Fronto-striatal circuits have been implicated in cognitive control of behavioral output for social and appetitive rewards. The functional diversity of prefrontal cortical populations is strongly dependent on their synaptic targets, with control of motor output mediated by connectivity to dorsal striatum. Despite evidence for functional diversity along the anterior-posterior striatal axis, it is unclear how distinct fronto-striatal sub-circuits support value-based choice. Here we found segregated prefrontal populations defined by anterior/posterior dorsomedial striatal target. During a feedback-based 2-alternative choice task, single-photon imaging revealed circuit-specific representations of task-relevant information with prelimbic neurons targeting anterior DMS (PL::A-DMS) robustly modulated during choices and negative outcomes, while prelimbic neurons targeting posterior DMS (PL::P-DMS) encoded internal representations of value and positive outcomes contingent on prior choice. Consistent with this distributed coding, optogenetic inhibition of PL::A-DMS circuits strongly impacted choice monitoring and responses to negative outcomes while inhibition of PL::P-DMS impaired task engagement and strategies following positive outcomes. Together our data uncover PL populations engaged in distributed processing for value-based choice. The prelimbic region of the prefrontal cortex is involved in goal-directed action. Here the authors investigate the function of the prelimbic pathways projecting along the anterior posterior striatal axis in value based decision making in male mice. Consistent with a topographical organization of afferent inputs4, striatum exhibits functional segregation along its anatomical axes, with the dorsoventral divisions segregating reward and motor processes and medial-lateral domains supporting goal-sensitive and habitual action strategies5. However, less attention has been given to striatal function along the anterior-posterior (A-P) axis10, despite early retrograde studies pointing to a unique longitudinal (A-P) organization of cortical-striatal inputs11.Value-based decision-making requires a complex series of neural computations—the integration of success and failure, the proper attribution of actions to temporally displaced outcomes, and the monitoring of context and underlying task structure. One hypothesis posits that inputs for this decision-making process are represented across forebrain excitatory populations, with their integration in the striatum serving as an early step in action selection9. In contrast, the importance of the anterior dorsomedial striatum (A-DMS) in goal-directed choice remained uncertain, with opposing results for pharmacological inactivation and excitotoxic lesions12. Optogenetic manipulations of specific spiny projection neuron subtypes within the A-DMS have implicated this subregion in supporting flexible responses during reversal learning13, consistent with pharmacological manipulations of anterior caudate in marmosets14. In contrast, the anterior dorsolateral striatum (DLS) supports a protein synthesis-dependent consolidation of newly learned actions15. Finally, a growing body of evidence has implicated the rodent striatal tail, the most caudal subregion, in behavioral responses to aversive stimuli and psychostimulants18.Seminal studies in rat provided the first evidence of functional segregation along the striatal A-P axis, with posterior dorsomedial striatum (P-DMS) lesions disrupting both the initial acquisition and post-training execution of instrumental conditioning, in particular modulation of response according to action-outcome association20. Analysis of prefrontal populations accounting for downstream synaptic targets has revealed pathway-specific functional differences for prefrontal control of social-spatial rewards21, reward anticipation22, and choice directions23. The prelimbic region of the prefrontal cortex has been hypothesized to support goal-directed action by encoding short-term memories necessary for subsequent action-outcome associations in dorsal striatum24. Specific targeting of prelimbic-striatal pathways has extended this view, demonstrating persistent neural coding of value essential for choice behavior25 and the mediation of response inhibition during tasks requiring sustained attention26. Finally, DREADD-mediated inhibition of PL neurons projecting to either anterior or posterior striatal subregions has uncovered involvement in instrumental learning10.The prefrontal cortex exerts cognitive control over mammalian behavior via extensive afferent integration and widespread downstream connectivityHere we systematically explore the function of PL pathways projecting along the A-P striatal axis in male mice via integration of mono- and di-synaptic viral circuit tracing, local circuit connectivity measures, single neuron calcium imaging, statistical modeling of neural coding properties, and target-specific optogenetic manipulations. Retrograde tracing from A/P-DMS subregions revealed non-overlapping PL populations, which exhibited unique encoding of behavioral variables over multiple time scales essential for shaping efficient action selection and execution. Target- and temporally- specific optogenetic manipulations confirmed the functional divergence of these fronto-striatal pathways, with PL::A-DMS pathways supporting choice monitoring and responding to negative outcomes and PL::P-DMS pathways supporting task motivation and responding to positive outcomes. Together, our results provide insight into the distributed nature of fronto-striatal pathways for decision making.27, we injected two distinct Alexa-conjugated Cholera toxin subunit-B retrograde tracers into A-DMS and P-DMS  injected in P-DMS  models, wherein values for each choice are incrementally updated in a trial-by-trial manner  provided a similar optimal mix of predictive power for given free parameters. Consistent with this, we found a strong correlation between the state estimates of the rLogReg model and the difference in Q values (ΔQ) of the RL model  imaging of retrogradely-labeled PL neurons expressing GCaMP7f during this task. Given the minimal fiber-of-passage overlap with standard retrograde tracers , local reward environment (ΣQ) and positive/negative reward prediction errors (RPE+/−)  single neuron calcium  Figs. , 3e. Locits Fig. . Regress2+ imaging data, measuring total model fit quality by calculating the fraction of Ca2+ signal variance explained (FVE). At a cut-off threshold of 5% FVE, our model fit ~38% of total PL::A-DMS neurons and ~32% of total PL::P-DMS neurons . Analysis of choice-associated kernels revealed that PL::A-DMS neurons exhibited robust activity starting just prior (~250 ms) to choice execution Figs. , S6g andNext, we examined how distinct PL::A/P-DMS pathways responded during behavioral outcomes. While broadly defined outcome tuning was seen in a similar proportion of neurons in each circuit Fig. , we founQ), the sum of choice values (ΣQ), positive and negative reward prediction errors (+/−RPEs) in addition to the local reward rate (RR) over the last five trials to capture the strength of engagement in this self-initiated task. We found that the PL::P-DMS pathway more strongly encoded these internal value estimates , inhibition of PL::A-DMS negative outcome signals led to less effective choice behavior following unrewarded trials while the opposite was true for high reward probability environments (Prew = 1.0/0.85)  Fig. . Finally85) Fig. 25,33,34.PL::P-DMS pathways were found to strongly encode action value differences and local reward rates, two temporally integrated measures of recent task outcomes. As the slow dynamics of these neural signals precluded precise optogenetic interrogation, we performed in-depth analysis with our second optogenetic paradigm, where inhibition was delivered for 6 s following outcomes Fig. . We assu35. Accordingly, neural processing within striatum is thought to be reflective of cortical activity36. Cortico-striatal projections are highly localized along the dorsal-ventral and medial-lateral axes4, but less so along the anterior-posterior striatal extent11. Here we sought to understand the implications of this architecture for cortico-striatal processing, focusing on prelimbic cortical connections to dorsomedial striatum. As for most DMS-targeting afferent populations, we found that PL cortex formed non-overlapping circuits according to A-P target. Prelimbic to A/P-DMS could be separated based on their local synaptic connectivity to striatal SPN subtypes as well as biases in their afferent drivers. In vivo imaging demonstrated that these two populations divided encoding of key behavioral variables for goal-directed choice. PL::A-DMS pathways strongly encoded selected choices and negative outcomes, while PL::P-DMS pathways strongly encoded internal value representations and an integrated positive outcome/choice signal. Target- and temporally- specific optogenetic manipulations further confirmed the functional divergence of these fronto-striatal circuits, with PL::A-DMS pathways providing choice and negative outcome monitoring while PL::P-DMS pathways supporting task engagement and reinforcement by positive outcomes.The dorsal striatum is a canonical set of circuits that interfaces much of the forebrain with downstream basal ganglia nuclei that select and modulate motor output37, and surprising considering more targeted DMS tracings38, all transsynaptic tracing studies remain limited in their ability to predict synaptic efficacy38. Despite both A/P-DMS dSPNs receiving strong excitation from their respective PL drivers, only PL projections to A-DMS strongly recruited feed-forward inhibition onto dSPNs  neuronal responses to positive outcomes , while having no choice effects following prior positive outcomes  as in Bari et al., our inability to control trial initiation precluded investigation into the relative persistence of these distinct value signals25. Engagement in self-initiated foraging tasks is strongly modulated by local reward environment, a variable we captured with a local average of the reward rate. Again, we found that PL::P-DMS pathways more strongly encoded this feature as compared to PL::A-DMS pathways. The persistent nature of value coding in these pathways made phasic optogenetic manipulation difficult. To circumvent this, we looked at all trials in sessions where inhibition was delivered in 30% of trials for 6 s. after outcome, reasoning that prolonged inhibition should sufficiently alter persistent neural signals to impact the overall behavior  in the session. Indeed, we found that post-outcome inhibition was able to both reduce the total number of initiated trials and reduce the win-stay probability in non-light trials, suggesting the involvement of reward-rate and ΔQ-encoding PL::P-DMS populations, respectively. It is interesting to hypothesize that the reduction in task engagement caused by disruption of this pathway may share a common cause with the reduced responding seen in earlier P-DMS lesion studies8.Internal representation of choice value and local reward availability are key determinants of behavior in dynamic foraging tasksals Fig. . This ΔQ12. While our initial Kir2.1 manipulations within DMS confirmed the importance of P-DMS for devaluation-based decreases in responding -Tg(Adora2a-cre)KG139Gsat/Mmcd) were grouped with littermates on a 12:12 light-dark cycle and provided ad libitum food and water. Given the potential for impacts of the estrous cycle on goal-directed behavior, all experiments were conducted on naive male mice.Animal experiment procedures were approved by the Intracranial surgery was conducted on a stereotaxic surgery frame  under isoflurane anesthesia (1.5–2% + oxygen 1 L/min). Animal body temperature was maintained at 30 °C during surgery using a feedback thermocontroller . Skin was cleaned with Nair hair remover followed by application of betadine to disinfect the area. Prior to surgery, 2 mg/kg bupivacaine was administered subcutaneously, and the mouse was given a single dose of meloxicam (5 mg/kg). Skin was carefully opened along the anterior-posterior midline, bregma was set to zero based on skull balance. A craniotomy was performed with a drill above the target site. Virus or Tracer was loaded into mineral oil -filled glass pipette  and delivered at rate 30 nl/min using a micro-infusion pump . At least 5 minutes after infusion, the pipette was slowly withdrawn (1 mm/min) from the brain, and the skin was sutured. Animals were monitored up to 1 h following regaining of consciousness, then transferred to the home cage and monitored after 24 h, 48 h and 72 h. Injection coordinates, A-DMS: AP + 1.2 mm, ML + 1.35 mm, DV −2.7 mm; P-DMS: AP −0.3 mm, ML + 1.95 mm, DV −2.2 mm; PL: AP + 2.0 mm, ML + 0.35 mm, DV −1.7 mm53. For CTB only tracing , a 1:1 mixture of AAV5-CaMKii::Cre  + AAVdj-EF1a::Flex-Synaptophysin-mRuby  viruses was injected into PL. For mapping retrogradely labeled neurons, a mixture of AAV1-Syn::Cre  + AAVdj-EF1a::DIO-RVG + AAVdj-EF1a::Flex-TVA-mCherry  was injected into A-DMS and Alexa647-conjugated Cholera toxin subunit-B  was injected into P-DMS. Seven days later, EnvA G-Deleted Rabies-eGFP  was injected into A-DMS. This trans-synaptic tracing approach allowed us to avoid retrogradely labeling neurons via fiber-of-passage uptake (cholera toxin-based tracers undergo passive membrane incorporation) in the A-DMS, where P-DMS fibers would be traversingAfter viral injection, 7 days (for Rabies virus) or 3 weeks (for AAV) were allowed for viral expression, animals were deeply anaesthetized with i.p injection of 100 μL pentobarbital sodium  and transcardially perfused with PBS followed by formalin (10%). Brains were removed and post-fixed in formalin  overnight, then transferred to PBS. Brains were sectioned coronally at 50 µm then brain slices were mounted on slide glasses and covered with fluoromount solution  for imaging.54.Stitched large-field images were obtained with a 4× objective  on an epi-fluorescent microscope . Fluorescence-positive neurons were counted using automated object detection . Three-dimensional brain images were then reconstructed using NeuroInfo software (MBF bioscience), which registered individual slices to the Allen Institute reference brain atlas At room temperature, free floating brain slices were permeabilized in 0.6% Triton x-100 and blocked with 6% normal goat serum  in PBS for 1 h. Samples were incubated in primary antibody solution  overnight in 0.2% Triton x-100 and 2% NGS in PBS. Slices were washed then incubated in secondary antibody solution  for 1 h in 0.2% Triton x-100 and 2% NGS in PBS, then mounted and imaged.2PO4, 24 NaHCO3, 5 HEPES, 13 Glucose, 1.3 MgSO4, 2.5 CaCl2. After perfusion, the brain was quickly removed, submerged, and coronally sectioned on a vibratome  at 250 μm thickness in ice-cold aCSF. Slices were transferred to NMDG based recovery solution at 32 °C of the following composition (in mM): 92 NMDG, 2.5 KCl, 1.2 NaH2PO4, 30 NaHCO3, 20 HEPES, 25 Glucose, 5 Sodium ascorbate, 2 Thiourea, 3 Sodium pyruvate, 10 MgSO4, 0.5 CaCl2. After 12–15 min recovery, slices were transferred to room temperature aCSF chamber (20–22 °C) and left for at least 1 h before recording. Following recovery, slices were placed in a recording chamber, fully submerged at a flow rate of 1.4–1.6 mL/min, and maintained at 29–30 °C in oxygenated  aCSF containing AP5 .For slice physiology, channelrhodopsin-expressing virus (AAV.DJ-hSyn-ChiEF-2a-Venus) was injected in PL. To visualize specific striatal cell types, AAVdj-EF1a::DO/DIO-GFP/tdTomato was injected into the A/P-DMS in Adora2A-Cre mice (KG139Gsat). All mice were allowed to recover for 4-5 weeks prior to recording. Mice were deeply anesthetized and trans-cardially perfused with ice-cold aCSF containing (in mM): 124 NaCl, 2.5 KCl, 1.2 HaH- reversal potential) and at +10 mV , which were determined in prior experiments. For current-clamp recordings, recording pipette filled with internal solution containing (in mM) 140 K-gluconate, 5KCl, 0.2 EGTA, 2 MgCl2, 10 HEPES, 4 MgATP, 0.3 NaGTP, 10 2Na-Phosphocreatine (pH adjusted to 7.3-7.4 using KOH). Recordings were performed using a MultiClamp 700B (Molecular Devices) and Igor7 , filtered at 2.8 kHz and digitized at 10 kHz. ChIEF expressing axon terminals were stimulated with brief (5 ms) pulses of 473 nm blue light from a collimated LED illuminator . Input and series resistance were monitored continuously, and experiments were discarded if either parameter changed by >20%. NBQX PTX  were applied if necessary.For voltage-clamp recordings, recording pipettes were pulled from borosilicate glass  that had a tip resistance of 3~5 MΩ when filled with internal solution containing (in mM) 130 CsMeSO4, 5 CsCl, 10 HEPES, 2.5 MgCl, 0.6 EGTA, 1 QX-314, 10 Na-Phosphocreatine, 4 NaATP, and 0.3 NaGTP 0.1 spermine (pH adjusted to 7.3–7.4 using CsOH). Striatal neurons were identified under visual control using IR-DIC optics . Visual identification of D1-MSN/D2-MSN was based on expression of GFP/tdTomato . Cells were voltage clamped at either −56 mV(Clhttps://github.com/sanworks/Bpod) in MATLAB (MathWorks). All port entries and events were recorded by the Bpod State Machine during behavioral sessions.Behavior training was conducted utilizing a custom built 3-port operant chamber . Each port is controlled by a TTL signal from the state machine consisting of white LED light, infrared beam break detector and liquid outlet. The center port was designated as a reward delivery outlet using a pinch valve . All behavior chambers were enclosed in sound-attenuating boxes . Behavior protocols were controlled by Bpod software  delivery from the center port spaced 20 s. apart. Following the habituation period, animals performed light-guided sessions as follows: (1) center port light indicated the beginning of a trial; (2) trial initiation via a center poke led to illumination of a randomly selected side port; (3) appropriate selection of the lit port within 3 s led to illumination of the center port and delivery of 12 μl of Boost at this location; (4) selection of the unlit alternative led to illumination of the center port without concomitant dispensing of reward. Each trial was separated by a 5 s. ITI in which all chamber lights were extinguished. Sessions lasted 1 h with no trial limits. Animals were considered to reach criteria with >200 completed trials per session.Poutcome = 85%). Correct choice resulted in delivering 12 μL Boost from the center port with 85% chance while incorrect choice resulted a in 500 ms punishment tone (white noise) with 3 s timeouts, also with 85% chance. In the remaining 15% of trials, animals didn’t receive any outcome (reward or punishment). Each trial was separated by a 3 s. ITI in which all chamber lights were extinguished. To prevent outcome-insensitive behavior, past-reward history was monitored in a 10-trial moving window, and rewarded side was switched when 8 of the last 10 choices were to the currently rewarded port. Sessions lasted for either 45 min. (1-p imaging) or 1 h (optogenetic manipulations). We utilized a relative reward-stay value >2 to decide when to move mice on to recording sessions. Relative reward stay was defined as:After reaching criteria performance levels in light-guided training, mice progressed to a 2-alternative forced choice task structured as follows: (1) center port light indicated the beginning of a trial; (2) a 500 ms. holding period  in the center port triggered the illumination of both side ports; (3) animals had a 3 s. window to register either left or right port choice. When animals failed to make a choice in this period this resulted in an omission, which was followed by a 3 s timeout and required the animal to reinitiate the trial. (4) successful registration of a choice was followed by 0.5 s delay period ending in the outcome period  and AAV5-CaMKii::Cre. To measure an ability to acquire instrumental learning, following the habituation period, animals performed instrumental acquisition sessions as follows: (1) center port light indicated the beginning of a trial; (2) trial initiation via a center poke led to illumination of a both side port; (3) selection to pre-determined port within 3 s led to illumination of the center port and delivery of 12ul of Boost at this location. pre-determined port did not change through a session; (4) selection of another side port led to illumination of the center port without concomitant dispensing of reward. Each trial was separated by a 5 s. ITI in which all chamber lights were extinguished. Sessions lasted 1 h with no trial limits. To compare response rate between pre-fed with substance for reward  and pre-fed with food chow , mice that were trained with fixed-ratio 20 schedule for 2–3 days were exposed to each substance for 1 h in different training day in home cage followed by behavior test under extinction protocol without outcome for 15 min.Q-value decayed towards zero, implementing a kind of “forgetting”,Qt is the value of the action taken on trial t of each choice and R is the actual reward received in trial t. The parameter α thus constituted a learning rate, while the parameter φ constituted a forgetting rate for the non-chosen port. The decision process mapping the Q-values to the probability of choosing one port over the other was modeled with a softmax rule:β is an inverse temperature parameter controlling the degree to which choices are biased by estimated value. High values for β indicate that mice more readily exploit differences in action values between the alternatives, while lower values suggest that mice exhibit more exploratory behavior. To fit this model to our choice data, we used the fmincon function in MATLAB to minimize the negative log likelihood of models using our parameters .). As a special case of this RL model, we obtained a simpler version without forgetting by imposing φ = 0.We adapted a simple Q-learning Reinforcement Learning Model with three parameters to fit the behavioral data produced by the serial reversal task. Mouse choice and outcome history were the primary inputs of the model. The values of the choice alternatives were initiated at 0 and updated as follows. For the chosen port,c(t) being the mouse choice on trial t , and r(t) representing the rewarded side (r(t) = 1 if trial t lead to a reward following a right choice, r(t) = −1 if it leads to a reward following a left choice, and r(t)=0 if there was no reward). Because of the symmetrical encoding of left and right choice, the regression coefficient β1,i captures the tendency of the mouse to repeat the choice operated at trial t-i, and β2,i captures the tendency to choose the side that was rewarded at trial t-i.As a further check on the performance of the RL model, we compared its goodness of fit and predictive power with that of three other models Figs. , S4d. Th29The second model we considered for this comparison was another logistic regression (rLogReg), where the weights on past rewards were constrained to decay exponentially in time:This model has four parameters . α represents a tendency to repeat the previous trial’s choice (“choice stickiness”); β is a general sensitivity to the history of reward; τ controls how far back in time the memory of past rewards extends; and γ is a general bias towards one choice or the other. This model is called “recursive logistic regression” because it allows for a recursive definition of a “state estimate” ρ:The third model we considered in the comparison with the RL model was a simple win-stay, lose-shift strategy (WS-LSh), made stochastic by the presence of a lapse rate ε. Under this model, on any trial the mouse performed a random choice with probability ε. If this was not the case (with probability 1−ε), the mouse repeated the choice made on the previous trial if that choice had been rewarded (win-stay) and made the opposite choice otherwise (lose-shift).We compared these three models with the Q-learning model by computing the Akaike Information Criterion (AIC), which adjusts the deviance of a model to penalize complex models with a larger number of parameters. The recursive logistic regression model had comparable AIC to that of the RL model, while the full logistic regression and the win-stay/lose-shift model both performed worse Fig. . Compari32. We labeled 8 reference videos and more than 50 frames/video to train the DLC network using resnet_50 network . Head velocity was estimated at any point in time as the difference between head position in two successive frames, multiplied by the frame rate. In order to use head velocity as a predictor in the encoding model, the resulting time series was linearly interpolated to allow for synchronization with the miniscope imaging timestamps.To track the animals’ body movement, behavioral sessions were recorded using an overhead webcam . Recording was performed using built-in software in miniscope software, frame rate was fixed at 30 fps/s. To extract head location, we averaged coordinates from right and left ear that were detected by DeepLabCut (DLC) software (Version 2.2.0.6)55 was unilaterally injected to A- or P-DMS, together with AAV1/hSyn-Flex::GCaMP7f-WPRE56 injection into PL. Prior to relay GRIN lens  implantation in PL, upper prefrontal tissue was gently aspirated using a glass pipette until reaching 0.5 mm above target site. Following tissue aspiration, the GRIN lens was slowly lowered (100 μm/min) until 0.3 mm above from the target site. Dental cement (Geristore™) was used to create a foundation around the GRIN lens, and the remaining exposed GRIN lens was covered with silicone paste to prevent scratches. After surgery, animals were transferred to a single housed cage, where their status was monitored until movement recovery. The anti-inflammatory Meloxicam (5 mg/kg) was applied subcutaneously daily for >1 week, and animals were carefully monitored. Four to six weeks following GRIN lens implantation, the miniscope baseplate was installed under 1-p imaging (UCLA miniscope v3.0)57 to locate fields of view (FOV) with robust GCaMP7f expression. Once a FOV was selected, the baseplate was fixed with dental cement to make a crown. Baseplates were covered with a cap, and animals were subsequently returned to the home cage.To record calcium signals from PL neurons targeting specific striatal subregions, retroAAV2/EF1a-3xFlag-Crehttps://github.com/JinghaoLu/MIN1PIPE/tree/v2-alpha) for motion correction , segmentation , and signal deconvolution (CNMF identifier)31. Each ROI selected by MIN1PIPE was individually reviewed to ensure somatic morphology and remove repeated selection of the same neuron’s proximal dendrites.To extract calcium traces from imaging videos, we utilized MIN1PIPE , and is characterized in the fitted model by a temporal kernel defined over a fixed time window around the event; therefore, this type of predictors will contribute to the predicted activity only in proximity of the associated event. By “continuous” we mean a predictor that simply takes on a particular value at any given point in time, such as the head velocity; therefore, this type of predictors will generally contribute to the predicted activity at any point in time .To analyze task-relevant neural activity we designed a neural encoding model based on a linear combination of event-based and continuous predictorsα, β, and γ are parameters to be fitted, the index i runs over all continuous predictors, and ith continuous predictor. The index j runs over all event-based predictors, kth function of the temporal basis set associated with the jth event-based predictor, and nth event of the jth type. ε represents some normally-distributed noise. As temporal basis functions  More precisely, we modeled the neural activity of a given neuron as:mentclass2pt{minim58. To do this we fit our choice data with a Q-learning reinforcement model with forgetting  and used Q values and reward prediction errors as internal behavioral variables. Specifically, we included trial-by-trial ΣQ (Qleft + Qright) and ΔQ (Qleft − Qright) values  and reward prediction errors (kernels tethered to the outcome). Finally, we included a local reward rate averaged over the prior 5 trials as a continuous behavioral variable (in units of μl/min). We fit these regression parameters using a generalized linear model with near-lasso regularization  to achieve sparse regression weights, using the glmnet library59 (wrapped for MATLAB usage with custom software available at 10.5281/zenodo.3568314). Details on the representation of each predictor in the design matrix of the model are given in Supplementary Table 60.We included episodic directly measurable variables, including trial start cue, self-initiation, choice, and outcome, which were represented by spline-based, temporally expanded kernels Fig. . As a prWe fitted an independent model to each recorded neuron. Using the model, the fraction of variance explained (FVE) was calculated by comparing the full model and actual calcium signal trace as an indicator of the accuracy of model prediction. To exclude non-task relevant neurons, we limited further analyses to those with at least 5% FVE. To assess contribution from a certain predictor, we calculated a tuning index defined as FVE(Full) – FVE (reduced), where FVE (reduced) is the FVE of the reduced model obtained by removing the predictor of interest from the full model Fig. . Tuning t is an estimate of the distance at lag t of the population vector from its time-averaged value, after accounting for the effect of the other predictors. By construction, then, the pathway kernels can never be negative, as they capture the overall magnitude of the effect of the predictor on the neural population, rather than a specific modulation direction. The statistical significance of the difference of pathway kernels in Figs. 60, performed with the Bias-Corrected and accelerated(BCa) technique and bootstrapping over the set of neurons belonging to the two pathways .Each neuron’s fitted model provided tuning estimates for all predictors, as well as neuron-level estimates of the model kernels such as those in Figs. 60. Choice epoch manipulations were continuous illumination from initiation poke to the end of the reward delivery delay following choice. Outcome epoch manipulations were continuous illumination from outcome delivery until next trial center light on. Either ΔWin-Stay or ΔLose-Stay was calculated as:To evaluate the behavioral role of each pathway (PL::A-DMS or PL::P-DMS), we used a Halorhodopsin-induced terminal suppression strategy . AAV5/CaMKii-NpHR3.0-eYFP (UNC vector core) or AAV5/Syn-GFP (Penn vector core) was injected bilaterally to the PL followed by bilateral implantation of custom-made optic cannulas  for pathway-specific light delivery into either A-DMS and P-DMS. The exposed internal portion of the fiber optic was completely painted by black nail polish, in an effort to reduce stray light emission above the fiber mouth. To ensure full expression of NpHR in the axonal terminal, a recovery period of at least 5 weeks was allowed after viral injection. Animals were acclimated to the fiber optic tethers for at least 5 days before any behavioral sessions. Once animals performed >200 trials/day with a relative reward stay >2 we proceeded to the optogenetic manipulation phase, the training proceeded to the light delivery stage. In the behavior task, two light delivery protocols  were used to assess temporally distinct contributions from each pathway. To prevent light-induced non-specific effect, light intensity was adjusted to 0.8–1.5 mW at the fiber endch Figs. , currentch Figs.  and prevch Figs. . Behaviokstest2 functions in MATLAB. Kernel density estimates were performed as indicated in results using ksdensity function in MATLAB. Significant effects and p-values are indicated in the figures and legends. Choice patterns for given Q value were regressed using glmfit function in MATLAB.All data were analyzed with prism9.0 and custom MATLAB code, available upon request. ANOVA , t-test (paired and unpaired) were performed using Prism 9.0 built-in-function. K-S tests were performed as indicated in results using Further information on research design is available in the Supplementary InformationReporting Summary


Fibrosing interstitial lung disease (F-ILD) is a major public health concern due to its poor prognosis. Recent clinical evidence shows that antifibrotic approaches such as pirfenidone and nintedanib provide better clinical outcome prediction in idiopathic pulmonary fibrosis (IPF) as well as selected progressive fibrosing ILD (PF-ILD) patients. Having epidemiologic insight into these diseases will be essential for the efficient utilization of these therapeutic resources. This study aimed to estimate the current prevalence, incidence, and mortality of F-ILD classified as idiopathic pulmonary fibrosis (IPF), PF-ILD other than IPF, and non-progressive F-ILD and their temporal trend in Korea.Population-based retrospective cohort study was conducted using the Korean Health Insurance Review and Assessment (HIRA) database (2011–2018). Patients with IPF were identified using ICD-10 code, RID code, and differential diagnosis approach. By leveraging medical records available from claim data and referencing those used in clinical trials, rigorous diagnostic criteria for PF-ILD detection were implemented.For the past eight years, the prevalence of IPF and PF-ILD has progressively increased, while non-progressive F-ILD has remained stable. IPF, PF-ILD, and non-progressive F-ILD prevalence per 100,000 in 2018 were 16.9, 10.4, and 11.7, respectively. The incidence of IPF in 2018 was more than twice that of 2012. The incidence of PF-ILD in 2018 was 1.5 times higher than that in 2012. In 2018, the mortalites were 10.3% and 12.2% for IPF and PF-ILD, respectively. The mortality rate of PF-ILD was greater than that of IPF in all years. Unclassifiable PF-ILD and rheumatoid arthritis-PF-ILD had the highest proportion and mortality among the PF-ILD subtypes.The prevalence and incidence of IPF and PF-ILD have been steadily increasing in recent years. The mortality rate of PF-ILD remained consistently high and exceeded those of IPF in all years.The online version contains supplementary material available at 10.1186/s12890-023-02373-z. Interstitial lung diseases (ILDs) refer to a heterogeneous group of parenchymal lung diseases that involve a number of common clinical and pathophysiological features while having a wide range of etiologies and prognoses. The most common type of ILD is idiopathic pulmonary fibrosis (IPF) and is considered as the prototype progressive-fibrosing ILD characterized by a decline in pulmonary function and poor prognosis with a median survival of 3–5 years , 2.In addition to IPF, a number of fibrosing ILDs can exhibit a progressive phenotype during the course of disease resulting in a decline in lung function and early mortality. For this group of fibrosing ILD with a progressive phenotype, the term progressive fibrosing ILD (PF-ILD) have been used widely in clinical field. But there is no standardized definition of progression and the criteria to define PF-ILD are variable in clinicians and researchers. Recently, instead of PF-ILD, the term progressive pulmonary fibrosis (PPF) is suggested and a consensus definition of PPF is determined in the 2022 ATS/ERS/JRS/ALAT clinical guideline. It is defined as at least two of the following three criteria occurring within the past year  [Due to morphological overlap and shared pathological traits, IPF and PF-ILD, which are subtyped according to their etiology, fall under the concept of progressive fibrosis phenotype. However, there are pathological distinctions between the two. IPF is primarily a fibrotic ILD, whereas in PF-ILD, fibrosis frequently occurs before or is associated with inflammation and the inflammation pathway results in an extracellular matrix that transforms healthy lung tissue into pulmonary fibrosis , 5.Although the disease burden by a poor prognosis of IPF and PF-ILD other than IPF (hereafter referred to as PF-ILD) is a major public health concern, epidemiological studies on these disorders are scarce and need to be updated –8. In caRecent clinical evidence shows that antifibrotic approaches such as pirfenidone and nintedanib guarantee better clinical outcome prediction in IPF as well as selected PF-ILD patients –19. SubsAs far as we know, there has been no study that investigates the comprehensive epidemiology and temporal variation of overall F-ILD by well-defined subtype for the entire national population. The purpose of this study was to estimate the prevalence, incidence, and mortality of comprehensive F-ILD classified as IPF, PF-ILD, and non-progressive F-ILD, progression rate among F-ILD other than IPF, and their temporal trend nationwide.https://www.hira.or.kr/rd/insuadtcrtr/InsuAdtCrtrList.do?pgmid=HIRAA030069000400). HIRA database included basically the whole Korean population (97%), unless participants’ eligibility was disqualified due to death or emigration. It provides information on details of all medical utilization including demographic characteristics, inpatients and out-patients services, diagnosis using international Classification of Disease, 10th revision (ICD-10) procedures, and prescription drugs. Individuals are “de-identified” as the database did not contain personal identifying information.The Korean Health Insurance Review and Assessment (HIRA) database was used in this study. South Korea has a mandatory single universal health coverage system  that the NHIS covers around 98% of the whole Korean population since 2000. HIRA is a government-affiliated organization that reviews and evaluates healthcare costs and healthcare service quality. The HIRA hold the comprehensive database containing all health care utilization information. , or at least two F-ILD diagnosis (ICD-10: J84.1) for the year based on ICD-10 diagnostic codes. For each year, the index date was defined as the first date that satisfies the case definition by the disease spectrum. Although there was no F-ILD diagnosis in the corresponding year, patients with F-ILD diagnosis in the previous one or two years and who have medical utilization in the year were also included in the case. Next, patients with the IPF, considered as a prototype of ILD, were first selected for those having IPF diagnosis (ICD-10: J84.18) and rare intractable disease (RID) code (V236) simultaneously, and having no differential diagnosis. NHIS has been operating a RIDs registration program since 2006, and IPF belongs to RIDs from 2011. Patients registered as RIDs receive a copayment reduction of up to 90%. Thus, diagnosis and billing for them are stricter in terms of insurance finances, making the data more reliable –26.For selecting PF-ILD patients, an alternative algorithm verifying the progression was developed by referencing INBUILD study and PROGRESS study , 19, 27.Index date, the first date that satisfies the case definition by the disease spectrum; F-ILD, fibrosing-interstitial lung disease; IPF, idiopathic pulmonary fibrosis; PF-ILD, progressive fibrosing- interstitial lung disease; PFT, pulmonary function test; HRCT, high-resolution computed tomography; CT, computed tomography.The subtypes of both PF-ILD and non-progressive F-ILD were divided into hypersensitivity pneumonitis-ILD, autoimmune diseases-ILD -ILD including Sjogren syndrome, and Systemic Lupus Erythematosus (SLE) or dermatomyositis-ILD), sarcoidosis-ILD, ILD due to external factors, and patients whose subtype of ILD could not be identified as unclassifiable ILD. These subtypes were identified using corresponding diagnostic code during 180 days prior to the index date (supplementary Table 1).Descriptive statistics were used to summarize the characteristics of patients with IPF, PF-ILD, and non-progressive F-ILD including sex, age, type of insurance, type of institution for each year based on the specifications of the index date. Charlson Comorbidity Index (CCI) was measured using the diagnosis history for 12 months prior to the index date. The chi-square test or Fisher’s exact test was used to assess the difference between the disease subgroups.Patients who met each disease group’s criteria stated in the ‘Selection of cases’ section above were considered prevalent cases. Prevalence was calculated by dividing the number of cases by the total population in South Korea. To be included as an incident case, the patient in the current year must not have been selected in the previous calendar year. Incidence rate was calculated by dividing the number of incident cases by the population at risk. Case mortality was determined by dividing the number of deaths by the number of prevalent cases. The mid-year population was used for the whole domestic population, with data from the Korea Statistical Information Service (KOSIS) . PrevaleAs part of the sensitivity test, in order to assign PF-ILD patients to subgroups, instead of checking for ICD-10 codes corresponding to subtypes until 180 days before the index date, ICD-10 code in the year of the first diagnosis of PF-ILD was used as a criterion for subtype determination.All data were analyzed using the SAS statistical application program . The study protocol was approved by Sungkyunkwan University’s Institutional Review Board (IRB No. 2020-03-001). Informed consent was waived by the Sungkyunkwan University’s Institutional Review Board.Table The prevalence by year from 2011 to 2018 were summarized in Table Table Table In 2018, among the subtype of PF-ILD identified, the number of patients whose specific disease was unknown accounted for 50% of the cases. Rheumatoid arthritis-ILD was accounted for the next highest proportion (31.4%), followed by other CTD-ILD including Sjogren syndrome (7.9%) and systematic sclerosis-ILD (5.6%). Among non-progressive F-ILDs, 53.7% belonged to unclassified, and most of the classifiable subtypes belonged to rheumatoid arthritis-ILD (39.5%) .The progression rate of F-ILD other than IPF calculated by dividing the number of PF-ILD patients by the number of F-ILD other than IPF patients steadily increased from 19.1% to 2011 to 46.9% in 2018. Except for sarcoidosis, all subtypes showed a 2-5-fold increase (Supplementary Table 3).Among the subtypes of PF-ILD, the incidence rate of rheumatoid arthritis-ILD increased steadily from 2012 to 2018, and the incidence rate per 100,000 people in 2012 was 0.64 cases and 1.19 cases in 2018. On the other hand, the incidence rate of other classified subtypes did not show much difference by year. The incidence per 100,000 in the unclassifiable subgroup increased 1.4 times from 1.07 to 2012 to 1.54 in 2018. Non-progressive F-ILD showed a decreasing trend in systemic sclerosis-ILD, from 0.14 to 2012 to 0.076 in 2018. Incidence rates in other subtypes did not show an annual trend (Supplementary Table 4).The annual trend of the mortality rate of PF-ILD was different for each subtype. Prevalence of autoimmune diseases-ILD decreased slightly from 13.9% to 2012 to 10.5% in 2018. On the other hand, unclassifiable ILD, which accounts for the largest proportion among subtypes, was remained nearly unchanged from 14.8% to 2012 to 13.8% in 2018, and the mortality rate of sarcoidosis-ILD and ILD due to external factors increased significantly. (Supplementary Table 5).As part of the sensitivity test, in order to assign PF-ILD patients to subgroups, instead of checking for ICD-10 codes corresponding to subtypes until 180 days before the index date, ICD-10 code in the year of the first diagnosis of PF-ILD was used as a criterion for subtype determination. The results of the sensitivity test using the year of the first diagnosis of PF-ILD as the criterion for subtype determination were not significantly different from the primary results (Supplementary Table 6).In this cohort study, the epidemiology of F-ILD from 2011 to 2018 was explored in Korea using national data. The prevalence of IPF and PF-ILD increased steadily for eight years, and non-progressive F-ILD was maintained. In 2018, the prevalence of IPF, PF-ILD, and non-progressive F-ILD per 100,000 were 16.9, 10.4, and 11.7, respectively. The incidence of IPF in 2018 was 6.2 per 100,000, more than twice as high as in 2012, and that of PF-ILD in 2018 was 3.1 per 100,000, which was 1.5 times higher than that in 2012. In 2018, the mortality rates were 10.3% and 12.2% for IPF and PF-ILD, respectively. The mortality of PF-ILD was higher than that in IPF in all years.The prevalence of IPF Korea seems to be similar to or somewhat lower than that of European and North American countries , 31, 32.Epidemiologic studies of IPF in Asian countries are rare. As a domestic study targeting 30 years of age or older, the incidence by narrow definition was 1.84 per 100,000 people using data from 1992 to 2000 . In a moWhile findings on the temporal trend of IPF epidemiology are mixed, , 39–41 tWhile a large number of non-IPF ILDs develop into a progression phenotype with poor prognosis, there is little epidemiologic research on them , 44. In In a systematic review that recently combined the published literature providing the prevalence of ILD and survey data to estimate the proportion of progressive phenotype, the overall prevalence of PF-ILD in Europe and the US were estimated to be 2.2–20.0 per 100,000 and 28.0 per 100,000 people, respectively . In our The prevalence and incidence of PF-ILD increased significantly between 2012 and 2018, by 4.3 and 1.6 times, respectively. Despite the possible influx of earlier patients by diagnostic technology like HRCT,  the progWhile direct comparisons between studies are cautious due to differing target groups and subclassification criteria, the proportion of subtypes in PF-ILD also varied from study to study. For example, whereas the proportion of rheumatoid arthritis-ILD was quite high in our study (31% vs. 4%), that of systemic sclerosis was remarkably high in the PROGRESS study. (26% vs. 6%) (11) In the clinical cohort study in the UK, the most common diagnoses associated with a PF-ILD were hypersensitivity pneumonitis-ILD (33.2%), unclassifiable ILD 17.3%), and connective tissue disease-ILDs including rheumatoid arthritis-ILD (16.6%), while our study indicated as 1%, 50%, and 47%, respectively. Connective tissue disease-associated ILD  and sarcoidosis  were reported as the most common subtypes in the systematic literature review, [.3%, and The mortality of PF-ILD also varied by subtype, and the high mortality in unclassifiable ILD, which accounts for nearly half of all PF-ILD, is consistent with the previous study . Such a Additionally, we found that even three years after antifibrotic agents such as pirfenidone and nintedanib were introduced, half of the patients with IPF did not receive antifibrotic treatment. Out of 8647 IPF patients in 2018, only 4220 patients had received a prescription for antifibrotic agents within one year after the index date. It is also reported that over one-third of PF-ILD patients were not receiving any medication . It indiThis study has several strengths. First, this is the first epidemiologic study to investigate the prevalence, incidence, and mortality rates of IPF, PF-ILD, and non-progressive F-ILD throughout the entire national population. Due to the rarity of these disorders, this population-based approach is thought to be particularly suitable for better estimation. Second, our study tried to minimize diagnostic misclassification by making the operational definition to find the progressive phenotype in the claim data aligning to that used in the clinical trials , 50. ThiThe present study has some limitations. First, while the case was carefully defined utilizing all medical records available from the claim data, verification of its accuracy is a future task. Specifically, it cannot be ruled out that the high proportion of unclassifiable F-ILD in this study may have attributed to the subtyping algorithm developed based on medical records available from claim data. Second, this study did not perform a stratification analysis based on basic demographic category such as gender and age. In addition, co-morbidities or medications that could affect the development and prognosis of F-ILD were not considered. Future research incorporating these factors will be needed. Finally, there were changes such as the introduction of the antifibrotic agent in 2015 and evidence supporting antifibrotic therapy in PF-ILD are adding , 19 InveKorea’s claim data includes information on the entire national citizen’s medical use, allowing for long-term follow-up. Survival analysis is available since the data is not segmented. These considerations suggest that the utilization of these data for rare disorders like F-ILD has greater utility because all cases can theoretically be captured. If future claims data are used to evaluate survival according to treatment decision for each illness category of F-ILD, it will be valuable real-world evidence for reducing the healthcare burden in terms of clinical and public health.In a population-based study to investigate the epidemiology of F-ILD in Korea for the past 8 years, the disease burden of IPF and PF-ILD was comparable to or slightly lower than that of European and North American countries. The incidence and prevalence of IPF and PF-ILD have gradually increased since 2011. The mortality rate of PF-ILD was consistently high and exceeded those of IPF in all years emphasizing unmet healthcare needs. The epidemiology of PF-ILD varied depending on the subtype showing a remarkably high proportion and mortality in unclassifiable PF-ILD and rheumatoid arthritis-PF-ILD.Below is the link to the electronic supplementary material.Supplementary Material 1


Monolayer graphene was also found in similar standard procedures, but it required higher growth temperature and longer time periods. The cost-efficient growth conditions for few-layer graphene are thoroughly discussed via TEM observation and AFM measurement. In addition, it has been confirmed that the growth period can be shortened by increasing growth temperature. With the H2 gas flow rate fixed at 15 sccm, few-layer graphene was synthesized at the lower growth temperature of 700 °C in 30 min, and at 900 °C growth temperature in only 5 min. Successful growth was also achieved without adding hydrogen gas flow; this is probably because H2 can be induced from the decomposition of methanol. Through further defects study of few-layer graphene via TEM observation and AFM measurement, we tried to find possible ways for efficiency and quality management in graphene synthesis in industrial applications. Lastly, we investigated graphene formation after pre-treatment with different gas compositions, and found that gas selection is a crucial factor for a successful synthesis.Few-layer graphene was successfully synthesized on copper foil via chemical vapor deposition with methanol as a carbon source. This was confirmed by optical microscopy observation, Raman spectra measurement, I Withructures , resultiructures . Transisructures . Monolayructures . Therefoructures  and heatructures  for indu2/graphene/Ni electrodes exhibits specific capacitance and mechanical stability improvement because of the buffer layer of graphene between MnO2 and Ni foams [The application of graphene composite and graphene-based inorganic nanocomposites currently has attracted more and more attention for enhancing the specific characteristic performance in various kinds of composite materials. For example, the cytotoxicity studies of biocompatibility with graphene/CNT hybrid silicone composites, exhibit high elasticity, good durability, low resistivity, and prominent electrical stability because of the few-layer graphene and CNTs ratio and hybrid filler loading . Prior sNi foams . The fieNi foams . AnotherNi foams . MoreoveNi foams . These iNi foams . CompletNi foams . TherefoNi foams .Graphene can be commonly prepared via solid phase methods such as mechanical exfoliation  and SiC Graphene growth temperatures are dependent on two key factors of precursors and substrates. The layer number and quality of graphene are dependent on the catalytic activities and carbon solubilities of the catalysts used. Intensive works have reported graphene synthesis with polycrystalline nickel  and copp−2 Torr, follow by a dry pump for graphene growth at the same fixed pressure to avoid the impact of the mechanical pump’s oil–gas backflushing to the system.The graphene synthesis equipment in this study is a self-assembled low-pressure chemical vapor deposition (CVD) system with a quartz tube , a tubular high-temperature furnace, a mechanical pumper, a dry pumper, and a steel cup. High-purity argon (Ar: 99.999%) was used as the carrier gas passing through the steel cup containing methanol, and then the methanol can be carried into the growth system as a carbon source. In the high-temperature experimental stage, the mechanical pump was used to pump the system pressure to 102. At the beginning of the experiment, the copper foil in the crucible was placed into the quartz tube. Evacuating the system pressure to 10−2 Torr by using the mechanical pump followed by the dry pump with a flow rate of 200 sccm (20% H2/80% Ar) for 1 h to ensure the oxygen in the quartz tube was completely removed. For the growth temperature study, the tubular furnace with H2/Ar atmosphere (200 sccm) was heated to the desired growth temperature of 500 °C, 700 °C, 800 °C, 850 °C, 900 °C, and 950 °C, respectively, for 30 min. The copper oxide residue on the copper foil surface was then eliminated. After 30 min. of copper oxide residue elimination, the H2/Ar atmosphere flow rate was reduced from 200 sccm to 35 sccm with the composition of 15/20 sccm in the desired growth temperature. The methanol in the steel cup was carried into the system via the carrier gas of Ar for 30 min. of operation. For the gas composition study, the tubular furnace with H2/Ar atmosphere (200 sccm) was heated to the growth temperature of 900 °C for 30 min. After 30 min. of copper oxide residue elimination, the H2/Ar atmosphere flow rate was, respectively, reduced from 200 sccm to the desired composition with H2 of 0, 10, 15, 20 sccm, and Ar of 20 sccm as a carrier gas for methanol into the system for 30 min. of operation. For the growth period study, the tubular furnace with H2/Ar atmosphere (200 sccm) was heated to the growth temperature of 900 °C for 30 min. After 30 min. of copper oxide residue elimination, the H2/Ar atmosphere flow rate was reduced from 200 sccm to 35 sccm with a composition of 15/20 sccm. The methanol in the steel cup was carried into the system via the carrier gas of Ar for 30 min. of operation. At the end of the temperature, gas composition, and growth period experiment, the carbon source stopped supplying while accomplishing the graphene growth. The tubular furnace was cooled down to room temperature under the H2/Ar atmosphere flow rate of 200 sccm. The obtained samples were taken out of the tubular furnace after the system pressure was recovered to 1 pressure atmosphere and subjected to OM and Raman analysis.All the growth substrates were cut from copper foil ,) with a thickness of 25 μm into many square pieces with a size of 0.5 × 0.5 cmPreliminary observations of all samples were made by using an upright optical microscope, also known as a metallographic microscope. The light source used is a mono-wavelength beam passed through a filter to reduce chromatic aberrations. The images of these microstructures are examined using an external computer and analysis software with an image processing function. The best identification rate of an optical microscope is 2000 Å, and the magnification is about 1000–1500 times. The quality of graphene and the number of layers were confirmed using Raman spectroscopy  with the excitation laser light source of 532 nm wavelength and laser spot width of ~2 μm, combined with an analysis device which is composed of an analytical light spectrometer and a low-temperature CCD. The Raman spectroscopic analysis can be performed with the test piece without special treatment. While performing, three different positions were measured in each sample. Every set of Raman data is the average value of two results. The number of layers and the quality of graphene can be analyzed from the peaks in the Raman spectrum. Transmission electron microscope, (TEM) (JEOL TEM-3010) and multi-functional scanning probe microscope, (Icon-AFM) (BRUKER Dimension Icon) were utilized for further observation. While the TEM samples and the AFM specimens were prepared, graphene was ultrasonically oscillated in anhydrous alcohol, and subjected to, respectively, a drop of evenly dispersed liquid on a carbon-coated copper grid, and on the Si substrate, followed by vacuum drying. AFM measurements were performed in the scan range of 5 μm × 5 μm × 5 nm with a resolution of X, Y < 1.0 nm, Z < 0.1 nm.2 at growth temperatures of 500 °C, 700 °C, 800 °C, 850 °C, 900 °C, and 950 °C in the time period of 30 min, respectively. Any graphene generated from the figure in the 500 °C growth temperature can not be observed. This result agrees with the report from Gadipelli et al. [i et al.  because −1 and 2650~2700 cm−1, respectively. The number of graphene layers can be realized through the intensity ratio of these two peaks as I2D/IG. Moreover, the number of graphene layers can also be confirmed from the position of the 2D band. If the peak of 2D band showed a higher frequency of movement, a so-called blue-shift phenomenon, the graphene is a multi-layer graphene. Note that, if a D band is found at 1250~1350 cm−1 in the Raman spectrum, which is a defect peak, the graphene shows a poor quality because of the disordered graphene structures. In addition, monolayer graphene shows the magnitude of I2D/IG ratio greater than 1.5. The number of graphene layers can also be determined by the 2D half width at half maximum (2D-FWHM) value. Monolayer graphene shows the magnitude of FWHM value around 30–36 cm−1. The magnitude of 2D-FWHM value greater than 36 cm−1 can be classified as double layers or few layers graphene.As is well known, the typical Raman spectra of graphene show the G band and 2D band at about 1500~1600 cm−1, respectively, in the growth temperature of 700 °C. The I2D/IG ratio and the 2D-FWHM value were calculated as about 0.33 and 66 cm−1, respectively, which proves that graphene can not only be synthesized at the temperature of 700 °C, but also a at few-layered properties. However, the defect peak of D band is obviously found with the ID/IG ratio of about 1.07, indicating lots of defects exist. Gadipelli et al. reported that methanol can start decomposing at 700 °C to produce H2, CO, and CH4, and be completely decomposed at 800 °C resulting in the graphene synthesized with CH4 is usually at around 900~1000 °C. Therefore, the carbon source for the graphene synthesis in the temperature of 700 °C is hardly from the CH4. Both the oxygen atoms decomposed from CO, and H2 produced from methanol can be easily combined to form a little water and be taken out of the furnace tube by pumping. Therefore, the main source of carbon for synthesizing graphene in 700 °C could be probably from the CO. As the growth temperature increased in 800 °C, 850 °C, 900 °C, and 950 °C, the I2D/IG were 0.80, 0.74, 1.60, 1.34, and the 2D-FWHMs were 60, 54, 30, and 36 cm−1, respectively. It is found that I2D/IG gradually increased, but 2D-FWHMs gradually decreased, as increasing the growth temperature. But as the growth temperature increases above 900 °C, the I2D/IG ratio decreases, and the 2D FWHMs increases slightly. The defect peak of D band is obviously found with the ID/IG ratio of about 1.07, indicating lots of defects exist. Moreover, the D band decreased as the growth temperature increased, and the defect peak was hardly observed while the growth temperature was above 900 °C.The characteristic Raman spectra peaks of graphene in this step, respectively, show the D band, G band, and 2D band at ~1350, ~1580, and ~2700 cm2D/IG ratio, 2D-FWHM, and ID/IG ratio corresponding to the growth temperature. It can be clearly seen that the good quality of monolayer graphene was successfully synthesized at a temperature around 900 °C. In the graphene synthesis works using a gaseous carbon source, the growth temperature needs to be around 900–1000 °C [–1000 °C ,47,48,49–1000 °C ,51. In a–1000 °C  also ann2 decomposed from the thermal cracking of methanol as the reducing agent. Because methanol can play two roles in high-temperature decomposition, one is as the source of carbon, and the other is as the generation of OH radicals which can also inhibit the formation of amorphous carbon. Consequently, graphene can also be simply synthesized by using the H2 decomposed from the thermal pyrolysis of methanol as a reducing agent without directly adding hydrogen as a reducing gas. Hydrogen not only can reduce copper oxide and remove impurities on the surface of copper foil but also can restrain the formation of amorphous carbon [It worth mentioning that Gadipelli et al.  reporteds carbon . Choubaks carbon . Therefo−1 and 2650~2700 cm−1 on all specimens. Notably, the impurity peak of the D band cannot be found on all grown specimens indicating suitable parameters management on atmosphere compositions and temperatures. The I2D/IG ratio and 2D-FWHM value of graphene grown in hydrogen flow rates of 0, 10, 15, and 20 sccm were calculated as 0.87, 0.71, 1.60, 0.9, and 48 cm−1, 54 cm−1, 30 cm−1, 54 cm−1, respectively. Both values were found to be returned after 15 sccm reached an extreme indicating a quality inversion.2D/IG ratio, 2D-FWHM value, and ID/IG ratio corresponding to the hydrogen gas flow rate . It can be seen that a number of monolayer graphene explicated with 1.6 of I2D/IG ratio and 30 of 2D-FWHM value has been successfully completed with a confirmation of hydrogen gas at 15 sccm flow rate in this stage of growth work. In addition, the number of graphene layers tended to be increased as the hydrogen gas flow gradually deviates from 15 sccm. This result proved that proper hydrogen flow rate is conducive to the few-layer graphene growth. Si et al. have reported that the influence of the hydrogen gas flow rate on carbon source in the graphene synthesis is that the number of graphene layers increased from monolayer to double layers as the hydrogen flow rate increased from 0 to 3 sccm [4/H2 ratios cause larger sizes of graphene crystals [4 on graphene by using density functional theory (DFT) and found that the dissociation of CH4 into CH3 and H is a rate-determining step [o 3 sccm . Wang etcrystals . In addiing step .Since the time course of sample shipment is affected by the processing time. The cost of growth is also dependent on the uninterrupted supply of gas and thermal energy while synthesizing. Therefore, the growth time period is also one of the key factors of the production cost for the finished product. The next step should be to study the efficiency times of few-layer graphene synthesis.2/Ar gas flow compositions of 15/20 sccm, and suitable temperatures of 900 °C in different time durations of 5, 20, 30, 35, and 60 min., respectively. It can be clearly observed that all specimens with different growth times duration show the graphene characteristic peaks. The D band has also been found at ~1350 cm−1, whereas the synthesis period is in 5, 35, and 60 min. in which the ID/IG is calculated as 0.33, 0.42, and 0.54, respectively. The D band indicates the disordered arrangement of graphene structures inside. No D band was observed at the synthesis time duration of around 20~30 min. indicating an optimal growth period. Moreover, either shortening or extending the process period will result in the degradation of graphene quality. The I2D/IG ratio and 2D-FWHM value at different growth periods of 5, 20, 30, 35, and 60 min. were, respectively, calculated as 0.47, 0.62, 1.38, 1.60, and 1.43, and 66, 54, 36, 30, and 60 cm−1.2D/IG ratio and 2D-FWHM value of Raman spectra corresponding to the graphene growth period of 5, 20, 30, 35, and 60 min. in accordance with Raman spectra of 2D/IG ratio decreases slightly while the growth time further extends to 60 min. indicating a degradation of graphene quality because of the prolonged growth duration. Therefore, the growth period of around 30–35 min. completed the good quality of the few-layer graphene synthesized in this study. The growth period of around 30 min. for industrial application is our priority suggestion because of the resources, cost saving, and time efficiency.2/Ar gas flow compositions of 15/20 sccm, temperature of 900 °C, and time duration of 30 min. Further characterizing the graphene, TEM observation was involved. Z-axis. AFM is an alternative tool for measuring the thickness of graphene due to the 0.1 nm precision in 2 gas flow rate fixing at 15 sccm in a lower growth temperature of 700 °C within 30 min or in a growth temperature of 900 °C within only 5 min. Graphene can’t be synthesized with both the lower temperature of 700 °C and a shorter time duration of 5 min indicating that few-layer graphene is hardly synthesized, saving both the thermal energy and time period. Moreover, few-layer graphene growth at a lower temperature of 700 °C within 30 min shows more defects than that at a temperature of 900 °C within only 5 min, indicating the choice of improvement. This defects observation also proved that the growth period can be saved by increasing the growth temperature.In summary of the Raman spectra studies, few-layer graphene can be synthesized with the H2 and Ar are generally used to drive away the oxygen and impurities in the quartz tube and related pipelines of the system and further reduce the copper oxide on the surface of the copper foil substrate while the temperature is increasing to the reaction condition. In order to further discuss the function and role of pretreatment gas, the relationship between different pretreatment gas compositions and graphene formation has been investigated.It should be noted that at the beginning of the graphene growth process via the chemical vapor deposition method, blunt gas and reducing gas such as H2/Ar of 15/20 sccm carrying methanol at a growth temperature of 900 °C and a growth period of 30 min, with a pretreatment atmosphere of (a) H2/Ar and (b) Ar gas compositions. It can be seen in the optical microscopy photos that the copper foil surface is clear and shining with the H2/Ar atmosphere pretreatment, but many black dots are distributed on the copper foil surface with the pure Ar atmosphere pretreatment. These black dots are probably not the graphene formation but the deposition of contaminants on the copper foil surface because the shape of these black spots does not resemble the shape of a graphene hexagonal structure. For the confirmation of this feature, the copper foil substrates with stander graphene growth processes using different pretreatment gas of H2/Ar or Ar atmospheres have also been inspected via Raman spectroscopy measurement.2/Ar or Ar. It is found that the G band and 2D band graphene characteristic peaks, respectively, appear at ~1600 cm−1 and ~2700 cm−1, when H2/Ar is used as the pretreatment gas.2/Ar as the pretreatment gas at high temperatures, which is conducive to graphene generation.However, no graphene characteristic peak is shown on copper foil substrates pretreated with pure Ar gas pretreatment. It can be deduced that possible contaminants on the surface of the copper foil can be removed by using H2D/IG ratio calculation, and 2D-FWHM value comparisons proved that few-layer of graphene had been successfully synthesized on copper foil with methanol as a carbon source by the chemical vapor deposition method. It is also found that the cost-efficient growth condition in H2/Ar atmosphere flow ratio, temperature, and time period are 15/20 sccm, 900 °C, and 30 min, respectively. A number of monolayer graphenes was also found in similar standard procedures, but they required higher growth temperatures and longer time periods. In addition, graphene can be synthesized without adding hydrogen gas flow because the H2 can be induced from the decomposition of methanol. With the H2 gas flow rate fixed at 15 sccm, few-layer graphene was synthesized at the lower growth temperature of 700 °C in 30 min, and at 900 °C growth temperature in only 5 min. Few-layer graphene growth in a lower temperature of 700 °C within 30 min. shows more defects than that in a temperature of 900 °C within only 5 min indicating that the quality of few-layer graphene can be improved by increasing the growth temperature without extending the growth periods. Moreover, pretreatment atmosphere composition choosing before graphene growth is a significant procedure because it may deduce a successful synthesis or not.Optical microscopy observation, Raman spectra measurement, I


Lung cancer is responsible for a significant number of cancer-related deaths worldwide. While various factors can lead to lung cancer, such as tobacco smoking, this article focuses on the relationship between sarcoidosis, a multisystem granulomatous disorder, and lung neoplasm. To investigate this association, the authors conducted a literature search using relevant keywords. The analysis of these reports concluded that while Sarcoidosis and lung cancer together is rare, it is possible. The presenting symptoms, age, gender, and diagnostic procedures of each case should be evaluated, and appropriate diagnostic procedures should be carried out to determine the appropriate treatment for each patient. Clinicians need to be aware of the possibility of these two diseases co-occurring, as they can impact the management of the patient's condition, whether it is curative or palliative. It is essential to rule out metastatic cancer in individuals with sarcoidosis-like clinical and radiographic features. Lung cancer is a severe concern today, as it has become the leading cause of cancer-related deaths for both men and women worldwide. Its insidious onset, early metastasis, and poor prognosis make it all the more concerning . It is eSarcoidosis affects multiple body areas, including the lungs, lymph nodes, liver, eyes, and skin. The disease is systemic and characterized by the formation of multiple granulomas of unknown origin . HoweverIndividuals with sarcoidosis must receive proper medical care and monitoring to manage potential complications and improve their overall health outcomes. Respiratory failure, pulmonary fibrosis, central nervous system involvement, and cardiac damage are severe risks associated with sarcoidosis, which can be life-threatening if not adequately addressed . It is eProper diagnosis is crucial to ensure that individuals with sarcoidosis receive the appropriate medical care and monitoring to manage potential complications and improve their overall health outcomes. Table Based on available data, the outlook for individuals with pulmonary sarcoidosis is positive. Up to 80% of patients with stage I disease have been observed to experience spontaneous regression of radiographic abnormalities. Additionally, the rate of development of chronic respiratory impairment is relatively low, affecting less than 5% of patients over 10 years . It is iCorticosteroid medications are a commonly prescribed treatment for individuals with severe symptoms and advancing pulmonary disease or significant extrapulmonary illness caused by sarcoidosis. In some cases, immunosuppressive treatment may also be necessary .This study focuses on rare cases reported and research articles published globally in the last 10 years to examine the relationship between sarcoidosis and lung cancer and determine whether there is a relationship between the two. It also offers guidance to doctors on how to manage such uncommon encounters.Even though there have been numerous documented associations, the relationship between lung cancer and sarcoidosis is still unclear. The authors break down the discussion session into theories, the diagnostic challenges, and the inflammatory responses related to sarcoidosis that may lead to neoplasm.TheoriesCancer seems more common in sarcoidosis patients, including malignancies of the liver, stomach, small intestine, lung, and skin, as well as non-Hodgkin's lymphoma and leukemia. Chronic inflammation is associated with a high risk of malignant lymphoma or cancer in the affected tissue . TheoretEpidemiological studies show that at least 25% of patients with sarcoidosis and neoplasm may have an etiological relationship . NumerouIn a study conducted in Japan from 1980 to 2007, three cases of sarcoidosis and lung cancer were noted. In one case, the patient developed lung cancer 16 years after being diagnosed with sarcoidosis. In another case, the patient had two different lung cancers, occurring 10 and 18 years after the diagnosis of sarcoidosis. In the third case, both illnesses were detected simultaneously. However, It can be challenging to ascertain whether the coexistence of non-caseating epithelioid cell granulomas with lung cancer in simultaneously detected cases represents a lung cancer reaction or genuine systemic sarcoidosis. These three cases were shown to have smoking indexes of 300,720 and 70, respectively [Ungprasert et al. discovered a higher incidence of malignancy among patients with sarcoidosis than those without sarcoidosis. However, it is essential to note that this relative risk was insignificant once detection bias was accounted for . In anotDiagnostic challenges and differential diagnosisA non-caseating granuloma can help confirm a diagnosis of sarcoidosis, especially in optimal clinical settings . In addiSarcoid-like reactions are autoimmune responses that resemble the sarcoidosis symptoms. Different etiologies, including infections most frequently, might cause this reaction. Additionally linked to sarcoid-like reactions are cancer, medicines, and other autoimmune responses . These tCases with concurrent sarcoidosis and malignant disease may help illuminate the causal relationship between them, even though the distinction between sarcoidosis and sarcoid responses in their mechanisms of development is still unclear . These gA popular diagnostic tool for detecting increased metabolic activity, fludeoxyglucose-positron emission tomography (FDG-PET), has a 96% sensitivity and a 79% specificity for lung cancer. The standardized uptake value (SUV), used to quantify a tumor's metabolic activity, is highly predictive of malignancy when it is more significant than 2.5. This test, however, may yield false-positive results in granulomatous diseases like sarcoidosis . The lymEven though the likelihood of sarcoidosis and lung cancer coexisting is extremely low (less than 1%) of the time, it is more frequently seen in squamous cell lung cancers . When diAccording to Koda et al., some textural traits have been shown to be able to distinguish SCLC from sarcoidosis in patients with mediastinal lymphadenopathy on CT. The combination of EBUS elastographic fat-to-lesion strain ratio and textural feature grey-level run length matrix with high gray-level run emphasis yielded nearly excellent diagnostic capability for discriminating between these two diseases. Therefore, enlarged mediastinal lymph nodes with CT-based texture analysis may help distinguish between sarcoidosis and other diseases that cause mediastinal lymphadenopathy, such as malignant lymphoma and metastatic lymph nodes associated with NSCLC .Pulmonary sarcoidosis has frequently been misdiagnosed as lung cancer, according to reports. In a rare instance, pulmonary sarcoidosis was mistakenly diagnosed as progressing lung cancer, delaying the detection of the disease . The misMedication safety margin in immunotherapy in alternative theories contends that lung cancer chemotherapy, tyrosine kinase inhibitors, or immunological checkpoint inhibitors like ipilimumab and nivolumab that trigger local sarcoid responses may be the cause of sarcoidosis . In justInflammatory response to sarcoidosisThere are conflicting views on the link between sarcoidosis and lung cancer. Some studies suggest that people with sarcoidosis may be at a higher risk of developing lung cancer and experiencing mortality. In contrast, others indicate that the incidence is not higher than expected. Nevertheless, the persistent inflammation and scarring that come with sarcoidosis and abnormalities in cell-mediated immunity could potentially be a factor in triggering cancer development .Active sarcoidosis is characterized by an enhanced local expression of T helper 1 (Th1) and T helper 17 (Th17) chemokines and cytokines like IFN-γ, TNF-α, IL-17A, and IL-22. In various chronic, autoimmune, inflammatory diseases, such as sarcoidosis, the percentage of IL-17A+/IFN-γ+ double-producing Th-cells increases in peripheral blood and is related to high disease activity -26. FurtInterestingly, during sarcoidosis, the expression of cytotoxic T-lymphocyte antigen 4 (CTLA-4) is decreased, while the expression of programmed death-1 (PD-1) is increased in Th17-cells in the mediastinal lymph nodes . Th-cellFurthermore, S100A8/A9 (belongs to the Ca2 + binding S100 protein family) levels in serum are higher in sarcoidosis patients, and monocytes and multinuclear giant cells express S100A8/A9 more cytoplasmically in granulomas. We might surmise that the inflammation associated with sarcoidosis, which is characterized by the expression of IFN, IL6, IL23, and S100A8/A9, shares a milieu with malignancies, which are distinguished by an elevated accumulation and immunosuppressive role of myeloid-derived suppressor cells (MDSC). As a result, sarcoidosis can encourage metastasis by causing cell subsets that promote tumors and regulate the immune system . More reLimitationsFirstly, many of the studies are retrospective, which may introduce bias and limit the ability to establish causality. Additionally, diagnosing sarcoidosis may be complex and subject to inter-observer variability, leading to misclassification and underestimation of the true prevalence of sarcoidosis in lung cancer patients. Furthermore, the heterogeneity of sarcoidosis in terms of clinical presentation and disease course may make it challenging to identify specific subtypes more strongly associated with lung cancer. Finally, the studies included in this review varied widely in terms of sample size, study design, and patient population, which may limit the generalizability of the findings. These studies cited in the article have not demonstrated a clear control adjustment for other variables, especially the ones that cause lung cancer including smoking, environmental exposure, etc. Overall, while the association between lung cancer and sarcoidosis is an essential area of research, further studies are needed to understand better the underlying mechanisms and potential clinical implications of this relationship.When dealing with a potential case of sarcoidosis, it is crucial to remember that it can mimic various other diseases, including infectious, neoplastic, and granulomatous diseases. Furthermore, individuals with sarcoidosis may be more susceptible to lung cancer. This is consistent with the elevated risk of cancer reported in other inflammatory respiratory diseases, and rather than sarcoidosis itself, the risk may be mediated by the accompanying cell regeneration. Therefore, conducting a thorough assessment and maintaining a diverse differential diagnosis is vital. Even though sarcoidosis and lung cancer seldom coexist, doctors should investigate the possibility of lung cancer in sarcoidosis patients who do not respond to early corticosteroid treatment and obtain a biopsy to establish pathological findings from any suspected lung lesion.While there is no evidence of a link between sarcoidosis and lung adenocarcinoma, physicians should ensure no lesion is overlooked in individuals without a biopsy. This structured approach can improve the patient's outcome and prognosis while detecting malignancy earlier. Given the diagnostic conundrum of not being able to differentiate sarcoidosis and lung cancer, it is better to treat the nodal involvement as SCLC metastasis because failure to do so might result in relapse and jeopardize survival. Lastly, more cohort studies are required to enhance the body of literature already in existence. It may lead to the discovery of answers to the link between lung cancer and sarcoidosis.


Allergy. The final part of the survey involved asking ChatGPT the same statistical question three times. Results: Out of the 40 general questions asked that related to broad statistical analysis, ChatGPT did not fully answer half of them. Assumptions necessary for the application of specific statistical tests were not included. ChatGPT also gave completely divergent answers to one question about which test should be used. Conclusion: The answers provided by ChatGPT to various statistical questions may give rise to the use of inappropriate statistical tests and, consequently, the subsequent misinterpretation of the research results obtained. Questions asked in this regard need to be framed more precisely.Background: Content generated by artificial intelligence is sometimes not truthful. To date, there have been a number of medical studies related to the validity of ChatGPT’s responses; however, there is a lack of studies addressing various aspects of statistical analysis. The aim of this study was to assess the validity of the answers provided by ChatGPT in relation to statistical analysis, as well as to identify recommendations to be implemented in the future in connection with the results obtained. Methods: The study was divided into four parts and was based on the exemplary medical field of allergology. The first part consisted of asking ChatGPT 30 different questions related to statistical analysis. The next five questions included a request for ChatGPT to perform the relevant statistical analyses, and another five requested ChatGPT to indicate which statistical test should be applied to articles accepted for publication in  Allergy pointed out that ChatGPT can generate reliable but incorrect and meaningless responses. For this reason, it is essential to keep expert supervision in mind when publishing this type of material [PLOS Medicine, Professor John P. A. Ioannidis argued that the results of the majority of scientific publications are fraudulent [PLOS ONE show that between 1998 and 2020, there was unfortunately no significant change in the frequency with which medical journal editors used statistical reviewers [PLOS Biology point to the need for education through critical analysis of accepted manuscripts [Allergy is the official journal of the European Academy of Allergy and Clinical Immunology, the first aim of this research was to determine the validity of the statistical analyses used by the authors of the articles published in this journal. The second objective was to analyse the validity of the statistical analyses carried out by ChatGPT on the datasets created, the subjects of which were related to allergology. The third objective of this research was to investigate the correctness of ChatGPT’s answers to questions on various statistical issues, using the medical field of allergology as an example. The fourth objective of the manuscript was, using one statistical question asked of ChatGPT as an example, to indicate the variety of answers given. The final objective was to provide statistical recommendations to bear in mind when considering the use of ChatGPT for conducting statistical analyses.At the end of July 2023, guidance related to the use of artificial intelligence, language models and chatbots was published by JAMA Network. The authors indicated that non-human artificial intelligence tools should not be credited as co-authors. The use of such tools should be transparently reported when writing manuscripts and other content . A similmaterial . Anothermaterial . An intematerial . The proaudulent . Other deviewers . In addiAllergy. ChatGPT provided answers to questions about what kind of statistical test it would apply to specific studies conducted by these authors, the results of which were subsequently published in Allergy. The articles were selected based on the various statistical tests applied. In order to show the variety of answers given, one question related to the conduct of a statistical analysis was selected and then asked several times of ChatGPT. Based on the answers given by ChatGPT to the 40 questions, the statistical recommendations mentioned in the introduction were created. The answers given were scored using a 0–1 system, where 0 was the wrong answer and 1 was the correct answer. Incorrect answers were related to the wrong choice of statistical tests, the tests’ assumptions, and other aspects related to conducting a statistical analysis, such as the correct description of the obtained test results, removal of outliers, etc. In an additional supplement file . A 2(1) = 1.2; p = 0.27. However, the responses associated with the need to use non-parametric equivalents of statistical tests were noteworthy. This refers to ChatGPT’s lack of consideration of the scale of the analysed variables, for example, related to ordinal nature, low sample size and other assumptions necessary for the application of specific tests. The remaining wrong answers given were to do with the wrong choice of statistical test, the wrong measure of effect size and the wrong descriptive statistics to be included in the description of the results obtained . Of the answers given, 12 were incorrect. The difference in the number of correct and incorrect answers given was not statistically significant, χobtained . For thiIn terms of the correctness of ChatGPT’s five different statistical analyses, as was observed for the 30 questions in Allergy. Once again, the incorrect responses were related to the fact that ChatGPT did not consider aspects such as sample size and the equality of the groups of people being compared, and therefore did not select the appropriate effect size measure (ChatGPT gave correct answers to the questions related to three of the five selected articles published in  measure  [9,10,11When asking ChatGPT questions related to different aspects of the statistical analyses being conducted, it was noted that there were discrepancies in the answers given to the same questions. One question was selected from the 30 questions presented in the first table and then presented to ChatGPT three times to show the discrepancies in the answers given. Of the 30 questions presented in Allergy, a study was carried out to analyse the accuracy of ChatGPT’s answers to questions related to statistical analysis in a broad sense. Unfortunately, the results obtained indicate that out of 40 possible questions, only half were correctly answered, which had to do with the lack of specification of questions in which assumptions such as sample size, measurement scale of analysed variables, etc., had to be taken into account. A review article published in Health and Technology pointed out that the use of artificial intelligence tools can help to facilitate comprehensive statistical analysis, including troubleshooting missing data [Schizophrenia with the following title: “ChatGPT: these are not hallucinations—they’re fabrications and falsifications”. Emsley R. pointed out that questions on general methodology produced general and down-to-earth answers. Difficulties were encountered in obtaining slightly more detailed answers related to a statistical analysis plan [PLOS Biology, it is now generally recommended to provide practical instruction in performing statistical analyses, e.g., using examples of published articles in which specific statistical tests have been applied [Nature pointed out researchers’ concerns about students writing homework with the help of artificial intelligence [The present research is, to the best of my knowledge, the first on ChatGPT’s statistical skills. Using the medical field of allergology as an example, including articles published in ing data . ChatGPTing data . A studying data . An intesis plan . This cosis plan . One recsis plan . An incrsis plan ,21,22. Tsis plan ,23. The sis plan ,25. Unfo applied . One pub applied . An artilligence . The nonlligence . ChatGPTlligence . Asking lligence . A commalligence . The prolligence . This maBased on the research conducted, statistical recommendations related to the use of ChatGPT are indicated below. An initial recommendation for authors wishing to use ChatGPT’s indicated answers to questions related to statistical analysis in the broadest sense concerns the initial analysis of the assumptions necessary to apply specific tests. Before the authors decide, for example, to apply a statistical test indicated by ChatGPT, they should first examine the individual assumptions. Assumptions such as normality of distribution, group size, equality of groups, the scale on which the variable being analysed is measured, etc., must be taken into account. ChatGPT should not be asked general questions related to what kind of analysis should be applied, and the highlighted assumptions should be considered. In other words, it is advisable to ask more precise questions about whether 16 samples are enough to apply an analysis, or, for example, whether the measurement scale on which a variable is measured allows the application of a particular test. Without a specific question, the percentage of statistical advice provided by ChatGPT that is appropriate may be low. The second recommendation is to ask the same ChatGPT question several times because, as indicated in this manuscript, there is the possibility that it will provide quite divergent answers and thus subsequently suggest the wrong advice. Once different answers to a single statistical question have been obtained, authors should analyse these in depth, choose the most optimal answer to the best of their knowledge and then, if there are further doubts, relate the chosen answer to the relevant references. One option is also to refer to published statistical recommendations in medical journals such as those published by this journal, among others . This isAt a time when there is a lower level of statistical analysis being conducted, we should have limited confidence in the advice provided by ChatGPT related to conducting statistical analyses.