length , but thiArabidopsis extend to these horticultural species. We review 29 studies spanning 19 years on the effects of blue photon fraction in lettuce, cucumber and tomato in Studies have typically found that increasing the fraction of blue photons decreases leaf area/plant diameter in the horticultural crops lettuce ,22,23,24o), which is the active form. Photon absorbance by FADHo induces conversion into the fully reduced (FADH–) state, which is inactive [o shows relative lower absorbance in the blue region and higher absorbance in the green region [– by photon absorbance [Unlike spectral distributions that lack either blue or red photons ,26,43, ginactive ,45. The n region . The fuln region ,48, but sorbance ,49. ThisArabidopsis thaliana [This hypothesis has been evaluated in multiple studies. Although increasing the green photon flux can induce shade morphology  in thaliana ,51, the thaliana  many oththaliana ,27,29,33thaliana , which iAdditionally, replacing red photons with green photons under a constant fraction of blue has increased leaf area and dry mass in some studies ,39,41,52The photosynthetic photon flux density (PPFD), or intensity, affects morphology. One notable example is that leaf thickness typically increases with increasing photon flux. Thick and thin leaves are referred to as sun and shade leaves. This response has recently been partially explained by the involvement of both cryptochromes and phototropins . In somePrevious studies have investigated some of the following interactions: (1) the effect of blue photons between 10 and 30% blue ,26, (2) Lactuca sativa, var. Red Salad Bowl), tomato  and cucumber  seeds were direct seeded then thinned for uniformity after emergence leaving four plants per module. Planted root modules were randomly placed into the 16 treatment chambers. Each chamber had dimensions of 20 × 23 × 30  with gloss white walls. Fans provided an air velocity of 0.5 m s−1 at the top of the canopy. The root modules measured 20 × 18 × 13 (4680 cm3) and contained a 1:1 ratio of peat and vermiculite by volume with five grams of uniformly mixed Nutricote ® slow-release fertilizer . Root modules were watered to 10% excess as needed with dilute fertilizer solution , and were allowed to passively drain. Type-E Thermocouples connected to a data logger  continuously monitored ambient air temperature at the top of the plant canopy. Day/night temperature was 23/20 °C, with less than 1 °C variation over time and 1 °C variation among chambers. CO2 concentration was continuously monitored and was identical for all treatments and varied over time between 450 and 500 ppm.Lettuce . Treatments were developed using LEDs  to output three white , three red/blue (RB) combinations, and two red/blue/green (RBG) combinations. The RB combination had about 10, 20 and 30% blue, and the RBG treatments contained about 10 and 20% B with 20 or 10% G, respectively. The spectral distributions of the treatments were measured before each replicate study with a spectroradiometer  and are shown in The system included 16 chambers with eight unique spectral outputs at two intensities for a 16 h photoperiod (PPFD: 200 µmol m2leaf m−2ground) was calculated by dividing total leaf area per chamber by the ground area of the chamber. Shoot dry mass (DM) was measured after the tissue was dried at 80 °C for 48 h. Dry mass per unit area (g DM m−2ground) was calculated by dividing total dry mass by the chamber area. Specific leaf mass  was calculated by dividing the total leaf dry mass of the four plants by the total leaf area of the four plants. The average stem and longest petiole length from each chamber were used for statistical analysis (four measurements averaged together).All species were harvested after canopy closure – when the leaves of the four plants in one of the treatments began to touch. This occurred 21 days after emergence in lettuce, 12, 13 and 20 days after emergence in tomato, and 11 or 13 days after emergence in cucumber. At harvest, stem and longest petiole length of the each of the four plants per chamber were measured in tomato and cucumber. Leaf area was measured using a leaf area meter . Leaf area index . In a mixed effects linear model, percent blue and percent green were treated as continuous variables while intensity was treated as a fixed effect. Replicates were treated as random factors. Interaction terms between these three factors were included in the linear model. The three-way interaction was insignificant for all parameters and was therefore pooled into the error term.The study was replicated three times (each with four plants per replicate in time). All data were analyzed using R statistical software . Blue, green and PPFD effects on the growth parameters in lettuce, cucumber and tomato were determined using lmer and Anova functions with an F statistic. We present significance at −2 s−1). This separation was also done in the analysis of the effects of green photons, and because green photons have been implicated in the reversal of blue photon effects, the data were further separated for 10 or 20% blue photons. The RB30 treatment was not included in this analysis, and both the cool and neutral white LEDs were considered about 20% blue.In order to understand the interactions, the effect of blue photons was also analyzed by separating the data by intensity (200 and 500 µmol mRepresentative photos of the three species in each treatment are shown with the percentages of blue and green in −2 s−1).Significant effects from the mixed effects linear model are presented in −2 s−1) resulted in an increased dry mass in all three species  compared to the lower intensity (PPFD = 200 µmol m−2 s−1) in all three species . These two chromophores (FAD and FMN) are structurally very similar, and as such they have similar absorbance properties for each of their oxidized, semi-reduced and fully reduced states . For cryve state ,45,61. Dve state . In a sive state , the revve state . Nonetheve state , and thuIn this study, both lettuce and tomato showed a significant decrease in SLM with an increasing fraction of green photons d,f. ThisPhotoreceptors may be saturated at higher photon fluxes, meaning that spectral shifts at higher intensity may have a smaller impact on plant morphology than at lower intensities. By contrast, preferential absorption of blue photons by pigments other than the blue photon receptors may minimize plant responses to lower intensities of blue photons. The latter scenario may be the case for the interaction between percent blue and PPFD in tomato SLM, as increasing percent blue at the higher PPFD increased SLM by 16%, but appeared to have no effect at the lower PPFD c. The inDespite these perplexing interactions, the overall response of SLM to an increase in the fraction of blue and green photons is generally consistent with the role of cryptochromes and phototropins.Mechanisms underlying leaf area are complex due to interactions with photosynthesis. Here, we review the potential role of phototropins and cryptochromes on the control of leaf expansion under blue and green photons.Arabidopsis thaliana mutants lacking phototropins have smaller curled leaves compared to wild-type plants [In a young seedling, blue photons contribute to early cotyledon expansion, and both cryptochromes and phototropins are involved in this response ,12. Matue plants ,66,67. TCryptochromes have been implicated in low blue shade avoidance responses like hyponasty , but thecry1cry2phot1phot2). Altogether, increasing the fraction of blue photons (perceived by cryptochromes) may both increase periclinal cell division at the expense of anticlinal cell division, and increase cell expansion along the abaxial to adaxial axis at the expense of elongation along the perpendicular axis. Considering the potential role of cryptochrome and phototropin in SLM, it follows that increasing leaf thickness through cell elongation in the abaxial to adaxial direction may decrease leaf expansion. Dougher and Bugbee  found thAlthough the mechanisms underlying the effect of blue photons on leaf expansion are not clear, cryptochromes appear to modulate the response. It is difficult to determine the role of phototropins in leaf expansion. Further research is warranted.The decreases in dry mass as the fraction of blue photons increased were likely caused in part by the lower quantum yield of blue photons compared to green and red photons. McCree  showed tIncreasing the fraction of blue photons decreased LAI in all three species, while increasing the fraction of green photons only increased LAI in tomato f. As dryDespite the early findings of Kim et al. , who shoArabidopsis thaliana, but the shade avoidance responses of stem and petiole elongation in horticultural species are often absent [Green photons have been shown to induce shade avoidance responses in n absent ,27,29,33n absent ,26,28,332 mol−1 for the activation of cry2 by 450 nm blue photons and 30 m2 mol-1 for de-activation by 560 nm green photons [The interactions between blue photons and green photons in predicting leaf area of lettuce and cucumber, and petiole length in cucumber can be explained by the sensitivity of cryptochrome to blue and green photons. Bouly et al.  measured photons . Photoco photons ). The re photons  indicateGreen photons only benefitted tomatoes. These data are contrary to the early findings of Kim et al. , who fouWe define shade tolerance as an increase in leaf area in response to shade-like light, and shade avoidance as an increase in stem elongation. Based on these definitions, tomatoes expressed both shade avoidance and shade tolerance in response to an increased fraction of green photons, while cucumber only exhibited shade avoidance.Blue photons typically decrease leaf area and dry mass. It therefore seems advantageous to include a low fraction of blue photons in a fixture. From our data, the lowest percentage of blue (10%) produced the highest dry mass. We are now studying the minimal amount of blue needed for normal plant growth.


Attention-deficit/hyperactivity disorder (ADHD) in adults is understudied, especially regarding neural mechanisms such as oscillatory control of attention sampling. We report an electroencephalography (EEG) study of such cortical mechanisms, in ADHD-diagnosed adults during administration of Test of Variables of Attention (TOVA), a gold-standard continuous performance test for ADHD that measures the ability to sustain attention and inhibit impulsivity. We recorded 53 adults , and 18 matched healthy controls, using 128-channel EEG. We analyzed sensor-space features established as neural correlates of attention: timing-sensitivity and phase-synchrony of response activations, and event-related (de)synchronization (ERS/D) of α and θ frequency band activity; in frontal and parietal scalp regions. TOVA test performance significantly distinguished ADHD adults from neurotypical controls, in commission errors, response time variability (RTV) and d′ (response sensitivity). The ADHD group showed significantly weaker target-locked and response-locked amplitudes, that were strongly right-lateralized at the N2 wave, and weaker phase synchrony (longer reset poststimulus). They also manifested significantly less parietal prestimulus 8-Hz θ ERS, less frontal and parietal poststimulus 4-Hz θ ERS, and more frontal and parietal prestimulus α ERS during correct trials. These differences may reflect excessive modulation of endogenous activity by strong entrainment to stimulus (α), combined with deficient modulation by neural entrainment to task (θ), which in TOVA involves monitoring stimulus spatial location (not predicted occurrence onset which is regular and task-irrelevant). Building on the hypotheses of θ coding for relational structure and rhythmic attention sampling, our results suggest that ADHD adults have impaired attention sampling in relational categorization tasks. N = 53), while they performed a gold-standard computerized performance test (CPT), Test of Variables of Attention (TOVA). We found that, compared with matched healthy controls (N = 18), ADHD participants showed reduced EEG indices of strength of rhythmic attention-sampling. The primary such index was parietal θ-rhythmic event-related synchronization (ERS), while other indices related to amplitude and phase synchrony of neural responses to stimuli. Characteristics of the task and brain-signal analysis suggest that ADHD participants may be deficient in relational processing, which (to our knowledge) has not been shown before.This study identifies one factor potentially contributing to difficulty of paying sustained attention among adults with attention-deficit/hyperactivity disorder (ADHD). We recorded electroencephalography (EEG) from a good-sized sample of adults with ADHD diagnosis (Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder, marked by persistent, age-inappropriate levels of inattention and hyperactivity/impulsivity, which persists into adulthood in approximately one third of those diagnosed . Here, wADHD is characterized by deficits in completing tasks that challenge long-lasting self-regulation of attention. One influential model says that ADHD is associated with deficient inhibitory control, which affects other executive functions such as working memory . Thus, sOne protocol that probes inhibition, inattention, and sustained attention is Test of Variables of Attention (TOVA), which has good external validity as it is widely used as a ‘gold standard’ to detect attention problems typical for ADHD. TOVA is a computerized performance test (CPT) with a monotonous hybrid Go/NoGo target-classification task design : particiIn the scientific literature, there is an ongoing search for aberrant neurocognitive mechanisms leading to ADHD symptomatology. Observed deficits in attention, inhibition, and working memory suggest that activity in the frontal cortico-striatal network and frontoparietal attention network (FPN) are altered in ADHD , and mayCortical oscillations play a fundamental role in brain networks  and haveIn general, α oscillations in task-relevant areas tend to reduce in amplitude  during effortful cognitive processing . The funIn contrast to α, θ amplitudes tend to increase with increasing cognitive effort and mental fatigue , which mOscillations can become entrained to predictable stimulus onsets, i.e., oscillatory phase becomes more likely to coincide with the expected stimuli . This enStudy of oscillatory neural mechanisms requires high time-resolution, magneto- or electroencephalography (M/EEG) recording. However, a sudden-onset trial-based task like TOVA presents the challenge of analyzing oscillations in the presence of strong evoked signal power, i.e., power locked to stimulus onset phase. However, because TOVA presents stimuli periodically, with little variation in visual characteristics of each condition, we expect that a visible predictive processing  based enHere, we aim to study behavioral performance and EEG-recorded evoked potentials and cortical oscillations during TOVA, comparing adults with ADHD to healthy controls. In particular, we examine how frontal and parietal neural correlates of visual attention differ between groups throughout TOVA. We focus on the following RQs:RQ1: How do ADHD and control groups differ in EEG amplitude response to targets during TOVA?RQ2: How do groups differ in phase synchrony of EEG amplitude to TOVA trials?RQ3: How do groups differ in the temporal dynamics of frequencies linked to attentional regulation, α and θ?RQ4: How do neural correlates of attention evolve over the duration of TOVA?To address our RQs, we examine frontal and parietal regions of interest (ROIs), using time-frequency representations (TFRs), event-related potentials (ERPs), and phase-synchrony measures. We test interactions of group with TOVA trial conditions (response to target vs inhibit response to nontarget) as they vary across TOVA test conditions [first half (H1) infrequent vs second half (H2) frequent target mode]. Data measurement consisted of visual TOVA CPT with EEG recording, gathered as part of a larger project detailed in . An ethin = 44) or ADD (n = 9), we refer to both as ADHD group, and a healthy control group of 18 adults  with no diagnosed neuro-cognitive deficits or ongoing medication for ADHD/ADD. The healthy controls were significantly more likely to have TOVA performance within normal limits compared with ADHD adults (as detailed in Results). All participants had normal or corrected-to-normal vision. The groups did not differ in terms of age, gender, or handedness. Because of reasons of data quality (detailed below) a number of participants were dropped from EEG analysis for different TOVA conditions. The final analyzed number of participants were: ADHD inhibition H1 41, H2 31; response H1 39, H2 37; control inhibition H1 15, H2 11; response H1 14, H2 13. The control group size was always at least 35% of the size of the ADHD group, and thus the statistical power of our tests is not substantially affected by the between-group size disparity .We recruited 53 adults  = 36.26, SD = 10.22) diagnosed with either ADHD  preexisting diagnosis of ADHD/ADD, (2) no neurologic diagnoses, (3) age between 18 and 60, (4) scores on Adult ADHD Self Report Scale ASRS;  and BrowF = 5.01, p < 0.030, r2 = 0.09). As expected, participants with hyperactive-impulsive (ADHD-HI) or combined (ADHD-C) diagnoses had higher hyperactivity-impulsivity scores  than those with an inattention (ADHD-I) diagnosis . There was no significant difference in the ASRS inattention scores between groups, and most analyses treat all ADHD participants as one group, except where noted below.Within the ADHD group, there was a small difference in the ASRS hyperactivity-impulsivity scores depending on the diagnosis . They were also advised not to take any other stimulants immediately before the measurement  and to arrive as well rested as possible to the measurement.Karolinska sleepiness scale KSS;  was emplWe administered TOVA visual version 8, consisting of the test software, USB relay hardware, a low-latency microswitch hardware response button, and Synchronization Interface hardware for test-to-EEG amplifier synchrony .TOVA presents a target and a nontarget stimulus, shown in TOVA has two consecutive conditions: infrequent and frequent target mode. During H1, the target stimulus appears infrequently . During H2, these frequencies are reversed . Participants are not informed about this transition. Each stimulus is presented for 100 ms and separated from the next stimulus by a 2000-ms interstimulus interval (ISI). The anticipatory cutoff time from stimulus onset is 150 ms, meaning that responses given 0–150 ms after stimulus onset are considered invalid. The duration of TOVA is 21.6 min, each condition lasting 10.8 min.N = 1596), this implies that behavioral variables do not represent, e.g., actual error but rather the difference from a “normal” value for error  to false alarms (commission errors). One participant who showed strong evidence of symptom exaggeration  was excluded from further analyses.Five standard scores are available: (1) mean response time (RT): mean of correct RTs in milliseconds (ms); (2) RT variability (RTV): SD of mean correct RT; (3) commission errors: incorrect responses to a nontarget stimulus; (4) omission errors: incorrect responses to a target stimulus; and (5) dFor normative comparison, TOVA is advised to be measured between 6 A.M. and 1 P.M. Our group-average measurement times were close to or within the normed hours: ADHD M = 1:45 P.M., SD = 2 h 26 min (35 after 1 P.M.); control M = 12:49 P.M., SD = 3 h 2 min (7 after 1 P.M.), and the starting times did not statistically differ between the groups.EEG was measured using Biosemi ActiveTwo equipment with 128 active electrodes mounted on a cloth headcap with equiradial positions. Electrode positions are labeled in text according to the closest International 10–5 electrode placement system label . Active https://doi.org/10.6084/m9.figshare.13614446.v1).During the TOVA, participants were advised to relax and sit as still as possible, avoiding excessive movements, and to fixate the middle of the screen shown as a small white dot between stimulus presentations. Three participants were excluded because of technical problems with event codes in the EEG data  starting from 400 ms before stimulus onset to 600 ms after stimulus onset, were rejected. After this, too few error trials remained to conduct statistical analyses: on average, controls had seven and ADHD nine trials for commission errors; controls had two and ADHD six trials for omissions. Thus, we analyzed only the correct (inhibition and response) trials.To counterbalance fluctuations in voltage offset across epochs and participants, baseline correction was applied to each epoch by subtracting the baseline mean amplitude from the whole epoch. For those data where the analysis concerned prestimulus phase, baseline period was from 1000 to 900 ms before stimulus onset. For all other analyses, baseline period was −1000–0 ms.Epochs were divided between correct responses and inhibits, in H1 and H2, to give four datasets per group for most analyses (see Results below for details of exceptions). For each of these four conditions, a participant was only included if they had at least 36 epochs for infrequent conditions (responses H1 and inhibits H2) or 72 epochs for frequent conditions (inhibits H1 and responses H2). This resulted in the participant numbers given above, and ensured each participant contributed 108 clean epochs per each test half . The epochs were randomly sampled without replacement from the total available per condition, on a participant-wise basis. In order to test that this sampling did not bias results, we conducted 11 similar draws and estimated variance across draws of the spectral power , the final mean variance was only 0.015.The number per participant was fixed to provide balanced data and help ensure that any observed effect would be because of altered neural processing, not merely because of possible differences in behavioral performance . In order to validate that any group differences in button presses would not bias neural results, we also tested the behavioral data, as reported in Results, and found no evidence of bias.Analyses focused on two ROIs (denoted using 10–5 system labels mapped from the Biosemi 128 channel): frontal at F4, F4h, Fz, F3h, F3, AFF4h, AFz, and AFF3h and parietal at P3, P1, CPPz, P2, P4, PO3, POz, and PO4. These locations are illustrated below. The electrode locations were chosen a priori based on previous research , but modERP images, displaying trials individually by visualizing amplitude with color, were used to show response-sorted activation within frontal and parietal ROIs (see section RQ1: response amplitude). These images were generated by ordering trials according to their RT value, and smoothing across trials with a moving Gaussian of width proportionate to the number of trials . Data were twice re-epoched to 1-s windows containing target and response, in one case target-locked and the other response-locked. Corresponding ERP waves were computed twice: adjacent to the ERP image are plotted the grand average ERPs of trials with RT less than, and greater than, median RT. Below the ERP images are plotted comparisons of grand-average ERPs for control and ADHD, with amplitude testing shown .We also plotted phase-sorted ERP images and intertrial coherence ITC; see . Both thPhase-sorted ERP images were computed for target and nontarget trials, in both ROIs, and show only the first 400 ms poststimulus (where coherent waves are prominent). Data were further subset (by random sampling without replacement) to ensure that each condition had a proportionate number of trials see A,B. PhasWe used EEGLAB to compute 10 Hz α parietal ITC, separately for the two TOVA halves (H1 and H2) and two groups, shown in In order to assess how the EEG spectrum changed from prestimulus to poststimulus, oscillatory power was calculated separately within prestimulus and poststimulus periods, using Welch’s power spectral density estimation of the mean log power spectrum via EEGLAB’s spectopo function see . The FFTTo examine the effect of TOVA condition on prestimulus activity, α power was also calculated for the 500-ms prestimulus period preceding correct trials for TOVA halves H1 and H2, in the parietal ROI. All correct trials (both correct inhibition and correct response) were combined for this analysis, since the focus was prestimulus. Within these correct trials, prestimulus power was calculated for each 1-Hz frequency bin separately within the extended α band (8–12 Hz).To examine event-related oscillatory power dynamics for correct inhibition and response trials, we calculated three types of TFR: event-related spectral perturbations ERSP; , evoked A TFR is obtained by calculating spectral power across an epoch, for multiple frequencies. ERSP is defined as the average of TFRs computed for each separate trial, and is sometimes referred to as the total power. Evoked power is the TFR of the time-domain average of all epochs . Induced power is defined as the total power minus the evoked power (intended to capture nonphase locked activation), but various methods to calculate it have been discussed see : here, wTo obtain these power estimates for ROIs, we created custom MATLAB code (based on EEGLAB’s newtimef function) to calculate TFR matrices for each electrode individually and take the mean of all electrodes to obtain the spectral power estimate reported below. This preserves estimates of spectral power from separate electrodes, as opposed to averaging channels first and computing TFR for the average, which may underestimate spectral power for electrodes at different phases.Spectral power was calculated in 54 frequencies from 4 to 30 Hz, in 200 time points (−583–581 ms), using Morlet wavelets with scaling factor 0.5 . Adjusting the wavelet scaling factor along with frequency aimed to balance the trade-off between temporal and spectral resolution. Note, the design of TOVA trials constrained the ERSP calculation to start at 4 Hz, since lower frequencies returned very narrow windows.Our results  TOVA software was not configurable. Although we obtain some insight from comparing H1 and H2 results, these should be taken with due caution. We thus analyzed the effect of time within-halves, by splitting the datasets for each condition (H1 vs H2 × response vs inhibit) into “early” and “late” trial subsets of equal size .′ . These were followed up by further DV-specific analyses, which are detailed in the Results. We also assessed how well behavioral performance in TOVA differentiates between ADHD diagnosed adults and healthy controls.Behavioral performance was analyzed using a 2 × 2 two-way mixed design MANOVA, with group (ADHD vs control) as a between-subjects factor and TOVA condition (infrequent vs frequent) as a within-subjects factor. The DVs were mean RT, RTV, commission errors, omission errors, and dEffect sizes for repeated measures were computed as partial η squared. Multiple comparisons were adjusted with Bonferroni correction, keeping the α at.05. Possible extreme outlier values were assessed by examination of studentized residuals for values greater than ±3. We do note, however, that excluding outlier scores in a clinical sample is not straightforward, as high behavioral variability itself has been reported as a clinical characteristic in adults with ADHD . In totaIn order to validate that the neural data analyses would not be confounded by relative variation in motor processing activations, because of any observed between-group difference in amount of button presses , we conducted the following analysis. We tested whether there was a difference in total number of button presses (= correct target presses + commission errors) between the treatment groups or test quarters by fitting a robust linear mixed model (LMM) with total presses as the DV, and treatment group , test quarter (Q1–Q4), and their interaction, as the predictors. A numerical participant ID was used as a random factor, allowing variability in the intercepts but not slopes. Robust LMM  was usedStatistical analyses of EEG were run in MATLAB (version 9.7), using EEGLAB toolbox  and in Rα = 0.05 level.Group differences in correct-response trial ERP waves were tested using two-sample Kolmogorov–Smirnov test of the mean amplitude within two time windows per trial: 150–250 and 330–430 ms in target-locked, and −170 to −70 and 0 to 100 ms in response-locked trials see  was usedWe computed statistical significance of the ITC using EEGLAB’s permutation-based testing of single-trial spectral estimates across latencies , with deWe analyzed the differences between baseline and poststimulus periods in their log-transformed power spectral density (log-PSD) at 4- and 8-Hz frequency bins  and α = 0.0005 (2000 permutations), to illustrate a robust test statistic.The group difference of ERSPs was tested for statistical significance using permutation tests , so degrees of freedom were corrected using Greenhouse–Geisser estimates of sphericity to counteract the inflation of Type I errors.A 2 × 2 mixed ANOVA was calculated to examine whether there was a group (ADHD vs control) × TOVA condition (frequent vs infrequent) interaction in parietal prestimulus α power on correct trials. Mauchly’s test indicated that the assumption of sphericity had been violated for this data , test half , time within test half , and group . We used Bayesian LMM  with loghttps://doi.org/10.6084/m9.figshare.13614446.v1.We first describe results from our behavioral analyses, then EEG results. In addition to what is reported in this section, we provide open access to the background details of our analyses  at 2 (exact two-sided) test of independence (χ2(1) = 7.66, p < 0.007).Normed TOVA performance strongly distinguished diagnostic groups . Healthy controls were significantly more likely to have TOVA performance within normal limits compared with ADHD adults, as revealed by Pearson χF = 2.63, p < 0.032, η2 = 0.177). That is, the impact that TOVA condition (H1 and H2) had on performance  depended on whether participants belonged to the ADHD group or controls. Thus, we followed up by examining the simple main effects of group and TOVA condition. Behavioral results are illustrated in In group-wise behavioral analysis (MANOVA), there was a significant TOVA condition × group interaction  < 1.5, ps > 0.2). We then ran univariate one-way ANOVAs separately for all DVs instead of using a multivariate approach, again separately for H1 and H2. Within H1, there was a significant between groups difference (Bonferroni-adjusted for multiple comparisons) in commission errors  = 4.12, p < 0.046, η2 = 0.059) and in d′  = 5.30, p < 0.024, η2 = 0.074): the ADHD group tended to produce more commission errors and had a worse d′ (the ability to discriminate between targets and nontargets) than the control group. Within H2, the ADHD group had significantly greater RTV  = 4.52, p < 0.037, η2 = 0.064).First, we evaluated the difference between ADHD individuals and controls at the two TOVA conditions (H1 and H2) separately, by running two one-way MANOVAs for H1 and H2. The simple main effect of group was not significant in either of these models  = 8.96, p < 0.0005, η2 = 0.505) and control group  = 7.63, p < 0.002, η2 = 0.746): both groups performed worse toward the H2 of TOVA. As before, we ran univariate repeated measures ANOVAs for each DV separately. Within the ADHD group, we found a significant effect of TOVA condition on commission errors  = 26.08, p < 0.0005, η2 = 0.352), omission errors  = 11.15, p < 0.002, η2 = 0.189) and d′  = 20.16, p < 0.0005, η2 = 0.296): within the ADHD group performance deteriorated during the second TOVA condition across all measures. Within the control group, however, there was a significant effect of TOVA condition only on commission errors  = 11.58, p < 0.003, η2 = 0.405), and d′  = 8.67, p < 0.009, η2 = 0.338).Next, we evaluated the difference between H1 and H2 (the difference in performance between the two consecutive conditions) separately for the ADHD and control groups, by running two repeated measures MANOVAs. There was a significant simple main effect of TOVA condition in both the ADHD (ts < 1.5). Thus, there was no difference in total number of button presses between the treatment groups .In the analysis on differences in total number of button presses, the main effect of treatment group (ADHD vs control), and the interaction between treatment group and test quarter were not statistically significant , and in pre-RT windows in all response-locked conditions.Statistical testing of mean amplitude in windows , shows groups are significantly different in all target-locked conditions  response-locked window covering 0−100 ms post-RT is never significantly different, contrasting with the strong effect in the second target-locked window. The median-split ERPs below each ERP image explain this: in target-locked data, the ERP of the RT <median trials (solid lines) is comparable between-groups, but for the ERP of RT >median trials (dashed lines), ADHD lags control and is of smaller peak amplitude. In response-locked data, the median-split ERPs are more similar between-groups.Finally, the full set of ERP images clearly illustrate a notable group difference: control participants experience much stronger phase-resetting: from early waves  before 200 ms, to the P3 preceding RT.Topographic scalp maps in The picture on phase-resetting is clarified by the α phase-sorted ERP image plots in The overall control group phase reset response is clearly stronger compared with ADHD. Stimulus-locked waves beginning after 100 ms are higher amplitude . By splitting the adjacent ERPs at median phase, we see the prestimulus waves in opposite phase, processing to alignment at some point after stimulus onset. The groups differ in when alignment is complete : in most conditions, controls see alignment of median phase-split ERPs before stimulus offset at 100 ms, while for ADHDs alignment happens one whole cycle later, after 100 ms.For RQ3 we first examined the log-transformed power spectral density during prestimulus and poststimulus periods of 1 s. There is a clear baseline peak, 10–11Hz parietally but close to 8 Hz frontally. In the poststimulus period, the power shifts to lower frequencies, prominent at 4 Hz. The pattern is quite similar between groups, although more pronounced for the controls.F = 12.2, p < 0.001) and frequency  were statistically significant; the interaction between Condition and frequency was also highly significant , showing that log-PSD was higher during the poststimulus phase only at the 4-Hz frequency, and that at 8 Hz the order was flipped with higher log-PSD observed during the Baseline period. These findings were robust to controlling for ROIs .The Bayesian LMM analysis found that the main effects of condition , induced, and evoked power, The evoked power plots (third row of each panel) illustrate the phase-locking difference between ROIs, as parietal ERS (0−200 ms) is much more synchronized across frequencies than frontal ERS. The groups do not differ substantially in this , nor does evoked power show any systematic group differences.While the ERSP and induced power plots are very similar, the mean power line plots show how the ERSPs contain some part of the evoked power. The ERSPs, especially parietally, show the temporal extent of baseline 8-Hz spectral power shifting to 4 Hz poststimulus. Thus, next we examine ERSPs for each condition.p < 0.05, and full-color blobs are different at p < 0.0005 . Color in the third panel represents control ERSP minus ADHD ERSP, thus blue tones indicate ERS for ADHD.Main patterns of difference include: ADHD group has higher prestimulus α ERS (appearing blue in the difference plots) centered on 10 Hz frontally and 12 Hz parietally, all conditions; control group has higher 8-Hz θ ERS parietally in inhibition trials; and control group has poststimulus bursts of 4-Hz θ ERS across all conditions, varying slightly in timing.For inhibition trials, the between-groups differences in poststimulus θ is ∼1–2 dB in magnitude. Parietally the difference begins ∼100 ms after stimulus onset, across the θ band from 4 to 8 Hz, so is clearly driven by evoked power. However, this broadband power difference quickly fades while the 4-Hz difference continues at least for 400 ms. The differences in frontal θ arise later at ∼300 ms and are not broadband, suggesting frontal evoked power influence is similar between groups.The group-wise ERSPs (rows 1 and 2 in each panel) indicate the pattern generating the differences: in all conditions the control group shows a larger initial response at all θ frequencies, which is then sustained at 4 Hz for inhibition trials. Parietal inhibition trials demonstrate the effect most clearly.F = 2.35, p < 0.014, η2 = 0.162). The between-groups difference lay between control and ADHD-I groups at 10–11 Hz (Bonferroni adjusted p < 0.05), wherein the ADHD-I group’s oscillatory power was twice as high  as for the control group . The effect was controlled for outliers defined as studentized residuals exceeding ±3. Thus, trait inattention is related to excess prestimulus α.To clarify the role of parietal prestimulus α, seen in the ERSPs, our analysis for ADHD versus control showed no significant main effect of group. However, when we split the ADHD group into diagnostic subgroups: ADHD-HI/C and ADHD-I, and applied a 2 × 3 ANOVA, we found a significant but small main effect of group on parietal α power  and frequency , were trivially expected given the nature of the data. Of greater interest, the interaction between test half and frequency was also significant , showing that the difference in log-PSD between test halves (H2 higher than H1) was prominent only at the 10-Hz frequency.The analysis of time within test half found no significant effects of early versus late data. Significant main effects of test half  they had more variable RTs than controls, suggesting deficient prediction of target onset in line with In terms of behavioral performance within halves, the ADHD group was affected by task demands in line with expected deficits. During H1 (infrequent targets testing inattention), the ADHD group had more commission errors and a lower d′ (possibly because of reduced vigilance) and increased commission errors (possibly because of fatigue). However, for the ADHD group the amount of omission errors also increased during H2, possibly because of mind wandering as suggested by the cognitive-energetic model of ADHD  even in H1. Thus, the deterioration of performance over TOVA halves appears a normal consequence of fatigue, brought on by paying sustained attention over an extended period of time.Taken together, the results of RQ1–RQ4 indicate that in TOVA, ADHD participants were mainly affected by trial-wise deficits, and not a deficiency of long-term neural energetics , this miSynthesizing all observed results, we propose an interpretation based on deficient rhythmic attention-sampling in a cortical area responsible for relational processing, the posterior parietal cortex (PCC). This may be inferred from the nature of the TOVA task itself, as follows.The TOVA task is temporally regular, such that every trial has predictable onset. This can be exploited to study ADHD by comparing behavioral and neural responses to neurotypical responses which have been facilitated by predictability. Our data suggest the ADHD behavioral deficit arises from a dual difference to controls in θ and α. θ Is reduced parietally at 8-Hz prestimulus and band-wide stimulus-locked, and fronto-parietally at 4 Hz from 200 ms on; α, by contrast, is increased fronto-parietally in the prestimulus period, especially in H1.We suggest these results are possibly because of weaker endogenous attention sampling, specifically, in line with the theory that attention continuously samples attended locations at an 8 Hz base rate . This thFurther, TOVA is a relational classification task: Go/NoGo (respond/inhibit) to targets/nontargets, which share the same stimulus properties , at two different spatial locations (above-below fixation). This relationship between target types permits a unidimensional encoding, and θ has indeed been proposed to facilitate relational encoding . Based oFinally, the observed difference in 4-Hz θ ERSP frontally suggests an involvement of executive function, which gets stronger from H1 to H2. In Our ultimate hypothesis is that our ADHD group show deficient θ-rhythmic attention sampling, linked to the specific function of sampling relationships in the world. Interesting converging evidence was presented by  for adulOne question that arises is the following: why was the significant difference in between-groups ERSP to response trials much weaker? If we draw again on the Alternative explanations are also possible. For one thing, primate data  supportiSince the aim was to study the neural correlates of TOVA in particular, we were thus bound to the particular design of that test, which has some atypical features compared with other lab tasks for attention: notably the fixed task structure and periodic stimulus presentation. As such, there were a number of limitations, both in the sense of constraints and issues.The foremost constraint on analysis is that time dependency is confounded by the fixed order of TOVA tasks in H1 and H2, which are not counterbalanced. Of course, such bursty temporal structure is quite natural and of interest ; howevern = 4), might have increased the number of false positives. In the present study, 19 participants (of which 15 with ADHD) belonged to this cohort. The low sample sensitivity and specificity highlight the issue of behavioral heterogeneity among people diagnosed with ADHD, and further motivates our sub-group analysis which found elevated parietal 10 Hz α among the ADHD-I group (compared with controls).The small size of some of the TOVA norming groups, such as males aged 30–39 , that is, the single frequency in the α band showing the highest power per individual. Counting iAPF as the central frequency of α, instead of 10 Hz, can be used to recalibrate all frequency bands and potentially account for clinical variation in EEG spectra . HoweverThe neuropathological heterogeneity of ADHD implies that distinct neurocognitive subtypes of ADHD may exist, and yet not map to symptomatic subgroups. Because of this, one criticism of ADHD studies has been that group-wise comparisons between ADHD participants and healthy controls are likely to yield small, hard-to-replicate effects . While tIn conclusion, this study is among the first of its kind combining TOVA with an EEG measurement in adults  – and thBoth the clinical and scientific pictures of ADHD remain complex and multidimensional . Because


In India, the high neonatal and infant mortality rate is due in part to an increasing number of preterm and low birth weight (LBW) infants. Given the immaturity of immune system, these infants are at an increased risk of hospitalization and mortality from vaccine-preventable diseases (VPDs). In this narrative review, we screened the scientific literature for data on the risk of VPDs, vaccination delay and factors related to it in Indian preterm and LBW infants. Although routine childhood vaccinations are recommended regardless of gestational age or birth weight, vaccination is often delayed. It exposes these infants to a higher risk of infections, their associated complications, and death. After-birth complications, lack of awareness of recommendations, vaccine efficacy and effectiveness and concerns related to safety are some of the common barriers to vaccination. Awareness campaigns might help substantiate the need for  vaccination in preterm and LBW infants. What is the context?In India, the high neonatal mortality rate is due in part to an increasing number of pretern and low birth weight intants.Affected infants have a poorly developed inmune system and are more susceptible to contracting vaccine-preventable diseases.The Indian Academy of Pediatrics recommends vaccination according to the same schedule used for full term infants, following chronological  age.Delays in vaccinations increase the risk of preventable infections.What is new?infections have more serious conseuences in preterm and low birth weight infantsdelays to vaccinate affected infants are common, mostly due to safety and effectiveness concerns from parents and healthcare pracitionrs.Our review of the scientific literature shows that, in India:What is the impact?Improving mternal nutritional status and immunization, and perinatal care could help reduce the number of preterm and low birth weight infants.Combining maternal immunization with vaccination of affected infants can confer safe and effective protection.Awareness campaigns for parents and healthcare practitioners could address the issue of vaccination delay in pretern and low birth weight infants in India. Among these countries, about 33% of deaths were reported in Nigeria and India alone.17Increasing numbers of preterm and LBW newborns every year could add to the disease burden on healthcare systems and individual families, depending on the setting.18 Of these, 0.57 million were neonatal deaths in which the reported causes were preterm birth (27.7%), encephalopathy due to birth asphyxia and trauma (14.5%), lower respiratory infections (11.0%), congenital birth defects (8.6%), sepsis and other infections (6.1%), hemolytic disease and jaundice (3.2%), diarrheal diseases (2.7%), tetanus (0.7%), other disorders (22.0%), and other causes (3.5%).18 This situation is alarming as India accounts for 23.4% of the global preterm births.14 Estimates of LBW infants are notable: during 2013–2014, amongst approximately 19 million newborns,19 68.7% were weighed at birth and among these, 18.6% were LBW .20In 2017, India recorded approximately one million deaths  among children under 5 years of age.17 The WHO and the Advisory Committee on Vaccines and Immunization Practices of the Indian Academy of Pediatrics (IAP) recommend that all infants receive immunization, regardless of any restrictions based on gestational age or birth weight, with the qualified exception of the hepatitis B vaccine as the birth dose is not counted toward the full schedule due to a reduced immune response.21–23The majority of deaths in children under 5 years of age and morbidity associated with infectious diseases can be averted by timely interventions including adequate nutrition, clean water, appropriate maternal care during pregnancy and immunization of the mother and infant.47–50 The situation is unlikely to be different in India, as a high level of vaccine-preventable disease (VPD) burden in infants or children persists.51 Within this context, there is a need to better understand the factors and barriers related to the absence or delay in vaccination among preterm and LBW infants. This information could help bridge existing knowledge gaps in the scientific community, specifically among healthcare providers (HCPs) who are perceived as the most trusted advisors and influencers of vaccination decisions.52Despite the existence of vaccination recommendations, several studies in high-income countries have reported either a significant delay or a complete lack of immunization in preterm infants.53 However, information on the extent of vaccination delay in preterm and LBW infants has not been previously summarized. In this review, we outline the rationale for immunization and highlight the risks of VPDs in preterm and LBW infants. We also provide an overview of recommended vaccinations, with a focus on whether efficacy/effectiveness and safety data are available in these populations. Lastly, we present the caveats linked to different vaccination strategies that could be utilized to mitigate the burden of VPDs in preterm and LBW infants in India. A recent publication summarizing practical issues surrounding vaccination in preterm infants lends support to the implementation of existing vaccination recommendations for preterm and LBW infants in India.54 This immune system immaturity is amplified in infants born preterm and in those with LBW, due to several deficiencies  and then innate immune response rather than adaptive immune response. At birth, both immune defense mechanisms are immature.ciencies .Table 2.55 Compared to full-term infants, this barrier is undeveloped in preterm and LBW infants, making it susceptible to ruptures and therefore serving as an inefficient defense barrier.55 Furthermore, antimicrobial peptides-producing flora are reduced in number within the mucosal barrier of the respiratory and gastrointestinal tracts, thus facilitating the penetration of pathogens and increasing the risk of infection.57Defense against pathogens consists of physical barriers, such as keratinized skin and mucous membranes lining the respiratory and gastrointestinal tracts, and chemical barriers containing various enzymes and other substances that elicit a direct antimicrobial action or inhibit microbial adherence to body surfaces.58 Similarly, a smaller pool of monocytes is available in preterm and LBW infants. Monocytes are capable of phagocytosis, secretion of cytokines or chemokines and antigen presentation, and regulate the activation of B-cells and T-cells, which are integral parts of the adaptive immune response.56 Consequently, preterm and LBW infants are at a high risk of infection  and peptides facilitate phagocytosis and elicit antimicrobial properties. The production of soluble proteins by the fetus is limited and thus adaptive immunity is mostly provided through maternal antibodies. Maternal IgG antibodies are transferred to the fetus starting at approximately 17 weeks of gestation, with cord-blood IgG levels similar to maternal titers after 32 weeks of gestation and up to 2-fold higher at term birth.59 This aspect is depicted in 62 influenza,68 invasive pneumococcal disease,39 bacterial meningitis,66 pertussis,69 bacterial and viral pneumonia,66 rotavirus gastroenteritis67 and tetanus.63 Importantly, the literature suggests that an increased risk of infection positively correlates with the degree of prematurity and LBW.70 Specifically, infection of the very and extremely LBW infants with opportunistic and aggressive multidrug-resistant pathogens often results in death.70Newborns usually contract infections either in the perinatal or the postpartum period. Exposure to infections is especially critical in preterm and LBW infants because of their immature immune system and inadequate levels of maternal antibodies.47–50 Due to this, the risk of complications and mortality from preventable infections is multiplied as the susceptibility window to infections is increased from the time of birth.69 Vaccination delay or refusal of vaccines for preterm and LBW infants appears to be a prevalent issue in India as documented from several studies.71–73 In these studies, delays in timely vaccination for each vaccine was defined as administration of the vaccine dose after 28 days of the minimum recommended age, meaning that vaccination was categorized as delayed if given on day 29 or later for Bacillus Calmette-Guerin (BCG), 71 days or later (after 10 completed weeks) for diphtheria-pertussis-tetanus (DPT)-first dose (DPT-1) and for DPT–third dose (DPT-3), when the infant was vaccinated at >18 weeks of age.71–73 For measles, delayed vaccination was defined as having received the vaccine after 4 weeks of recommended/due-time, i.e. after 9 completed months of age (measles is recommended at 9 months of age).74 In the first prospective study, almost half of the infants <33.5 weeks of gestational age (very preterm) and weighing <1,500 g (very LBW) were without immunization, while 62.5% of the remaining infants had a documented delay in immunization.72 In the second study, data from the National Family and Health Survey-4 revealed that LBW infants with a birth weight <2,000 g had higher odds of a delay in receiving the BCG vaccine  and the DPT-1  and the first dose of the measles vaccine .71 In a third study, in which 10,644 LBW infants  were enrolled and followed until 12 months of age, a significantly lower immunization uptake was documented both in terms of the proportion of infants immunized and of the timing of vaccine administration (73 There was a delay in the time of administration of the vaccines compared to the recommended timing. The median delay (interquartile range) for the BCG vaccine was 41 (19–75), and for the three doses of the DPT vaccine  was 30 (12–63), 46 (23–89) and 62 (34–112) days, respectively.73 For the measles vaccine, the median delay from the recommended timing was 24 (9–46) days.73Published literature suggests that vaccination in preterm and LBW infants is delayed despite the existence of recommendations.stration . About 376 Several factors were identified as causes of vaccine hesitancy in India: these relate to immunization effectiveness, safety/adverse events, provider belief, attitudes of parents, religious/socioeconomic factors, and policy guidelines regarding vaccination.75 These factors become even more complex in preterm and LBW infants.71–73 Factors of delayed vaccination in preterm and LBW infants were identified in two studies.73 Choudhary et al. and Upadhyay et al. both reported that Islamic religion and young maternal age (<20 years of age) were associated with lower odds of full immunization and higher odds of delayed vaccination for DPT–1. Female sex of the infant, birth weight <2,000 g, delivery by unskilled personnel, higher number of children and a lack of awareness about vaccination risks/benefits among mothers were also associated with lower odds of full immunization. In contrast, a high level of maternal education was strongly associated with improved vaccination status of the infant.73 Across studies, the main reason for a delay in vaccination was the general lack of awareness among HCPs and parents about vaccination benefits and concerns about possible adverse events due to vaccination in preterm and LBW infants.76 To this, we suggest the use of vaccines with published efficacy and safety data in the preterm and LBW infant population  can provide protection against VPD for the mother, the developing fetus and the newborn through maternal antibodies transfer via the placenta and subsequently the breast milk.79 An example is neonatal tetanus, which tends to occur during the first 3–14 days of life and which carries a case fatality rate of 100% in newborns. Through immunization efforts, maternal and neonatal tetanus have been eliminated from India.80 Pertussis and influenza are other preventable diseases with potentially severe consequences  that can be averted through maternal immunization.81 Maternal immunization provides clear benefits. It is worth noting that the uptake of maternal immunization can however be slow.83 Common reasons include issues of confidence  and vaccine efficacy, driven possibly by the timing of vaccination.82–84The use of indirect immunization strategies such as maternal immunization and cocooning have been suggested as relevant strategies to alleviate the burden of VPDs in infants .86 For influenza vaccination, researchers have shown that seasonal influenza vaccination should be given at any stage of pregnancy, with the caveat that it takes 2 weeks after vaccination for the mother to be protected against influenza.87–90 Public health authorities have also revised their recommendations, with a few of them even recommending vaccinations as early as possible during pregnancy.90 Further research efforts to establish the appropriate timing of vaccinations during pregnancy could strengthen the use of maternal immunization in preventive neonatology.84Recent studies have suggested that antigen‐specific cord-blood antibody titers are greater following maternal immunization with the tetanus, diphtheria, and acellular pertussis vaccine in the second, rather than the third trimester.78 However, there is little evidence to support the use of this strategy in protecting the extremely preterm and LBW infants. Additionally, cost and logistical barriers could further limit the widespread implementation of this strategy.92Other indirect immunization strategies such as cocooning could be considered when maternal immunization is missed or delayed. The IAP recommendation states that immunizing individuals who have regular contacts with a newborn might help reduce the risk of infection in newborns.93In preterm and LBW infants, implementing the same vaccination schedule as set forth for full-term and normal birth weight infants appears crucial, as can be seen in the vaccination recommendations . Specifi95 Studies have shown that, following administration of vaccines, preterm and LBW infants mount an immune response directly proportional to their gestational age and birth weight.96 Importantly, vaccines display a good safety profile even when given in combination, without compromising the immune response; this could potentially alleviate concerns of parents or HCPs with respect to safety.97 In addition, vaccinations recommended for use in healthy infants and children have shown good levels of efficacy, safety, and effectiveness regardless of prematurity or birth weight , given alone or with other pediatric vaccines, has a clinically acceptable safety and immunogenicity profile in preterm (>24 weeks) and LBW (as low as 700 g) infants as in full-term infants, although HBV and Hib vaccine responses appeared lower in preterm and LBW infants.37 The occurrence of post-immunization cardiorespiratory events is influenced by the severity of underlying neonatal conditions, but most tend to resolve spontaneously or require minimal intervention.37 These data make a strong case for the vaccination of preterm and LBW infants according to the schedule proposed for full-term and normal birth weight infants . However, monitoring of the preterm/LBW infant up to 72 hours after vaccination is recommended.98 Notably, additional doses of HBV should be administered in infants receiving the first dose during the first days of life if they weigh less than 2,000 g because of a reduced immune response; for preterm infants born to hepatitis B Ag-positive mothers, both Ig and HBV should be given within 12 hours.99 The timeliness of vaccination and completion of the primary vaccination series at chronological age rather than gestational age appears crucial to provide the earliest possible protection in preterm and LBW infants.95 Importantly, we suggest the use of vaccines that have been tested in the preterm and LBW infant population and have robust efficacy and a clinically acceptable safety profile.Among the combination vaccines available, the diphtheria, tetanus, pertussis, hepatitis B, inactivated polio vaccine and 51 India’s National Health Policy 2017 set a target of 16 deaths per 1,000 live births for neonatal mortality by 2025,100 and the GOI has also set a target of less than 10 neonatal deaths per 1,000 live births by 2030 under the India Newborn Action Plan.101 Within this context, prematurity and LBW in neonates deserve special attention, as a significant number of children born in India are born preterm or have LBW.19 Although a systematic literature search was not included in this review, which is a limitation, it reaches its objective of raising awareness on the importance of reducing the incidence of VPDs in preterm and LBW infants in India through immunization.The considerations presented in this review have both clinical and public health implications for India. In recent decades, India has seen a signiﬁcant improvement in neonatal and infant health after the introduction of several initiatives by the Government of India (GOI).102 Reducing the incidence of VPDs in this vulnerable population after birth is the need of the hour. This can be achieved through timely immunization of the mother and newborn. Maternal immunization should be encouraged and there is a large evidence base supporting the safety and effectiveness of immunization during pregnancy.103 Similarly, vaccines in preterm and LBW infants are equally safe, immunogenic and effective as compared to full-term and normal birth weight infants.94–96 Generating more evidence on the timing of maternal immunization, as well as identifying and addressing barriers to vaccination uptake, are key challenges to overcome.88Published evidence from studies conducted outside India indeed shows that prematurity and LBW can predispose the infant, given their immunocompromised status, to a high risk of VPDs.21–23 Notwithstanding these recommendations, studies from India show that preterm and LBW infants are vaccinated with a significant delay,76 driven by the clinical judgment of the treating HCP whose recommendation is instrumental in ensuring vaccination. Delays due to true contraindications  are justified, but avoiding risks related to ‘small for gestational age’ or birthweight are often cited as the reason behind vaccination delays. LBW appears to be a strong indicator of vaccination delay. Given that being born preterm is a leading cause of LBW, gestational age could also be recognized as a predictor of vaccination delay.50 Data specific to vaccination delays in premature infants from India are lacking and are needed to shape the national vaccination policy. In addition, information assessing the relationship between vaccination delay and disease occurrence should be generated through large-scale observational studies. Further studies estimating vaccination coverage in preterm and LBW infants might provide insights on the scale of the problem and the underlying reasons for vaccination delay.In India, healthcare institutions advocate that preterm and LBW infants are vaccinated following the same schedule as that of their counterparts who are born full-term with normal birth weights, apart from the hepatitis B vaccine wherein an additional dose is required.50 There are several barriers in achieving timely vaccination of preterm and LBW infants in India. Among these, HCP and parent knowledge, perceptions and attitudes to vaccination stand out. The role of HCPs in facilitating immunization uptake is well-documented hence training HCPs to discuss the risks versus benefits of vaccinations with parents, on scientifically validated grounds, seems highly relevant.77 To achieve this, HCPs must regularly acquire up-to-date information on vaccinations in preterm and LBW infants. Besides efficacy and safety, parents tend to worry about the number of vaccinations.53 Targeted education and awareness initiatives for HCPs and health literacy interventions for parents, with focus on the importance, effectiveness and safety of vaccinations could help bridge immunization gaps in the vulnerable preterm and LBW infant population. In addition, the use of combination vaccines should be encouraged, as it addresses parents’ fears of multiple injections and increases the acceptance and compliance with the vaccination schedule.97Delayed vaccination increases the susceptibility window to VPDs and their complications.Routine childhood vaccinations can help reduce or eliminate the burden of VPDs and should be given to preterm and LBW babies, regardless of prematurity or birth weight. It is crucial that HCPs are made aware that preterm and LBW infants could be faced with detrimental health effects if vaccinations are not administered in a timely manner. Inappropriate delays in vaccinating this fragile population should be minimized by ensuring that vaccination discussions are encouraged with families and caregivers at the point of care. These steps should be closely integrated within neonatal and other overall infant health management strategies to increase vaccination compliance and improve health in the fragile population of preterm and LBW infants.


Bartonella species, including zoonotic ones. In this study, small wild rodents, collected from two sites in rural areas of Switzerland, were screened for Bartonella spp. using molecular detection methods. In brief, 346 rodents were trapped in two rural sites in the Gantrisch Nature Park of Switzerland . Pools of DNA originating from three animals were tested through a qPCR screening and an end-point PCR, amplifying the 16S-23S rRNA gene intergenic transcribed spacer region and citrate synthase (gltA) loci, respectively. Subsequently, DNA was extracted from spleen samples belonging to single animals of gltA positive pools, and gltA and RNA polymerase subunit beta (rpoB) were detected by end-point PCR. Based on PCR results and sequencing, the prevalence of infection with Bartonella spp. in captured rodents, was 21.10% (73/346): 31.78% in Apodemus sp. (41/129), 10.47% in Arvicola scherman (9/86), 17.05% in Myodes glareolus (22/129), and 50% in Microtus agrestis (1/2). A significant association was observed between Bartonella spp. infection and rodent species (p < 0.01) and between trapping regions and positivity to Bartonella spp. infection (p < 0.001). Similarly, prevalence of Bartonella DNA was higher (p < 0.001) in rodents trapped in woodland areas  compared to those captured in open fields . Sequencing and phylogenetic analysis demonstrated that the extracted Bartonella DNA belonged mainly to B. taylorii and also to Candidatus “Bartonella rudakovii”, B. grahamii, B. doshiae, and B. birtlesii. In conclusion, the present study could rise public health issues regarding Bartonella infection in rodents in Switzerland.Rodents represent a natural reservoir of several  Bartonella species are Gram-negative, facultative intracellular and emerging zoonotic bacteria infecting both domestic and wild mammals  an anBartongltA specific for Bartonella spp. cPCR was performed using previously described primers 443f and 781r [gltA gene fragment was amplified by a protocol consisting of a first step at 95 °C for 15 min, followed by 45 cycles at 95 °C for 5 s and 60 °C for 45 s.Pools showing a quantification cycle (Cq) less than 35 were further analyzed by cPCR, detecting a 340 bp segment of the and 781r ,54 and aBartonella spp. FG4-1 DNA (NCBI: txid545598) was used as a positive control and nuclease-free water included as a negative control in each PCR run. Pools showing amplicons consistent in size with the amplified locus were considered positive to Bartonella spp. infection. Therefore, DNA from spleen of single animals belonging to positive pools was extracted using DNeasy Blood and Tissue Kit  and retested for gltA (as described above) and rpoB loci. For this latest gene, an 800 bp segment was amplified using previously described primers 1400f and 2300r [PCR products were electrophoresed in 1.5% and 2% agarose gels, stained by GelRed Nucleic Acid Gel Stain  and visualized under UV light. PCR products were visualized by electrophoresis, purified through MinElute PCR purification kit (Qiagen) and sequenced in both directions using Sanger method by a commercial sequencing provider .https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 2 August 2021)). All sequences were deposited in the GenBank under accession numbers described in The raw sequences were edited using Geneious Prime version 2021.2.2  and compGltA and rpoB sequences from this study and from GenBank were aligned  and phylogenetic relations were estimated through a Bayesian inference method using a GTR substitution model with the MrBayes [ MrBayes  plugin i MrBayes , with 1,Bartonella spp. prevalence among small rodent species, sampling locations, gender and age were assessed by Fisher’s exact and Chi-square test and 95% confidence intervals were set. Statistical analysis was conducted using GraphPad Prism 6 version 6.07 and a p value < 0.05 was considered significant.Differences of Bartonella species were detected in two rural areas of Switzerland. However, these regions are not distant from urban areas and, thus, contacts between humans and infected rodents are possible. Therefore, the Bartonella-infected wild rodents might represent a potential pathogen reservoir in Switzerland and should be considered of public health importance.Wild rodents infected with zoonotic


The present study aimed to investigate whether adult patients with attention deficit hyperactivity disorder (ADHD) show deficits in social cognition and to identify the structural neural correlates of social cognitive skills in ADHD. Twenty-six adult patients with ADHD and 26 matched healthy control participants performed the Movie for the Assessment of Social Cognition and underwent a structural magnetic resonance imaging scan. We compared theory of mind (ToM) performance between ADHD patients and healthy controls. Using voxel-based morphometry, we further compared gray matter volumes in regions that are critical for social cognition between the two groups and examined whether ToM performance was correlated with brain morphometry measures. We did not observe any between-group differences in ToM abilities or regional gray matter volumes. Across both groups, performance on affective aspects of ToM correlated positively with gray matter volumes in the medial part of the superior frontal gyri, which is typically involved in social cognition. This study is the first to relate brain structure to social cognitive abilities in adult patients with ADHD. Although our sample was small and heterogeneous, with half of the patients showing mild-to-moderate psychiatric comorbidities, our results may encourage longitudinal studies that relate social cognitive development in childhood and adolescence to brain maturation of ADHD patients. Patients with attention deficit hyperactivity disorder (ADHD) are frequently faced with a range of interpersonal difficulties and poor social functioning . ToM refBoth cognitive ToM and affective ToM have been repeatedly demonstrated to be impaired in children with ADHD   absence of neurological disorders, (ii) absence of autism spectrum disorders, (iii) no intake of psychotropic drugs (for patients: no psychotropic drugs different from medication for ADHD), (iv) suitability for MRI and (v) absence of severe psychiatric disorders , as assessed using the German versions of the Structured Clinical Interview for DSM-IV (SCID-I) , were inThe study was approved by the ethics committee of the University of Oldenburg. Written informed consent was obtained from all participants prior to study participation.The MASC is an ecologically valid video-based psychometric test for social-cognitive skills that has been shown to be sensitive to even subtle ToM deficits, while minimizing the possibility of social desirability bias  as indicator of inhibitory performance in the Go/No-go task, and the interference score  as a measure of interference control in the flanker task. To assess verbal intelligence, participants completed the Multiple Choice Vocabulary Test (MWT-B) : a Go/No-go task to measure response inhibition and a flanker task to assess interference control  .n = 6 per group, Prisma: n = 20 per group) and scanner was included as a covariate in the statistical models for MRI analyses. High-resolution T1-weighted structural images were recorded using magnetization-prepared rapid gradient-echo (MPRAGE) sequence  = 250 × 250 mm, Repetition Time (TR) = 1900 ms, Echo Time (TE) = 2.52 ms, Flip Angle (FA) = 90°).Due to acquisition of a new research scanner and end of contract with the old scanner facilities, we had to switch to the new scanner after measurement of six patients and their matched healthy controls. Imaging data were obtained in two different 3-Tesla MRI Scanners . To control for possible image differences, each patient and his/her respective matched healthy control were tested in the same scanner  or absence (null hypothesis) of group differences, additional Bayesian Mann–Whitney U tests with default prior option (Cauchy distribution) were calculated.Behavioral data were analyzed using SPSS Statistics 22  and JASP 0.13.1. As most outcome variables were not normally distributed (Kolmogorov–Smirnov test), we conducted non-parametric tests and report their median values and IQRs. To ensure comparability to other studies, mean values and standard deviations are in addition reported for all behavioral outcomes, and demographic and clinical data in P ≤ 0.017). For a complete picture, the number of answers reflecting exceeding ToM, the total number of correct responses and the number of correctly answered control questions are also displayed  as one of our primary outcome measures. Based on the study by ayed see .P ≤ 0.05).To further explore the relation between ToM and ADHD symptoms, we calculated Spearman rank order correlations between ToM performance and ADHD questionnaire scores (ADHS-SB and WURS-k) across both groups. In addition, we tested for correlations between ToM performance and executive functions. For those exploratory analyses, no adjustment for multiple testing was applied (significance level of https://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass15.pdf) using SPM12  and MATLAB R2016a. First, raw data were visually inspected to check for artifacts (e.g. stemming from movement), which led to the inclusion of all images for further analyses. As part of the preprocessing, each T1 image was segmented into different tissue types . To enhance inter-subject alignment, a Dartel  template, which was registered to Montreal Neurological Institute (MNI) space, was generated. This template was then used to align the individual spatially normalized scans into MNI space. To preserve regional differences in the absolute amount of gray matter (i.e. gray matter volume), volume change correction (modulation) was conducted by modulating image intensity of each voxel with the Jacobian determinants derived from spatial normalization. Finally, to increase signal-to-noise ratio, images were spatially smoothed using an isotropic Gaussian kernel at 8 mm full width half maximum. After each preprocessing step, images were reviewed visually. In addition, quality control procedures integrated in the Computational Anatomy Toolbox—CAT12 .To examine structural correlates of social cognition performance, we performed VBM analyses using the high-resolution T1-weighted structural MR images . Those aox—CAT12  were appt-tests on the preprocessed gray matter images. To test for relations between social cognition and gray matter volumes, we performed correlational analyses between scores reflecting ToM performance  and gray matter volumes across groups, as well as in patients and controls separately. In all group analyses, an absolute threshold masking of 0.2 was used in order to remove edge effects between gray and white matter. As data were acquired in two different MRI scanners, scanner was included as a covariate in all statistical models.For the purpose of group analyses, total intracranial volumes were computed by summing up gray matter, white matter and cerebrospinal fluid for each volume and were then accounted for by using proportional scaling. This approach is dividing the preprocessed data by the total intracranial volumes. Instead of removing variance explained by total intracranial volume, all data are proportionally scaled by their total intracranial volume values. To investigate volumetric differences between ADHD patients and healthy controls, we conducted two-sample post hoc whole-brain analyses. The initial voxel threshold was set to P < 0.001 uncorrected and multiple testing was accounted for on cluster level, based on a corrected pFWE < 0.05, as provided in SPM12. Stereotaxic coordinates are reported in MNI space.All analyses were conducted hypothesis-driven within predefined regions of interest (ROIs), which have been related to social cognition in previous studies . Using tParticipant characteristics are displayed in U test). There were also no group differences regarding the number of correct responses to the MASC control questions. The additional Bayesian analysis showed moderate evidence for the null hypothesis (absence of group differences) for all three outcome measures .Descriptive data and statistical outcome values for all behavioral measures are depicted in Post hoc, we conducted additional analyses excluding patients receiving regular medication (n = 4). Further, we compared a subgroup of patients with high ADHD symptoms  to their respective matched healthy controls. In both analyses, we did not find group differences in any outcome variable.There were no significant differences in regional gray matter volumes between ADHD patients and healthy controls, in both ROI and whole-brain analyses.x, y, z] = ; k = 602; r = 0.43; 95% CI = ; see post hoc whole-brain analysis, we observed an additional positive correlation between affective ToM and gray matter volume in one cluster in the right angular gyrus extending to right inferior parietal regions .Across groups, higher affective ToM performance correlated with increased gray matter volume in one cluster comprising medial parts of the superior frontal gyri  as well as including only patients with high ADHD symptoms  and their respective matched healthy controls were also computed. Further, we conducted an analysis including only patients and their matched controls who were measured at the new scanner . Results of those analyses are presented in To control for the influence of possible confounding variables, the original analyses  were repeated, including depression scores (BDI) or gender as covariates. Additional models excluding patients receiving regular medication  between patients and healthy controls, which is well in line with the lack of ToM deficits in our ADHD sample. Our findings could be interpreted as support for the developmental delay theory in ADHD, suggesting that cortical development  in adolescence is delayed in most patients . Our finIn our adult sample, superior skills in emotional aspects of ToM (i.e. inferring affective states) were associated with larger volumes in medial parts of the superior frontal gyri, and in whole-brain analyses, with larger volumes in the right angular gyrus and inferior parietal regions. This is in line with previous studies showing associations between gray matter volume in those regions and social cognition in healthy and clinical populations  and one-half of our patient sample was free of any psychiatric comorbidities. However, half of the patients showed comorbid mild-to-moderate depression and/or anxiety disorder, and one patient presented comorbid borderline personality disorder. While this heterogeneity in our sample has to be considered when interpreting our findings, including patients with frequently occurring comorbidities has the advantage of a more representative ADHD sample e.g. . ImportaAlthough ADHD symptom severity in our study was high on average see . Ibanez Finally, it is worth noticing that we cannot exclude the possibility that the lack of differences between patients and healthy controls in our study might be due to the small sample size. Because data acquisition was conducted in the context of a larger project on the effects of exercise on cognitive functions in ADHD , power analyses were conducted for the primary outcome parameters of this project. To detect differences in social cognitive skills between patients with ADHD and healthy controls, for which small-to-moderate effect sizes would be expected (e.g. Social cognition deficits have been frequently reported in children with ADHD, while findings for adult patients are mixed. In our adult ADHD sample, we did not observe any impairments with regard to affective or cognitive aspects of ToM. In addition, we did not find structural differences in brain regions typically underlying social cognitive abilities between patients and healthy controls, which might provide a potential neural basis for the lack of social cognition deficits in our sample. However, to interpret such findings in light of the view that ADHD is characterized by a delay in cortical development, longitudinal studies to relate social cognitive development to brain maturation are necessary.nsab060_SuppClick here for additional data file.