Numerous studies have shown the value of patient home video recordings within the field of epilepsy. Despite the growing influence of mobile technology and telemedicine, there is a paucity of studies examining the role of home videos in the diagnosis of movement disorders.To characterize the clinical value of patient home videos in a movement disorders practice.We performed a retrospective review from our video database over the past 10 years and identified 20 encounters where an in-person, clinic evaluation and studio video were supplemented by a home video. We reviewed these encounters to determine if the home video added additional value to the clinic video. The home videos were screened by 3 movement disorders attendings and 3 movement disorders fellows to assess for quality and to determine whether or not the patient phenomenology could accurately be identified.Of the 20 videos identified, 10 (50%) were determined to be of additional clinical value. In 62.4% of evaluations movement disorders attendings and fellows were able to identify phenomenology from the home videos consistent with the final diagnosis. Videos rated as “poor” quality had significantly lower odds of leading to a correct phenomenology  than those rated as “excellent” quality.Patients should be encouraged to produce good quality home videos, particularly in paroxysmal or fluctuating movement disorders, as they may add value to the eventual diagnosis and management. Video recordings have a well-established utility in the medical field as a teaching, diagnostic, and research tool. In a survey of 70 children’s hospitals across Canada, the United Kingdom, and the United states, 94% of hospitals reported using video recording of patients in some capacity . Within With the advances in mobile technology there has been an increased implementation of wearables, telemedicine, and other device or computer-based methods in movement disorders practices and research 67. In ou6This study consisted of a retrospective review of the video database maintained at the Parkinson’s Disease Center and Movement Disorders Center (PDCMDC) at the Baylor College of Medicine from January 2010 to August 2020. The database includes videos taken of movement disorders patients in our clinic recording studio by clinical professionals  and videos recorded by patients (“selfies”), their friends, family members or care givers in a home, hospital, vehicle, or some other environmental settings that were then provided to the PDCMDC. The term “home” video is used colloquially and applies to all or any of these situations. Videos were catalogued in a “video log” that documented the patient’s age, brief clinical history, phenomenology of movements seen on the video, the patient’s diagnosis, and any ongoing plan of care at the time the video was reviewed by clinicians. Phenomenology, diagnosis, and plan of care were determined by consensus of movement disorders clinicians at regularly scheduled video conferences where patient videos, patient history of present illness, and findings based on physical examination were discussed. We screened the video log for patients who met the following criteria: presented for a clinic visit in which they underwent a formal clinical evaluation comprising of a history of present illness and neurological examination, had a formal video recording in our clinic studio according to our standardized protocol, had a home video of their movement disorder, and consented to allow for both clinic and home videos to be used for research purposes.In patients who met the criteria for inclusion, a retrospective review of the electronic medical record and video log was performed. Information was gathered on patient age at the time the video was obtained, the patient’s diagnosis, phenomenology of movement disorders noted on clinical examination by the patient’s attending, the phenomenology noted on the video obtained in the clinic determined by PDCMDC attending consensus at video conferences, and the phenomenology of patient home videos determined by PDCMDC attending consensus at video conferences.Following the retrospective review, home videos were classified as supportive of the diagnosis or not supportive of the initial diagnosis. Home videos were determined to be supportive of a patient’s initial diagnosis if they demonstrated a phenomenology consistent with their diagnosis and/or a response to treatment that would be expected based on their clinical diagnosis. Home videos that did not demonstrate a phenomenological finding, showed a phenomenological finding and/or treatment response inconsistent with the proposed diagnosis, or were of too poor quality to interpret based on the video log consensus were determined to be of no additional value. Home videos were also classified as having provided additional clinical value or being of no additional clinical value. Home videos that provided additional clinical value demonstrated a phenomenology and/or a treatment response that was not or could not be appreciated during their clinical encounter. Home videos of no additional clinical value demonstrated phenomenology or a response to treatment that was also appreciated during the patient’s clinical encounter or met the criteria for videos not supportive of the initial diagnosis as stated above.Patient home videos were then retrieved from the PDCMDC video database to be reviewed by “blinded” raters. Patient identifying information was removed via Pinnacle video editing software. In cases where patients presented with two or more home videos on a visit, all the home videos were compiled into one video file for review and rating. Blinded raters consisted of 3 movement disorder faculty/attendings and 3 movement disorder fellows at the PDCMDC. Attendings and fellows were asked to identify any attributes that may have interfered with video interpretation. These included any issues with framing of the subject or symptom of interest, sound interference from ambient noise, persons or objects obstructing the camera, poor image quality (i.e. low resolution), inadequate length of video recording, issues with lighting , video stability (i.e. camera shake), poor sound quality , and poor camera focus (i.e. blurriness). Based on these attributed they were asked to assign an overall grade to video quality of “poor”, “fair”, “good”, or “excellent”.Attendings and fellows were then asked to list any applicable phenomenology observed in the video and list the top three most likely clinical diagnoses based on the videos alone. The raters were also asked to assign a confidence level to their interpretation on a scale of 0–100%. No fellow or attending rated a video they had previously viewed during weekly video rounds. Due to this requirement, the number of videos evaluated by each rater in the attending group varied.Rater responses were compared with the phenomenology and diagnosis as listed in the patient’s chart, and the video log to assess whether raters could accurately assess phenomenology and propose a diagnosis on video alone. As some videos demonstrated multiple phenomenologies, a correct phenomenological diagnosis was defined as one or more phenomenology survey responses matching one or more phenomenologies listed in the corresponding video log entry for the applicable home video even if other phenomenological responses from the same survey were not listed in the video log. An incorrect phenomenological diagnosis was defined as not having identified any phenomenology listed in the video log.Descriptive statistics were used for evaluation of demographic information and evaluation of utility of patient home videos. Binary logistic regression was used to determine if obtaining a correct phenomenological diagnoses or not could be predicted based on video quality. Chi-square and Fisher’s exact test (when appropriate) were used to assess for associations between video attributes and phenomenology accuracy. Fleiss’ kappa was calculated to measure interrater reliability among the physicians who interpreted all videos. Statistical analysis was performed in SAS studio.Table 1.A review of the video log identified 20 patient encounters between January 2010 to August 2020 where a home video accompanied a video evaluation performed in the clinic recording studio. The demographic information, identified phenomenologies, and patient diagnoses are listed in Video 1), and characterization of a severe tic in a Tourette patient that did not occur during the patient’s clinical encounter (Video 2).Based on review of the medical records and the video log, 19 (95%) of the home videos were found to be supportive of the initial diagnosis the patients received during their clinic encounter. The one home video not supportive of the initial diagnosis could not be adequately interpreted due to video poor video quality. We also determined that 10 (50%) videos added an additional value beyond the clinic encounter regarding the patient’s diagnosis or treatment. Of these, 9 (45%) videos showed a phenomenological finding that was not noted during clinic examination, helping to further support the diagnosis. One video (5%) helped influence management decisions by demonstrating that a patient’s dystonia was not responsive to levodopa. Notable examples of these include a home video demonstrating paroxysmal gait changes in a functional patient  and fellows . Raters were able to accurately identify at least one phenomenological finding based solely on patient home video 62.4% of the time. The average reported confidence by raters in phenomenology was 70.4% (standard deviation (SD) 30.3). Based on home video alone, raters were able to accurately identify a correct clinical diagnosis as their top differential diagnosis 34.4% of the time. The correct clinical diagnosis was listed in the top three differential diagnoses 46.2% of the time. Diagnostic confidence among raters was 62.9% (SD 33.1).2 = 0.07, p = 0.80). Only one attending physician was able to able evaluate all 20 videos, the remaining two attending physicians were limited to 3 evaluations and 10 evaluations as they had previously viewed the other home videos as part of their clinical duties. There was fair inter-rater reliability between raters  who evaluated all 20 videos .There was no difference in frequency of correct phenomenological diagnosis between attending physicians (60.6%) and fellows (63.3%) (xTable 2). There was no other significant difference noted between overall video quality and accuracy of phenomenology.Movement disorders attendings and fellows were asked to identify any attributes and assign videos an overall grade to video quality of “poor”, “fair”, “good”, or “excellent”. The odds of identifying the correct phenomenology were significantly lower in the “poor” quality group compared to “excellent” quality videos, with an odds ratio (OR) of 0.07 : [0.01–0.72]) (Table 3). Image quality was the only attribute significantly associated with a difference in accuracy in phenomenology (35%), . Other notable differences in accuracy were noted in videos with noise interference and poor sound quality, however these differences did not reach statistical significance.The video attributes that were identified to interfere with interpretation were assessed independently to determine whether they influenced the accuracy of identifying the correct phenomenology on home video .Our study is the first to quantify the value of home videos in determining the phenomenology and diagnosis. Though our sample size was limited, we were able to demonstrate that the majority of home videos taken by patients are of diagnostic value. Indeed, in 50% of cases the home videos provided additional clinical information that would have otherwise not been appreciated during a clinical encounter and in 45% the videos showed a phenomenological finding that was not noted during clinic examination. While our physicians were able to accurately identify a correct phenomenology 62.4% of the time, they were able to determine a correct diagnosis 34.4% of the time based on video alone. We also demonstrated that there is a relationship between the quality of the video and the odds that the physician would be able to interpret it correctly. As such, even though home videos are usually done with cell phones, the recordings can still be highly valuable, especially if patients and their family members or friends are provided instructions on how to best optimize video quality  and there was no difference in frequency of correct phenomenological diagnosis between attendings and fellows. While retrospective in nature, and without a standardized protocol for filming we still found that our results are comparable to other studies that evaluated the inter-rater reliability of movement disorders experts in recognizing phenomenology by videos obtained by clinicians. In a study by Van der Salm et al., videos of 60 patients with either myoclonus, tics, or psychogenic jerks were evaluated by 39 experienced movement disorder specialists. The inter-rater agreement among experts based on video interpretation alone was fair for myoclonus (κ = 0.29 ± 0.13), psychogenic jerks (κ = 0.22 ± 0.16), and tics (κ = 0.32 ± 0.18) . Anotherth case. The overall inter-rater reliability including the control case was very good . Another study asked 7 movement disorder neurologists to evaluate 29 videos of movement disorders on YouTube to judge whether not the video represented a functional or organic disorder [Where home videos have been found to be particularly useful, with high inter-rater agreement, is in the recognition of functional movement disorders. In a study that evaluated whether or not functional neurological disorders could be identified by videos in the news media, 10 movement disorder specialists were asked to identify 7 cases that were presumed to be functional movement disorders and 1 control case with an organic diagnosis . All 10 disorder . They iddisorder . A recendisorder . Due to We recognize several limitations in our study. Due to the need to preserve the blind, raters were excluded from rating if they previously reviewed the videos during weekly video rounds. Therefore, not every video was evaluated an equal number of times by raters. We also recognize that the patient disease demographics are not representative of a typical population in a movement disorders practice and, therefore, the findings may not be generalizable. This may be due to selection bias for rarer conditions or a higher frequency of atypical or functional movement disorder requiring second opinion based on video review. Conversely, these demographics may better represent those complex patients in whom supplemental home video recording can be more helpful. Other limitations include small sample size and the retrospective nature of the study.Home video recordings allow for a unique and additional perspective and remain a vital tool in evaluation of patients with organic and functional movement disorders. Several barriers to more routine usage may be technological constraints on the patient or the providers office and poor integration of video sharing and the electronic medical record. As it is well recognized that telemedicine and remote medicine will continue to become more common place, we think it is important to encourage high quality home videos as a supplemental tool in diagnosis and management of patients with movement disorders.


Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region  in a video camera feed. In the first step, the riders are identified by an Alphapose skeleton detector and tracked with a spatiotemporally aware pose tracker. Next, each detected pose is enriched with additional meta-information, such as rider modus  and detected team (based on the worn jerseys). Finally, a post-processor brings all the information together and proposes ride lines with meta-information for the riders in the fence. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment. Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization. Over the past years, an exponential growth of cycling sensor-generated data has been observed. In contrast, the impact of these new technologies on race reporting, coaching and training, injury prevention, and safety is still limited. The main reason is that existing applications just add an additional layer of raw sensor data on top of the live broadcast . ThisIn general, data about sports events are becoming more and more available. In last year’s Tour de France, for instance, NTT showcased the potential of big data in cycling by recording more than 35 million records per stage ,5. HowevThe remainder of this paper is organized as follows: in the related work section, we discuss relevant work on sector timing and video-based ride performance analysis; under methodology, we further elaborate on the video processing pipeline and the tracked riding lines; next, the results section discusses the implementation and presents some practical results of the proposed building blocks; in the final section, we briefly summarize our main findings and contributions and point out some future work that is required to further finetune the processing pipeline.To answer the first part  of the sector analysis question, several approaches have been suggested in the literature. Sector timing is often performed by some kind of wireless communication protocol. Athletes or objects wearing an active or passive tag pass through a checkpoint, which acts as a receiver or senses the tags at the checkpoint. Ultra-high-frequency RFID is one of the more popular technologies uses in sports, such as mountain biking, running, skiing, and road cycling or cyclocross. Kolaja and Ehlerova  performeIn the previous paragraph, some available sensor-based solutions for tracking and timing were discussed, but computer vision techniques could also be used to perform performance analysis. Popular sports, such as soccer, tennis, basketball, or cycling, are usually broadcasted on national television. Although video data cannot be directly used for performance analysis, several computer vision techniques can be utilized to extract performance data for further analysis. In basketball, for instance, Arbués-Sangüesa et al.  proposedThe fencing principle has already been extensively documented in non-sports-related literature. Chen et al.  defined In our ride line analysis, we will adopt a video-based approach to extract the ride lines of cyclocross riders, but as further explained in the discussion section of this paper, our approach might also benefit from additional sensor-based rider tracking. The pipeline uses a set of individual state-of-the art building blocks, which can, when brought together, perform the specific task at hand . On top of these building blocks, some extensions and case-specific modifications were made.The proposed methodology, shown in The first step of the analysis consists of the decomposition and initial preprocessing of the incoming video source. The pipeline accepts both recorded video clips and live video data delivered by popular streaming formats, such as HTTP Live Streaming (HLS) and the Newtek Network Device Interface (NDI) protocols. The video data is decomposed into frames and based on the framerate of the recorded video, some frames are periodically skipped for an optimal balance between accuracy and processing time. Experiments with high-definition footage (1920 × 1080 pixels) at a framerate of 30 frames per second show that processing every third frame gives the best balance between pipeline detection accuracy and processing speed . By processing every third frame, the video is processed in (near) real time. For preprocessing, the frame can be cropped to leave out irrelevant background information for further analysis (and further speed up processing times). Next, a rectangular fence is defined within the cropped region. The fence is defined as the region of the video in which movements and the behavior of riders are analyzed and is illustrated by the measurement zone rectangle in In the following two steps, a combination of computer vision and machine learning algorithms will gather more information about the riders that are present in the fence. As a start, an Alphapose pose estimator  is run oIn cyclocross, riders ride or run with their bikes, based on the technicality and surface conditions. In the next step of the video processing pipeline, a clear distinction between each of the ridemodi a rider can adopt is made. Technical sectors might, for instance, be perfectly rideable for a rider with great technical prowess but might be completely unrideable for another less technical rider. The barriers are a great example of such a technical sector. If the barriers are relatively high and are placed at a challenging part of the course , some less technical riders will be forced to dismount their bike and run over this course feature. Monitoring these differences among riders within the video fence can be very valuable to help understand why, how, and where riders are taking a certain line and to explain why one rider is slower than another. To answer the riding mode question, a YOLOv5 neural network was trained to detect cyclists that are either running or cycling and spectators. This knowledge is attached as metadata to the previously detected poses and its tracked pose identifiers.Another interesting metadata generator within the pipeline is the team (jersey) recognition module. The team jersey recognition should be capable of detecting which team a rider belongs to based on the team jerseys they are wearing. Team jerseys usually have distinct patterns with some sponsors on them. If team jerseys do not change over the years, it might be perfectly feasible to train a state-of-the-art object recognition model that has a couple of hundreds of images for each team. However, in practice, team jersey designs, sponsors, and even colors change usually every year (or sometimes even faster), which makes this approach rather unfeasible. To overcome this limitation, a methodology that only uses relatively few examples for each jersey class should be implemented. For this purpose, a transfer learning approach was used . In neurThe next link in the video processing pipeline is the check of whether a rider is in the fence. This is determined by the center of the bounding box (bbox) around the joints that are detected by the Alphapose detector. If the center of the rider’s bbox is within the fence’s box, the rider is considered in the fence. After the previously mentioned pipeline elements have run, the post-processing step can now be performed. The difference with the previous steps and the post processing step is that the previous steps take place on a per frame level, but the post processing happens across multiple frames. In the real-time path analysis scenario, the post processing is initiated if the fence remains empty for 10 consecutive analyzed frames. In post-processing, the actual paths travelled by the tracked skeletons are determined. If desired, the coordinates of the paths can also be transformed into real life coordinates using a homography perspective transformation . The maiIn summary, the merging process consists of three steps. In the first step, the paths are split based on jumps in frame numbers of the different tracks. A track is defined as a pose that was tracked over time in the fence and was assigned a tracking identifier. Tracks with non-subsequent frame sequences are split into separate tracks. This prepares the tracks for step two of the merging process, where the tracks are attempted to be merged again based on a spatiotemporal weighting function. As illustrated in The proposed pose tracking methodology combined with the merging strategy produces a set of pose tracks that can be directly used in the following steps in the video pipeline. In this next step, the pose tracks (or parts of the tracks) that are within the boundaries of the desired fence’s coordinates are extracted. To check if a skeleton of a path is within the fence, the center of the bounding box around its joints is used. Using this approach, the tracked skeletons’ coordinates stay much more consistent and less spikey as when a joint, such as the knee or foot, were used. The skeleton is considered within the fence if this center coordinate is within the fence’s bounding box coordinates. This check is performed for every frame in which the skeleton in the track was detected. A valid path in the fence is defined as a rider that is entering and exiting the defined bounding box (and has multiple detections within the fence). Once the valid paths have been detected, all information is available to create insightful statistics of the path a rider did follow within the fence. The extra metadata, such as the rider modi and the team jersey recognition results, are used to annotate the path with a major riding mode and the team probability scores of the rider. The combination of the video (stream) frame rate and the start frame and end frame when the rider entered or exited the fence also allows an estimate of how much time the rider did spend in that zone. In the final step, the data of the tracked skeleton across different frames within the fence is brought together and summarized, which in its turn can be published. In our video pipeline, the fence data is published to a REST API, allowing easy retrieval for other stakeholders within the cyclocross broadcasting world. As a final note, riders can be identified with a MyLaps detection loop at the start and end of the fenced zone (as will be further explained in the discussion section).In the methodology, the mechanism of the fencing framework was theoretically discussed. In this section, we will further elaborate on the implementation of the introduced building blocks and some preliminary results will be showcased.An important metadata generator within our pipeline is the ride mode detector. To train such a model, a training dataset of 869 images was constructed, with 747 cyclists that are riding, 457 running, 116 crashing, and 1038 spectators. The dataset was split uniformly across the categories in 75% for the training data, 20% for test, and 5% for validation. The data was used to train a YoloV5 model (yolov5s variant) for 100 epochs and achieved a mean average precision (mAP) of 68%. As can be seen in The next model that was trained to serve as the metadata generator in our ride line pipeline was the team detector model. To prove the effectiveness and its relative easiness to train with few sample images, a model was trained on five teams . Another possibility we have been investigating is the publication of the ride line graphs presented in The proposed pipeline in this paper is just the starting point for further analyses and visualizations of riders’ lines and behavior on a small area on the racecourse. As the pipeline is fully modular, (metadata) pipeline elements can be added or removed as required. As an example, in sports, such as cyclocross and motocross, a MyLaps gate timing solution is often used. When the riders wear a transponder and ride over a measurement loop, their time of crossing is recorded on the MyLaps system. When such a setup is installed at the start- and endpoints of the video fences, some extra meta-information could be introduced in the video pipeline. With some post-processing and accurate time synchronization of the MyLaps and video data, rider identification and fence timing could be directly derived from the MyLaps measurements and linked to the detected skeletons in the video fence.An extra extension of the metadata collection could be the inclusion of wearable sensor data. ANT+ is a protocol that facilitates the broadcasting and processing of sensor data, such as power, heart rate, speed, or cadence. It can be easily consumed by a USB ANT+ dongle and with one of the available Software Development Kits. The combination and time synchronization of sensor data with ride lines of the riders can provide more profound insights, such as the average power during the ridden line, maximum power, or average speed. Sensors have a distinct identifier so the pairing of a sensor with an identified rider on the video stream can be achieved.Lastly, it is also worth mentioning that the use cases of the core detector  are not limited to cyclocross and other cycling disciplines only. A similar methodology for all other sports where riders follow a course or path can be compiled with the simple addition of sports-specific metadata generation models. As a side project, the video processing pipeline was already reused to analyze a filmed ski downhill run. The ride mode detector was changed by a ski pole and flag detector. The fencing module was also removed from the pipeline but was replaced by a pipeline post-processing element that looked at when the skier’s bounding box overlapped with the detected ski flags bounding boxes. With this information, the path the skiers took could be recreated as well as the timings of the segments between consecutive flags that were slalomed.In this paper, we presented a video-based end-to-end modular and stepwise solution for detection and analysis of cyclocross riding lines. As a first step, human poses are extracted from a video frame. These poses are further identified by a ride modus and team detector. The poses from an individual frame are merged and post-processed by the pose tracker. In a final post processing stage, the ridelines are extracted from a static camera for a certain region of interest . The pipeline outputs both the raw metadata output  and the processed rideline data. This approach was tested on a static camera positioned on the sandpit sector of a professional cyclocross race. The pipeline was able to detect and highlight irregular riding behavior. The produced insights can be used for broadcasting storytelling purposes or can be directly pushed as infographics to social networks .


Participants completed a 2-week daily diary study during which they filled out daily reports of their past day drinking, mood, and craving. Participants completed an functional magnetic resonance imaging (fMRI) alcohol cue-reactivity paradigm half-way through the study. Ibudilast did not have a significant effect on negative mood . However, ibudilast, relative to placebo, reduced the odds of heavy drinking across time by 45% ). Ibudilast also attenuated alcohol cue-elicited activation in the ventral striatum (VS) compared to placebo  = 7.36, p = 0.01). Alcohol cue-elicited activation in the VS predicted subsequent drinking in the ibudilast group  = 6.39, p = 0.02), such that individuals who had attenuated ventral striatal activation and took ibudilast had the fewest number of drinks per drinking day in the week following the scan. These findings extend preclinical and human laboratory studies of the utility of ibudilast to treat AUD and suggest a biobehavioral mechanism through which ibudilast acts, namely, by reducing the rewarding response to alcohol cues in the brain leading to a reduction in heavy drinking.Ibudilast, a neuroimmune modulator which selectively inhibits phosphodiesterases (PDE)-3, -4, -10, and -11, and macrophage migration inhibitory factor (MIF), shows promise as a novel pharmacotherapy for alcohol use disorder (AUD). However, the mechanisms of action underlying ibudilast’s effects on the human brain remain largely unknown. Thus, the current study examined the efficacy of ibudilast to improve negative mood, reduce heavy drinking, and attenuate neural reward signals in individuals with AUD. Fifty-two nontreatment-seeking individuals with AUD were randomized to receive ibudilast ( Alcohol use disorder (AUD) is a chronic relapsing disorder with a major public health impact. Over 14 million adults in the United States have an AUD  with numA growing body of literature indicates that the neuroimmune system may play a critical role in the development and maintenance of AUD, termed the neuroimmune hypothesis of alcohol addiction . In animIbudilast  shows promise as a novel AUD pharmacotherapy. IBUD reduced alcohol intake by 50% in two rat models, and selectively decreased drinking in alcohol-dependent mice relative to nondependent mice . In a huWhile IBUD is a promising AUD pharmacotherapy, its underlying mechanisms of action on the human brain remain largely unknown . PDE4 isThe current study was an experimental medication trial of IBUD compared to placebo in non-treatment-seeking individuals with an AUD. To advance the development of IBUD as an AUD treatment, the present study examined the efficacy of IBUD, relative to placebo, to reduce negative mood and reduce heavy drinking  as ≥5 drinks/day for men and ≥4 drinks/day for women) over the course of 2-weeks. A micro-longitudinal design allowed for daily assessments during the course of treatment. We hypothesized that ibudilast would reduce negative mood and decrease heavy drinking over the course of the study. To investigate the neural substrates underlying IBUD’s action, the present study also examined the effect of IBUD on neural alcohol cue-reactivity. We hypothesized that ibudilast would attenuate alcohol cue-elicited activation in the VS relative to placebo. Finally, this study explored the relationship between neural alcohol cue-reactivity in the VS and drinking outcomes.This was a 2-week clinical study  of ibudilast for negative mood improvement and drinking reduction in non-treatment-seeking individuals with an AUD. Eligible participant were randomized to ibudilast or matched placebo. Participants completed three in-person visits and daily online diary assessments to report on their drinking, craving, and mood from the previous day. This trial was approved by the Institutional review board of the University of California, Los Angeles. All study participants provided written informed consent after discussing the study medication with the study physician. Participants were enrolled and randomized in the study between July 2018 through March 2020. Data analysis were conducted from March to July 2020.This study was conducted at an outpatient research clinic in a medical center. Participants were recruited through social media and mass transit advertisements. Initial screening was conducted through telephone interview, with eligible participants invited for an in-person assessment. Eligible individuals were between 21 and 50 years old who met criteria for a current DSM-5 mild-to-severe AUD. Participants were required to drink above moderate drinking levels, as defined by the NIAAA as >14 drinks/week for men and >7 drinks/week for women, in the 30 days prior to screening. Exclusion criteria were: currently receiving or seeking treatment for AUD; past year DSM-5 diagnosis of substance use disorder ; lifetime diagnosis of schizophrenia, bipolar disorder, or any psychotic disorder; nonremovable ferromagnetic objects in body; claustrophobia; and serious head injury or prolonged period of unconsciousness (>30 min). Participants were excluded if they had a medical condition thought to interfere with safe participation and if they reported recent use of medications contraindicated with ibudilast. Women of a childbearing age had to be practicing effective contraception and could not be pregnant or nursing. See Fig. A stratified randomization list was developed by a statistician and was based on sex and withdrawal-related dysphoria, a measure of AUD severity. Participants were randomized to 50 mg b.i.d. of ibudilast or placebo. MediciNova, Inc. supplied the ibudilast and placebo for the study and the UCLA Research pharmacy prepared all study medication in blister packs, which were dispensed on the randomization study visit. Participants, providers, and research staff remained blind to medication assignment throughout the study. Ibudilast was titrated as follows: 20 mg b.i.d. during days 1–2 and 50 mg b.i.d. during days 3–14. Medication compliance was monitored through pill counts at the midpoint and final study visits, as well as through self-report in the daily diary assessments. Participants were compensated up to $350 for their participation in the study .Participants completed a series of assessments for eligibility and individual differences. These measures included the Structured Clinical Interview for DSM-5 (SCID-5) , the CliParticipants completed three in-person study visits occurring on Day 1 (randomization), Day 8 , and Day 15 . Randomization visits occurred on Mondays and Tuesdays to ensure that participants were at the target medication dose by the weekend. Side effects were elicited in open ended fashion and were reviewed by the study physicians (K.M. and A.G.). Adverse events were coded using the MedDRA v22.0 coding dictionary. Treatment-emergent adverse events were defined as adverse events that started after the first dose of the study drug or worsened in intensity after the first dose of study drug. Participants completed daily diary assessments, reporting on their past-day alcohol use, mood, assessed with a shortened form of the Profile of Mood States  (POMS), Participants were scanned at the midpoint study visit (Day 8). Neuroimaging took place at the UCLA Center for Cognitive Neuroscience on a 3.0 T Siemens Prisma Scanner . Detailed neuroimaging procedures can be found in the Supplement. Participants completed a 720-s-long alcohol cue-reactivity task , in whichttp://www.fmrib.ox.ac.uk/fsl). This included motion correction, high-pass temporal filtering (100-s cut-off), and smoothing with a 5-mm full-width, half-maximum Gaussian kernel [Preprocessing followed conventional procedures as implemented in FMRIB Software  with compound symmetric (exchangeable) covariance structure were run in SAS 9.4 to account for repeated measures ,44,. GEEA GEE model was first run to assess the effect of medication on negative mood. The dependent variable, negative mood (assessed via items from the POMS), was treated as continuous so a normal distribution with identity link function was chosen. A compound symmetric covariance structure was chosen to account for the repeated assessments. Independent variables for these analyses were medication (IBUD vs. PLAC), drinking day (yes vs. no), and the interaction of medication by drinking day. Sex, age, depressive symptomology (log BDI-II score ), and smThe dependent variables for the drinking analyses (heavy drinking and any drinking) were binary, such that 1 indicated a heavy drinking day (HDD) or drinking day and a 0 indicated no heavy drinking or drinking, respectively. A binomial distribution with logit link function was chosen to model the binary dependent variable . Since participants were not on medication at baseline (Day 0), this timepoint was excluded from the analysis. Independent variables included in the models were medication (IBUD vs. PLAC), time (measured in integer days), and the interaction of medication by time. Baseline drinking information  were also included in the model as a control. As above, sex, age, depressive symptomology (log BDI-II score), and smoking status (smoker vs. nonsmoker) were examined as covariates; only significant covariates were retained in the final model to improve model clarity and ease of replication. For both analyses, predicted probabilities, standard errors, and 95% confidence intervals for heavy drinking and any drinking were calculated based on final models.A general linear model was used to evaluate the effect of medication on VS activation. The dependent variable was VS percent signal change between ALC and BEV blocks. Medication (IBUD vs. PLAC) was the independent variable. Age, sex, depressive symptomology (log BDI-II), and smoking status (smoker vs. nonsmoker) were examined as covariates; only significant covariates were retained in the final model. Finally, to evaluate if VS activation interacted with medication in predicting drinking in the week following the scan, a between-subject factor for VS activation . Participant demographics, drinking, and mood characteristics are presented in Table p’s > 0.40; see Supplement Table p > 0.39, see Supplement Table Screening and randomization of participants are summarized in Fig. There were no significant main effects of medication or medication by drinking interactions on daily mood , and medication by drinking day interaction. There was a trend towards an interaction between medication and drinking on craving , such that men had higher activation than women; however the interaction between medication and sex was not significant (p = 0.26). There were no significant effects of age, smoking status, or depressive symptomatology on VS activation.The alcohol cue-reactivity paradigm elicited the expected mesocorticolimbic brain activation across participants , such that individuals who had decreased activation to ALC vs. BEV and were treated with IBUD had the fewest number of drinks per drinking day in the week following the scan   = 6.85, can Fig. . There wThis was the first study to evaluate the effects of ibudilast, a neuroimmune modulator, on mood and drinking outcomes in a clinical sample with AUD. Contrary to our hypothesis, ibudilast did not have a significant effect on negative mood on drinking or non-drinking days. However, in support of our hypotheses, ibudilast significantly reduced the probability of heavy drinking compared to placebo. Ibudilast also significantly attenuated alcohol cue-elicited activation in the bilateral VS. Furthermore, exploratory analyses indicated that ventral striatal activation to alcohol cues was predictive of drinking in the week following the neuroimaging scan. These results suggest a biobehavioral mechanism through which ibudilast acts, namely, by reducing the rewarding response to alcohol cues in the brain leading to a reduction in heavy drinking per se.Unexpectedly, this study did not find support for an effect of ibudilast on negative mood or a moderating effect of baseline depressive symptomology on medication response. This contrasts with previous findings from our lab in which ibudilast improved mood response to stress and alcohol cues . The curRegarding the drinking outcomes in this study, IBUD significantly reduced the probability of heavy drinking compared to placebo. Specifically, individuals treated with IBUD were 45.3% less likely (OR = 0.547) to drink heavily compared to individuals treated with placebo. This resulted in a 24% predicted probability of heavy drinking over the course of the study in the ibudilast group, compared with a 37% predicted probability in the placebo group. Of note, there were no significant differences in AE’s between groups, indicating that this reduction was not due to increased side effects, including nausea, in the IBUD group. There was not a significant effect of IBUD on the probability of overall drinking compared to placebo. While nonsignificant, the effect of IBUD for any drinking days was in the expected direction, such that individuals on IBUD were 16.9% less likely (OR = 0.831) to engage in any drinking relative to placebo, but high variability in the prediction prevented conclusive statistical findings. This nonsignificant effect may not be surprising, as the study sample was comprised of non-treatment-seekers and therefore not motivated to abstain from drinking altogether. Rather, participants treated with IBUD reduced their heavy drinking, which produces a harm reduction benefit, particularly for those with a mild-to-moderate AUD ,. This fThis study also examined a potential biobehavioral mechanism underlying IBUD’s action using an fMRI alcohol cue-reactivity paradigm. IBUD attenuated alcohol cue-elicited reward activation in the VS compared to placebo. PDE4 and PDE10 are highly expressed in the striatum and negatively regulate dopaminergic signaling . Thus, iThis study has several strengths and limitations which should be considered when interpreting the results. Study strengths include the use of daily diary reporting, which captures real-world drinking and minimizes recall bias, and the combination of neurobiological (fMRI) with behavioral and self-report methodologies. However, this study recruited a non-treatment-seeking sample; therefore, these findings may not generalize to a treatment-seeking sample with AUD (see ). An ongIn conclusion, this is the first combined clinical and neuroimaging study of ibudilast (50 mg b.i.d.), a neuroimmune modulator, to treat AUD. Ibudilast did not improve negative mood on drinking or non-drinking days, indicating that its mechanism of action may be non-mood dependent in non-treatment-seeking individuals. Ibudilast reduced the probability of HDDs over 2 weeks for non-treatment-seeking individuals relative to placebo. Ibudilast also attenuated alcohol cue-elicited activation in the VS, potentially through a dopaminergic-related mechanism. This is a critical proof-of-mechanism whereby modulation of neuroimmune signaling via ibudilast reduced the incentive salience of alcohol cues in the brain. Exploratory analyses indicated that ventral striatal activation to alcohol cues was predictive of subsequent drinking in the ibudilast group, such that individuals who had attenuated ventral striatal activation and were treated with ibudilast had the fewest number of drinks per drinking day in the week following the scan. Overall, these findings extend preclinical  and humaSupplementary Material for Ibudilast, A Neuroimmune Modulator, Reduces Heavy Drinking and Alcohol Cue-Elicited Neural Activation: A Randomized Trial


This study aimed to explore the drinking culture in Korea by sex, age, household type, occupation, and income level to identify demographic groups with prominent drinking behaviors and factors affecting their drinking. Furthermore, we evaluated recent changes, including those due to COVID-19, in drinking behavior, using data from the Korea Welfare Panel Study from 2010 to 2020. Panel analysis was performed to reveal the effects of material deprivation, depression, and sociodemographic factors on drinking behavior. We used the AUDIT 3 scale including frequency of drinking, average amount of drinking, and frequency of excessive drinking. The two characteristics of Korean drinking are consistent with the claim of the ecological system theory that humans, as social beings, drink to facilitate social communication or promote problematic drinking when social communication is difficult. Drinking among Koreans is characterized by a pattern that alternates between social drinking and problem drinking. Our study recognizes drinking as a social problem that should be managed at social as well as national levels. Drinking is not only a personal act affecting the individual body, personality, and emotions, but also a social action influenced by various environmental factors, such as family, social culture, and economy, leading to a habit. Harmful alcohol consumption is considered one of the major risk factors threatening the health of the global population . ProblemDrinking is largely influenced by social factors, such as social norms and beliefs about the harmful effects of drinking, in each country . AccordiHowever, in Korea, drinking is a part of a tolerant, permissive culture, and the harm of drinking tends to be perceived as an individual health risk rather than a social problem. Thus, little effort has been made to understand drinking problems in a social context . DrinkinJessor  reportedThe ecological theory introduces an ecological perspective to the general system theory and provides a framework for understanding the relationship between humans and the environment. In this theory, humans and the environment interact to form an integrated system by maintaining homeostasis. The theory hypothesizes that the individual as a complete being is understood in social situations and behavior and personality are formed and developed as humans adapt to and modify environmental needs to suit their needs ,10.Jessor  argued tTraditionally, drinking in Korea is part of a strong group culture that tolerates male drinking and is a group behavior that strengthens solidarity. Similar to foreign countries, there are gender differences in drinking motives, and young men often drink socially to promote social relationships, while women predominantly drink to relieve negative emotions ,13. In aThe Korea National Health and Nutrition Examination Survey by the Ministry of Health showed that the drinking rate of Korean adults in 2019 decreased with age . In 2019In recent years, the number of single-person households has increased rapidly in Korea. According to a press release by Statistics Korea, in 2019, three in ten (30.2%) were single-person households in Korea . In multYoon and Lee  analyzedThe drinking culture in foreign countries is such that lower income is associated with increased drinking. According to the World Health Organization (WHO), groups with lower socioeconomic status are more vulnerable to problems caused by drinking . In foreThe Korea National Health and Nutrition Examination Survey by the Ministry of Health showed the monthly drinking rate for Korean adults in 2019 was highest in those with a mean income level, followed by those with high, high-median, median-low, and low-incomes . Kim et Khan et al.  found thMental disorders, including depression, are closely related to excessive alcohol consumption. Simultaneous experiences of a psychiatric disorder and substance abuse are referred to as dual diagnoses or co-occurring disorders . DrinkinIn 2020, the spread of coronavirus disease 2019 (COVID-19) worldwide brought about changes in people’s daily lives. In some countries, alcohol sales soared as people attempted to cope with stress . The MorIn the UK, factors related to drinking behavior during social distancing and local lockdown due to COVID-19 were assessed, and drinking in young age groups was found to increase (compared with times before the COVID-19 pandemic) . SimilarHere, we analyzed data from the Korea Welfare Panel Study (KoWePS) from the 5th year (2010) to the 15th year (2020) (11 years of data). The Korea Welfare Panel Data is a survey that dynamically assesses changes in the size and living conditions of the poor, working poor, and near-poor, in which the household type, income level, and employment status are rapidly changing. Thus, the KoWePS database dynamically identifies the living conditions and welfare needs of various population groups according to age, income level, and economic activity. The data contain accumulated information on the drinking behavior of Koreans and their factor variables over a long period, which would be suitable for this study.The WHO uses tools such as the Alcohol Use Disorder Identification Test (AUDIT) and Cut, Annoyed, Guilty, Eye-opener (CAGE) to measure alcohol abuse and problematic drinking. The Korea Welfare Panel also evaluates 14 drinking-related questions that consist of 10 items on AUDIT-K and 4 on CAGE. AUDIT-K assesses frequency of drinking, the average amount of drinking, and frequency of excessive drinking and seven items on negative experiences after drinking. Previous studies conducted in Korea and other countries reported that AUDIT 3  is effective in screening problematic drinking compared with the 10 items of AUDIT ,35,36. TIn addition, independent variables of drinking in Koreans included age, household, sex, income level, education level, religion, marital status, and occupation, based on the literature review in The time-series trends of the main variables related to drinking were investigated, and panel analysis was performed using the STATA 16 package  to identify the factors of the three variables of the AUDIT scales. Panel data allow us to observe the dynamic trends between variables beyond static relationships at a specific time point . For exaThus, the random-effects model was used because it satisfied the assumption of uncorrelated heterogeneity by checking the estimated coefficient of time-fixed variables (sex) and controlling for time-fixed individual factors. The estimated panel model was as follows:μi = unobservable individual effects;λt = unobservable time effect;νi·t = remaining stochastic disturbance term.The frequency of drinking was analyzed in a time series from 2010 to 2020, as shown in First, increased age was associated with reduced frequency of drinking, average amount of drinking, and frequency of excessive drinking. Moreover, higher income and education levels were associated with a higher frequency of drinking. In contrast, education level showed a significant negative relationship with the frequency of excessive drinking and the average number of drinking. Compared with non-religious individuals and women, religious individuals and men showed a higher frequency of excessive drinking. As for marital status, divorced, bereaved, and separated groups drank more than those who were married. In addition, single-person households had a higher frequency of drinking, number of drinks, and frequency of excessive drinking compared with multi-person households. Regular workers and employers showed a tendency for increased frequency of drinking and problematic drinking.The increased total deprivation index had significant positive effects on the frequency of drinking and frequency of excessive drinking. It showed that low-income households consumed less alcohol, while deprived households were more likely to have problematic drinking behaviors. As depression increased, the frequency of drinking and problematic drinking increased. Lastly, satisfaction with health and social relationships increased the frequency of drinking, average amount of drinking, and frequency of excessive drinking. Furthermore, the frequency of drinking, average amount of drinking, and frequency of excessive drinking decreased as satisfaction with the residential environment, family relationships, and employment increased.The main results of this study are as follows: In Koreans, higher income and educational levels were associated with increased frequency of drinking, average number of drinking, and decreased frequency of excessive drinking. Groups with heavy alcohol consumption included men (compared with women), divorced/bereaved groups , and those with high total deprivation and depression indices. These findings are in agreement with the WHO  and the In the 2020 data that reflect the COVID-19 pandemic, the frequency of drinking was reduced; however, the average amount of drinking further increased. This finding suggests that although people drank less often due to social distancing, average amount of drinking consumed on each occasion increased compared with times before the COVID-19 pandemic. Therefore, drinking factors were not unilinear and showed different patterns according to the frequency of drinking, average amount of drinking, and frequency of excessive drinking.Therefore, this study suggests two different kinds of policy implications to improve the drinking culture in Korea. First, continuous efforts are needed to amend the drinking culture, which encourages social drinking. According to the research results, problem drinking among Koreans is not just a problem of the underprivileged. In addition, policy interest and support are required to reduce problematic drinking for single-person households, socioeconomically deprived groups, and those with difficulties in family relationships. Specific policies need to be created for these groups to detect and treat drinking problems early, through annual health checkups for alcoholism. In particular, institutional arrangements are necessary for the treatment of drinking problems within local communities.This study is meaningful because various factors of drinking over 10 years were comprehensively assessed. The characteristics of Korean drinking revealed through the panel analysis in this study  are in lSecond, single-person households, material deprivation, and dissatisfaction with family relationships were highly related to drinking among Koreans. The a1, a2, and a3 were significantly greater in single-person households, and residential environment satisfaction and family relationships significantly decreased a1, a2, and a3. Moreover, material deprivation and depression were factors that increased the average amount of drinking (a2). These findings were in contrast with previous studies that showed that multi-person households tended to have lower drinking behaviors due to the control and persuasion of family members , and theThese two drinking characteristics in Koreans are consistent with the ecological theory, which suggests that humans, as social beings, drink to encourage and facilitate social communication, and difficulties in social communication  promote problematic drinking behaviors . This loThird, drinking trends have changed since the onset of the COVID-19 pandemic. The pandemic has brought about considerable changes in the daily lives of people around the world. Following social distancing, the pattern of drinking has also changed. It was expected that social drinking would decrease, and problematic drinking in groups with poor goodness-of-fit would worsen. Many people hope to return to their pre-pandemic daily life with the help of vaccines; however, as mutations of the virus are being observed, new lifestyles and drinking patterns are expected to continue for the time being. Micallef  reported


The cumulative incidence of grade II–IV acute graft-versus-host disease (aGVHD) was lower in the Dec group . Compared with mBuCy group (15.5% ± 0.3%), a significantly higher proportion of limited chronic GVHD (cGVHD) in Dec group (35% ± 0.6%) was observed (P = 0.025). Dec-intensified mBuCy conditioning was associated with better 2-year overall survival (OS) and GVHD-free relapse-free survival (GRFS) . Our results also elucidated that the Dec group had better 2-year OS and lower 2-year cumulative incidence of relapse (CIR) in patients acquiring haploidentical HSCT than that of the mBuCy group , which did not increase the treatment-related mortality and regimen-associated toxicities. Dec-intensified myeloablative regimen and high-risk stratification were the variables associated with OS, leukemia-free survival (LFS), and GRFS in multivariate analysis. In high-risk patients, no differences were found in CIR, OS, LFS, and GRFS between the two groups. These data indicated that Dec-intensified mBuCy conditioning regimen was associated with better survival than mBuCy regimen in AML patients, especially in patients undergoing haploidentical HSCT.To identify the benefit of decitabine (Dec)-intensified myeloablative conditioning on the outcomes of patients with acute myeloid leukemia (AML) after related donor hematopoietic stem cell transplantation (HSCT), we performed a retrospective matched‐pair study from a pool of 156 patients to evaluate Dec [20 mg/m Allogeneic hematopoietic stem cell transplantation  is the backbone curative treatment for acute myeloid leukemia (AML) patients, especially for high-risk patients . Due to Recently, intensive MAC such as intensification of BuCy with fludarabine , cladribDec (5-aza deoxycytidine) acts as an inhibitor of DNA methyltransferase I, resulting in the reexpression of silent antioncogenes and the terminal differentiation of leukemic cells . FurtherA previous study observed durable response rate for high-dose Dec-intensified BuCy regimen in high-risk AML patients undergoing human leukocyte antigen (HLA)-identical sibling donor (ISD) transplantation . For higHerein, we retrospectively performed a matched‐pair study to clarify the association of Dec-intensified mBuCy conditioning regimen with the outcomes of intermediate- and high-risk AML patients undergoing either an ISD or a haploidentical donor (HID) transplantation.de novo AML undergoing related donor HSCT at our center between March 2012 and November 2020 was enrolled in this study. Among them, 58 patients had received Dec in combination with mBuCy as the conditioning regimen (Dec group), and 98 who received the mBuCy regimen were selected as the control group (mBuCy group). High-risk AML included the following: hyperleukocytosis at diagnosis, no response to induction chemotherapy, ≥complete response 2 (CR2) or not in remission, extramedullary leukemia, relapse within 6 months from induction or consolidation therapy, ≥two relapses or relapse after auto-HSCT, and high-risk cytogenetics according to the National Comprehensive Cancer Network 2018 guidelines (www.nccn.org) (A cohort of 156 patients who were diagnosed with ccn.org) . PatientAll patients or their legal guardians provided signed informed consent for the use of protected health data for research, as approved by the Institutional Review Board on Medical Ethics at Tongji Medical College of Huazhong University of Science and Technology and which has been conducted in accordance with the Declaration of Helsinki.2 intravenously (i.v.) every 12 h on day −8, busulfan 3.2 mg/kg/day i.v. on days −7 to −5, cyclophosphamide 1.8 g/m2/day i.v. on days −4 and −3, and semustine (Me-CCNU) 250 mg/m2/day orally on day −2. Dec-intensified mBuCy regimen included Dec 20 mg/m2/day i.v. on days −11 to −7, cytarabine 1.5 g/m2 i.v. every 12 h on days −9 to −7, followed by busulfan 3.2 mg/kg/day i.v. on days −6 to −4 and cyclophosphamide 1.8 g/m2/day i.v. on days −3 and −2, and Me-CCNU 250 mg/m2/day orally on day −1. Antithymocyte globulin  was used as part of conditioning in HID-HSCT as previously reported  stem cells and/or peripheral blood stem cells (PBSCs) were collected using standard mobilization protocols. All donors were given recombinant human granulocyte colony-stimulating factor (rhG-CSF) 8–10 μg/kg/day to mobilize BM and/or peripheral blood. For HLA-identical sibling and HLA 4–5/6 or 6–8/10 matched related donor transplantation, unmanipulated rhG-CSF-mobilized PBSCs were infused into the recipient on the day of collection. After removal of plasma and some of the red blood cells, the BM collections were resuspended in human albumin and infused.2 on day +1 and 10 mg/m2 on days +3, +6, and +11. MMF  was given orally from day −9 and tapered and discontinued after 2 months. Basiliximab was given i.v. at a dose of 20 mg on day 0 (2 h before graft infusion) and day +4.Cyclosporin A (CsA) with short-term methotrexate (MTX) was given to patients who received matched related donor transplantation. A regimen of tacrolimus (FK506), short-term MTX, mycophenolate mofetil (MMF), and basiliximab was given to patients who received haploidentical transplantation. Dosage adjustments of CsA or FK506 were based on plasma target levels of 150–250 or 7–15 ng/ml, respectively. CsA or FK506 was tapered and discontinued after 3–4 months in cases without GVHD. MTX was administered i.v. at dosages of 15 mg/mAll patients had BM examinations and minimal residual disease (MRD) evaluation by quantitative real-time PCR (qRT-PCR) or/and multiparametric flow cytometry (MFC) performed at 1, 2, 3, 4, 5, 6, 9, and 12 months after allo-HSCT and at 6-month intervals thereafter. Prevention of posttransplant hematologic or cytogenetic relapse includes immunosuppressant withdrawal and multiple consolidation chemotherapy and therapeutic donor lymphocyte infusion, with basic reference to Huang’s protocol .9/L for 3 consecutive days posttransplantation. Platelet engraftment was defined as an absolute platelet count >20 × 109/L without platelet transfusion for 7 consecutive days. Regimen-related toxicities were graded by common criteria according to Bearman et al.  , 30 to oP < 0.1 in CIR, OS, LFS, or GRFS univariate analysis was defined for selecting variables for entry into the multivariate model. All P values were two-sided, and P < 0.05 was considered significant. Statistical analyses were performed with SPSS 24.0 , R 4.1.2 , and the R package MatchIt.Descriptive statistics are presented as median values and ranges for continuous variables and percentages for categorical variables. Comparisons of variables between the two groups were performed using chi-square test, Fisher’s exact test, Student’s t-test, or Mann–Whitney U-test. LFS, OS, and GRFS were calculated by the Kaplan–Meier method and compared using the log-rank test. The cumulative incidence of TRM, relapse, and GVHD was estimated using the Gray’s test considering competing risks. TRM and relapse were the competing risks. For aGVHD and cGVHD, the competing events were relapse and death. The Cox proportional hazards model was used for multivariate analysis. All P = 0.014), risk stratification (P < 0.001), and donor HLA type (P = 0.028) were significantly different.All patient characteristics are summarized in P > 0.05).To overcome the imbalance among the aforementioned baseline variables, PSM was introduced and 46 case-control pairs (92 patients) were selected. After PSM, potential selection bias related to the two groups was minimized (P = 0.475). Platelet engraftment time was 13 days (range 5–21) in the Dec group and 11 days (range 7–26) in the mBuCy group, with engraftment rates of 93.5% and 97.8% (P = 0.609), respectively. In the Dec group, graft failure was observed in 1 patient, and this patient died 34 days after allo-HSCT.The neutrophil engraftment in Dec and mBuCy groups occurred at 12 days  and 11 days , respectively. The corresponding engraftment rates were 95.7% and 100% (P = 0.676) and diarrhea . Oropharyngeal mucositis was more often observed in Dec-intensified conditioning regimen. Twenty-nine cases (63.0%) of oropharyngeal mucositis (58.7% grade I–II and 4.3% grade III) were observed in Dec group vs. 16 cases (34.8%) in mBuCy group (32.6% grade I–II and 2.2% grade III) (P = 0.007). Reversible elevations of liver enzymes were also seen in the two groups . One patient in the Dec group had a seizure that was relieved by early intervention. Regimen-related cardiovascular or renal complications were not observed.Most cases experienced mild and moderate regimen-related toxicities. Toxicities involving the gastrointestinal tract were most common, including mild nausea/vomiting , 15.2% vs. 13.0% (P = 0.765), and 23.9% vs. 19.6% (P = 0.613), respectively  antigenemia, hemorrhagic cystitis, and respiratory infection for the Dec vs. mBuCy group were 43.5% vs. 60.9% . No difference in the 2-year CIR  was observed  and 9 months  . Two-year LFS of the Dec group was similar to that of mBuCy group , whereas for the mBuCy group was 23 months . Two-year OS and GRFS were significantly improved in the Dec group than those in the mBuCy group . Disease status before HSCT may be more susceptible to affect OS (P = 0.043). The conditioning regimen containing Dec had a beneficial influence on OS (P = 0.031), LFS (P = 0.016), and GRFS (P = 0.043). Additionally, donor/recipient sex match was associated with CIR (P = 0.029).Univariate analysis Table 4 P = 0.008), a lower OS , a lower LFS , and a lower GRFS . The conditioning regimen containing Dec had a positive impact on OS , LFS , and GRFS . Female donor/male recipient was associated with a lower CIR . However, disease status did not affect OS .These results prompted us to conduct a multivariate analysis Table 5,P = 0.036; P = 0.047; P = 0.097; P = 0.260; P = 0.073; P = 0.184; 2-year LFS: 62.2% ± 11.2% vs. 40.9% ± 10.5%, P = 0.160; 2-year GRFS: 40.9% ± 12.3% vs. 27.3% ± 9.5%, P = 0.180; P = 0.945, 11.0% ± 0.6% vs. 21.2% ± 0.8%, P = 0.279, respectively; 2-year OS, 75.8% ± 10.8% vs. 60.7% ± 10.9%, P = 0.340, 90.9% ± 6.1%, vs. 73.8% ± 9.3%, P = 0.170, respectively; 2-year LFS, 70.0% ± 11.5% vs. 55.9% ± 11.1%, P = 0.450, 84.4% ± 8.5% vs.70.4% ± 9.4%, P = 0.210, respectively; 2-year GRFS, 59.3% ± 12.1% vs. 31.7% ± 10.4%, P = 0.100, 75.5% ± 9.7% vs. 53.6% ± 10.3%, P = 0.089, respectively; Subgroup analysis of matched patients’ cohort was conducted according to the donor HLA type or risk stratification, and the number of patients in each subgroup was shown in To our knowledge, this retrospective matched‐pair study represents the first so far to evaluate the Dec-intensified mBuCy conditioning regimen in intermediate- and high-risk patients with newly diagnosed AML undergoing related donor HSCT.The choice of Dec in combination with mBuCy was made mainly on the basis of 1) excellent antileukemic activity by inhibiting DNA methyltransferase , 2) stroP = 0.570; 2-year LFS, P = 0.730; 2-year GRFS, P = 0.920), consistent with previous reports comparing outcomes of HID- to ISD-HSCT in AML patients in CR . Remarkably, for high-risk AML patients, Dec-intensified regimen was superior to our previous study  using idts in CR , 44. Alt2 i.v. on day −9 and 50 mg/m2 i.v. on day −8), our treatment plan (Dec 20 mg/m2/day i.v. on days −11 to −7) in high-risk patients achieved higher 3-year OS and 3-year LFS . This result, which is inconsistent with our study, may be attributable to the differences of Dec therapy and patient disease status pretransplantation. Additionally, in univariate analysis, the cumulative incidence of grade III–IV aGVHD and extensive cGVHD was not correlated with the outcomes possibly due to  may interfere with the conclusions. However, patients’ survival rate in this study was similar to that of previous studies. Tang et al.  retrospe2/day for 5 days) potentiates the antileukemic effects while retaining sufficient tolerability.More notably, previous research has shown that Dec had little hematological toxicities in allo-HSCT , 19, 37.However, some limitations of the present study should be considered. These include a single-center design, small sample size, and the relatively short follow-up time of some patients, which may reduce the reliability of the comparative inferences.In conclusion, Dec-intensified mBuCy conditioning regimen may be a priority for intermediate- and high-risk AML patients, especially in patients undergoing HID-HSCT, as it is able to provide better survival and tolerated toxicity; certainly, a randomized clinical trial is required to provide more definition.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Institutional Review Board on Medical Ethics at Tongji Medical College of Huazhong University of Science and Technology. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.YH and HFW designed the study. ZYL, WS, XL, HL, XNC, LT, HY, ZDZ, YY, and LHX selected and treated patients and contributed data. ZYL, WS, and XL analyzed the data and wrote the article. YH and HFW critically reviewed the article. All authors read and approved the final version of the article.This work was supported by the National Natural Science Foundation of China , Beijing Medical Award Foundation of China (YXJL-2019-0163-0114), and Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province, China (CXPJJH12000009-114).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The number of patients with bradyarrhythmia and the number of patients with cardiac pacemakers are increasing with the aging population and the increase in the number of patients with heart diseases. Some patients in whom a cardiac pacemaker has been implanted experience problems such as pacemaker infection and inconvenience due to electromagnetic interference. We have reported that overexpression of HCN channels producing a pacemaker current in mouse embryonic stem cell-derived cardiomyocytes showed enhanced pacing function in vitro and in vivo. The aim of this study was to determine whether HCN4 overexpression in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) can strengthen the pacing function of the cells.HCN4 was transduced in the AAVS1 locus of human induced pluripotent stem cells by nucleofection and HCN4-overexpressing iPSC-CMs were generated. Gene expression profiles, frequencies of spontaneous contraction and pacing abilities of HCN4-overexpressing and non-overexpressing iPSC-CMs in vitro were compared.Human β adrenergic stimulation. The pacing rates against parent iPSC line-derived cardiomyocytes were also higher in HCN4-overexpressing iPSC-CMs than in non-overexpressing iPSC-CMs.HCN4-overexpressing iPSC-CMs showed higher spontaneous contraction rates than those of non-overexpressing iPSC-CMs. They responded to an HCN channel blocker and If current, spontaneous firing and pacing function in iPSC-CMs. These data suggest this transgenic cell line may be useful as a cardiac pacemaker.Overexpression of HCN4 showed enhancement of The online version contains supplementary material available at 10.1186/s13287-022-02818-y. The number of patients with bradyarrhythmia is increasing with the increase in the number of patients with heart diseases associated with aging. The treatment for bradyarrhythmia is mechanical pacemaker implantation, but there are remaining issues such as battery replacement due to battery drain, infection, electromagnetic interference and rate response. Biological pacemakers generated by gene transfer or cell transplantation are expected to be used as a new treatment for resolving these problems.TBX18 gene transduction by an adenoviral vector has been shown to convert ventricular myocytes into sinoatrial node-like cells in vitro and in vivo.  However,TBX3 with a lentiviral vector during the cardiac mesoderm stage, but the proportion of sinoatrial node-like cells was only 20%. For each method, there is room for improvement in induction efficiency, yield and simplicity.Several methods for the induction of sinoatrial node-like pacemaker cells derived from human pluripotent stem cells (PSCs) have been reported. Yechikov et al.  increaseHCN4 into mouse embryonic stem cell-derived cardiomyocytes (ESC-CMs) to intensify their pacing function in vitro and in vivo [HCN4 encodes a hyperpolarization-activated cyclic nucleotide-gated potassium channel (HCN channel) producing a pacemaker current. It has been reported that human PSC-derived cardiomyocytes (PSC-CMs) express HCN4 and have some pacing potency that would be lost along maturation [Recently, designer cells with functions added by genetic modification, such as cells used in chimeric antigen receptor (CAR) T-cell therapy, are expected to become new tools for cell-based therapy , 10. Pre in vivo , 12. HCNturation –16. HCN4turation . In thisThe strategy to generate HCN4-overexpressing iPSC-CMs is summarized in Fig. EGFP_polyA” in pCAGIG , “splice acceptor (SA)_loxP_PGK promoter_neomycin resistance gene (Neo)_loxP” in pRosa26-DEST  and “AAVS1 3' homology arm_Ampicilline resistance gene (AmpR)_AAVS1 5' homology arm” in AAV-CAGGS-EGFP  were amplified by the polymerase chain reaction (PCR) with PrimeSTAR GXL DNA polymerase , purified using a QIAquick PCR Purification Kit , and then ligated with an In-fusion HD enzyme  to get the control donor vector, “pAAVS1_SA_loxP_PGK_Neo_loxP/CAG_ IRES_EGFP/AmpR”. Human HCN4 open reading frame (ORF) subcloned into pCMV6-XL5 vector (Cat#. SC303651) was purchased from Origene . HCN4 ORF was amplified by PCR, purified, and ligated with an Xho I-digested control donor vector to get “pAAVS1_SA_loxP_PGK_Neo_loxP/CAG_ HCN4_IRES_EGFP/AmpR”.The sequences of “CAG promoter_IRES_PCR primers are shown in Table 3/cm2 on 0.25 µg/cm2 iMatrix-511 silk -coated 6-well plates  [Human iPSCs generated from dermal fibroblasts obtained from a healthy subject with retroviral vectors were used in this study , 18. The States) . The medHuman iPSCs were harvested using StemPro Accutase Cell Dissociation Reagent. Ten µg of a non-linearized donor vector, 5 µg of AAVS1 1L TALEN  and 5 µg of AAVS1 1R TALEN  were transduced with a Nucleofector II/Program B-016  and an Amaxa Human Stem Cell Nucleofector Kit 1 . The cells were treated with 50 µg/ml G-418  for 10 days. EGFP-positive clones were picked up and expanded on iMatrix-511-coated plates in StemFit AK02N. The established clones were genotyped by PCR. PCR primers are shown in Table 2 iMatrix-511 silk-coated 12-well plates  at 3 × 105/cm2 in StemFit AK02N supplemented with 10 µmol/L Y-27632. The medium was changed with a medium without Y-27632 every day. Four days after plating, at day 0, cells were treated with 6 µmol/L CHIR99021  in RPMI 1640  supplemented with B-27 supplement minus insulin . After 24 h, at day 1, the medium was changed to RPMI supplemented with B-27 supplement minus insulin and 100 µmol/L ascorbic acid 2-glucoside . On day 3, the medium was changed to RPMI supplemented with B-27 supplement minus insulin and 5 µmol/L IWP2 . On day 5, the medium was changed to RPMI supplemented with B-27 supplement minus insulin. From day 7, the medium was changed every 2 or 3 days. On day 12, the medium was changed to DMEM, no glucose  supplemented with 16 mM L-( +)-Lactic acid  to remove non-cardiomyocytes [Our cardiac differentiation protocol was based on that previously reported by Lian et al.. Human iTotal RNA from iPSC-CMs was extracted using Trizol Reagent  and PureLink RNA Mini Kit . Complementary DNA was synthesized from 500 ng of total RNA using SuperScript IV VILO Master Mix  according to the manual and subjected to PCR amplification. PowerUp SYBR Green Master Mix  and Quantistudio 1 Real-Time PCR System  were used for quantitative PCR (qPCR). Quantitative PCR experiments were performed in duplicate. The qPCR data were processed by the ΔΔCT method. PCR primers are shown in Table HCN4-overexpressing iPSC-CMs were seeded on a Matrigel-coated 35 mm dish. The cells were fixed by chemical fixation and TEM was performed by Tokai Electron Microscopy .2 iMatrix-511 silk-coated plates, fixed in 4% paraformaldehyde , permeabilized with 0.1% Triton X-100/ phosphate-buffered saline (PBS), and blocked with 10% goat serum and 0.1% Triton X-100/ PBS . The cells were stained overnight at 4℃ with primary antibodies againstα-actinin , cardiac Troponin T , HCN4 , NKX2-5 , and Connexin 43 . Secondary antibodies were iFluor™ 488 goat anti-mouse IgG (H + L), iFluor™ 555 goat anti-mouse IgG (H + L), iFluor™ 488 goat anti-rabbit IgG (H + L), iFluor™ 555 goat anti-rabbit IgG (H + L)  and Alexa Fluor 555 goat anti-rat IgG (H + L) . Nucleus staining was performed with Hoechst 33,342 . Plasma membrane staining was performed with Wheat Germ Agglutinin, Alexa Fluor™ 350 Conjugate .The purified iPSC-CMs were plated on 0.5 µg/cm2, 1.2 MgCl2, 1.0 BaCl2, 2.0 MnCl2, 5.0 D-glucose, and 10 Hepes; pH 7.4. Pipettes (2–4 MΩ resistances) were filled with a pipette solution containing (in mM): 110 K-aspartate, 5.0 K2-ATP, 11 EGTA, 1.0 CaCl2, 1 MgCl2, and 5 Hepes; pH 7.2. Then 0.3 mg/mL Amphotericin B  was added to the pipette solution to achieve patch perforation . The If current was activated by a standard activation protocol. If currents through activated HCN channels were obtained during hyperpolarizing test pulses of 5 s between − 45 and − 125 mV in 20-mV increments from a holding potential of − 35 mV. Action potentials were measured using a modified Tyrode’s solution containing (in mM): 140 NaCl, 5.4 KCl, 1.8 CaCl2, 1.0 MgCl2, 5.5 glucose, and 5.0 HEPES; pH 7.4 (NaOH). The pipette solution contained (in mM) 110 DL-aspartic acid, 30 KCl, 1 CaCl2, 5 ATP-Mg, 5 Creatine P-Na, 5 HEPES, and 10 EGTA; pH 7.25 (KOH). To achieve patch perforation (series resistance: 10–20 MΩ), amphotericin B (0.3 mg/mL) was added to the pipette solution. The temperature was maintained at 35–36 °C by a TC-344B dual channel heating system .The perforated patch‐clamp technique was performed using an Axopatch 200B amplifier  and data were acquired with pClamp10.2/Clampfit (Molecular Devices). Cardiomyocytes were dissociated using TrypLE Select Enzyme (10X) (Cat#. A1217701) for 15 min, resuspended in RPMI 1640 supplemented with 20% fetal bovine serum and replated on Matrigel -coated cover glasses and incubated for 72 h. The cells were superfused with a bath solution containing (in mM): 132 NaCl, 4.8 KCl, 2.0 CaCl2 iMatrix-511 silk-coated plates at 1 × 105/cm2 in RPMI 1640 with B-27 supplement and incubated for 4 days. We counted spontaneous eating frequencies for 15 s with an optical microscope and examined responses to 10 µmol/L ivabradine  and 1 µmol/L isoproterenol .The purified cardiomyocytes were plated on 0.25 µg/cm5 EGFP-positive iPSC-CMs were resuspended in 30 µL RPMI 1640 with B-27 supplement, 10 µmol/L Y27632 and 5% fetal bovine serum  and then seeded on the iMatrix-511-coated area (day 0). On day 1, 1 mL/well RPMI 1640 with B-27 supplement was added. On day 3, the medium was changed to RPMI 1640 with B-27 supplement and 1 µg/well iMatrix-511 silk. On day 4, 4 × 105 EGFP-negative parent iPSC-derived cardiomyocytes were resuspended in 1 mL RPMI 1640 with B-27 supplement, 10 µmol/L Y27632 and 5% fetal bovine serum and seeded on 12-well plates containing EGFP-positive cardiomyocytes. On day 5, the medium was changed to RPMI 1640 with B-27 supplement. On day 7, synchronized beating rates for 15 s of EGFP-negative iPSC-CMs away from the EGFP-positive area were counted with an optical microscope.The center of each well of 12-well plates was coated with 30 µL PBS containing 0.5 µg iMatrix-511 silk. Next day, 4 × 10P < 0.05 were considered significant.All data are expressed as means ± standard deviation. Statistical analysis was performed by Student’s t-test for unpaired data or one-way ANOVA with comparison of different groups by Dunnett’s post hoc test using SPSS ver. 24. Values of HCN4 ORF was inserted with TALEN in the AAVS1 region, where the transgene is less susceptible to silencing during differentiation [Human ntiation , 23. HomIf current was also confirmed by the patch clamp technique  were not upregulated in HCN4-overexpressing iPSC-CMs , which may not be sufficient to activate HCN channels [If was observed in HCN4-overexpressing cardiomyocytes at − 45 and − 65 mV as shown in Fig. If current caused by HCN4 overexpression led to an enhanced pacing effect of iPSC-CMs. So far, many groups, including us, have reported an increase in the frequency of sponteneous firing and the addition of a pacing function by transduction of HCN genes in mouse ESC-CMs, mesenchymal stem cells or the left ventricle [In this study, we generated HCN4-overexpressing cardiomyocytes from human iPSCs, and showed that HCN4 overexpression in human cardiomyocytes evoked a higher spontaneous firing frequency than that of control cardiomyocytes, resulting in enhanced pacing function in vitro. PSC-CMs are immature and lack channels . Howeverentricle , 25–32. HCN4 into the AAVS1 safe harbor locus. We have also experienced loss of EGFP expression during differentiation in cardiomyocytes and non-cardiomyocytes derived from EGFP-positive iPSC lines without homologous recombination of the transgene (data was not shown).As previously reported , 33, it HCN4-overexpressing cardiomyocytes were induced very efficiently since the commonly used efficient cardiac differentiation protocol using a GSK3 inhibitor and a Wnt inhibitor and the cardiomyocyte purification method can be applied , 21, 34.In the embryonic heart, HCN4 expression is first seen in the cardiac crescent, the source of the left ventricle and atria, and as it develops, it becomes localized to the conduction system, including the sinoatrial node , 36. In Only in vitro studies have been conducted. It has not been investigated how much pacing can be done in vivo. Since the connexin expression patterns are different between the atrium and ventricles or between healthy and diseased hearts , the pacThe firing frequency probably depends on the expression level of transduced HCN4. Although EGFP expression was detected for more than 3 months in vitro in this study, the maintenance of transgene expression over a longer period should be checked, especially in vivo.In addition, the possibility of tachycardia is a concern because some groups have reported that transplantation of a large number of pluripotent stem cell-derived cardiomyocytes transplanted into an infarcted heart resulted in ventricular tachyarrhythmias –42. HoweCardiac Troponin I promoter-driven human HCN4 transgenic mice showed similar cardiac morphology at birth but cardiac dilatation after birth due to dysregulation of calcium homeostasis and increased myocardial apoptosis . TherefoSmall animals such as mice and rats have a faster heart rate than humans (> 350 bpm). Even if atrioventricular block is performed to create a bradycardia model, its heart rate is more than 120 bpm , making it difficult to evaluate the pacing function of human cells. Therefore, in vivo studies need to use large animals such as monkeys or pigs that have been treated with immunosuppressive drugs. In addition, tumorigenic potential should be assessed.If current and pacing ability. Application of these cells with added pacing function to a biological pacemaker is expected in the future.We generated HCN4-overexpressing human iPSC-CMs showing enhancement of Additional file 1: Differentiation of day 8 cardiomyocytes derived from HCN4-overexpressing human induced pluripotent stem cells.Additional file 2: HCN4-overexpressing cardiomyocytes on differentiation day 103 (GFP).Additional file 3: HCN4-overexpressing cardiomyocytes on differentiation day 103 (phase contrast).Additional file 4: HCN4-overexpressing cardiomyocytes on day 27Additional file 5: Non-overexpressing cardiomyocytes on day 27.Additional file 6: Drug response of HCN4-overexpressing cardiomyocytes (no treatment).Additional file 7: Drug response of HCN4-overexpressing cardiomyocytes (10 µmol/L ivabradine).Additional file 8: Drug response of HCN4-overexpressing cardiomyocytes (1 µmol/L isoproterenol).Additional file 9: Parent cardiomyocytes paced by HCN4-overexpressing cardiomyocytes. The left half is HCN4-overexpressing cardiomyocytes and the right half is parental line-derived cardiomyocytes.Additional file 10: Parent cardiomyocytes paced by non-overexpressing cardiomyocytes. The left half is non-overexpressing cardiomyocytes and the right half is parental line-derived cardiomyocytes.