Barcode medication administration (BCMA) is advocated as a technology that reduces medication errors relating to incorrect patient identity, drug or dose. Little is known, however, about the impact it has on nursing workflow. Our aim was to investigate the impact of BCMA on nursing activity and workflow. A comparative study was conducted on two similar surgical wards within an acute UK hospital. We observed nurses during drug rounds on a non-BCMA ward and a BCMA ward. Data were collected on drug round duration, timeliness of medication administration, patient identification, medication verification and general workflow patterns. BCMA appears not to alter drug round duration, although it may reduce the administration time per dose. Workflow was more streamlined, with less use of the medicines room. The rate of patient identification increased from 74% (of 47) patients to 100% (of 43), with 95% of 255 scannable medication doses verified using the system. This study suggests that BCMA does not affect drug round duration; further research is required to determine the impact it has on timeliness of medication administration. There was reduced variability in the medication administration workflow of nurses, along with an increased patient identification rate and high medication scan rate, representing potential benefits to patient safety. Duration of drug administration rounds;Timeliness of medication dose administration;Active identification of patients by nurses;Active verification of medication by nurses (BCMA only);Walking patterns of nurses on the ward.Barcode medication administration (BCMA) involves scanning a patient’s unique barcode and medication barcodes to verify that these are correct before proceeding to administer a dose. It is becoming more common in many countries, often integrated with electronic prescribing and medication administration (ePMA) systems . BCMA isThe study was undertaken on two surgical wards at a large acute London hospital. Data were collected on a ward that had not yet implemented BCMA , and on a ward with the same physical layout (a vascular surgery ward) four days after BCMA implementation. Each ward had a total of 24 beds, distributed among four bays plus four side rooms. During each drug round, nurses administered doses from ward stock, non-stock medication dispensed to individual patients, and patients’ own medication from home. Patients’ own and individually dispensed medication was typically stored in bedside lockers; ward stock medication was stored in the medication room and/or in lockable cabinets underneath computers on wheels (COWs). About half of the ten COWs had cabinets for medication storage on the non-BCMA ward, though these were used by all staff and sometimes were not available for use during drug rounds. On the BCMA ward, five COWs were fitted with a larger medicine cabinet and designated for drug rounds only, while five COWs without cabinets remained in place for general use. Nurses accessed the ePMA system on the COWs to determine the doses due and record their administration. Prior to BCMA introduction, each individual dose had to be electronically signed to indicate that it had been given. Drug rounds were scheduled for 8am, 12pm, 2pm, 6pm and 10pm, with most medication scheduled to be given at these times. Each nurse would be responsible for one bay of five beds and possibly a side room.The ePMA and BCMA systems were part of Cerner Millennium . FollowiThis was a comparative study with direct observation used to collect data during 8am drug rounds. Data were collected for ten consecutive weekdays during November 2019 on the non-BCMA ward, and then for ten weekdays during December 2019 on the BCMA ward. Random sampling was used before commencing observations to determine that bay “H” would be selected for observation every day. Nurses responsible for that bay at each drug round were approached and invited to give consent for observation. A pharmacy student observed the nurse concerned, aiming to be as unobtrusive as possible. The study was approved locally as a service evaluation; NHS ethics approval was not required. Data were collected on the outcome measures in Additionally, on eight drug rounds on each ward, a spaghetti diagram ,19 was ct-test was used to determine whether any differences were statistically significant. This was also done for mean time per patient and mean time per dose as secondary analyses. Timeliness of medication administration was determined by calculating the mean time difference between scheduled and administered dose times on each ward. The percentage of patients whose identification was checked was calculated on each ward, and a chi-square test used to identify whether any difference was statistically significant. Additionally, on the BCMA ward, the percentage of medication doses that were verified was calculated. All spaghetti diagrams were visually compared to identify differences in walking patterns and task activities.Quantitative data were entered into an Excel spreadsheet. Following visual inspection of the data to rule out any obvious skew, mean drug round duration (our a priori primary outcome measure), was calculated for each ward; an unpaired Ten drug rounds were observed on each ward; between three and six patients received one or more doses of medication on each ward. The mean number of doses administered was 17 on the non-BCMA ward (range 7 to 25) and 29 on the BCMA ward (range 21 to 38). Seven different nurses were observed on the non-BCMA ward, and eight on the BCMA ward.p = 0.98; t-test). There was wide variation in drug round duration (The mean drug round duration was 67.8 min (range 27 to 98 min) on the non-BCMA ward, and 68.0 min (range 48 to 99 min) on the BCMA ward . The mean time per dose was 4.2 min (range 3.3 to 5.5 min) on the non-BCMA ward and 2.3 min (range 1.8 to 3.2 min) on the BCMA ward .The mean time per patient was 14.1 min (range 9.0 to 19.6 min) on the non-BCMA ward and 16.0 min (range 11.6 to 20.0 min) on the BCMA ward . Despite drug rounds being scheduled to start at 8am, we noted that drug rounds on the BCMA ward generally started later, with seven of ten drug rounds beginning after 08:30 am, compared to the non-BCMA ward where only one began after 08:30 am.On the non-BCMA ward, a total of 185 medication doses were due to be given, of which 165 (89%) were administered. On the BCMA ward, 363 medication doses in total were due to be given, of which 294 (81%) were administered. Doses were not administered due to patient refusal, the dose requiring review or if the dose was not available on the ward. The mean time difference between the scheduled and administered dose times was 60.3 min on the non-BCMA ward, and 67.5 min on the BCMA ward. Medication administration appeared to be more timely on the non-BCMA ward . On the non-BCMA ward, all positive identifications involved the nurse manually checking the patient’s wristband. On the BCMA ward, BCMA was used to check patient identity in 40 cases (93%) with nurses also confirming this manually in 13 cases (30%). Nurses were not permitted to take the COW into side rooms due to infection control policy, so in the three cases (7%) where medication was administered to a side room patient, they scanned a barcode sticker from the patient’s notes instead of the wristband. In all three cases, the patient’s wristband was also checked manually.Nurses administered medication to 47 patients on the non-BCMA ward of whom 35 (74%) had their identification checked prior to medication administration. On the BCMA ward, nurses administered medication to 43 patients, of whom all 43 (100%) had their identification checked, showing an increase in positive patient identification with BCMA , of doses administered had a scannable barcode. Those without a scannable barcode included patients’ own drugs that had been repackaged in non-original packs, and insulin pens stored individually without their box. Although all other medication had scannable barcodes, some products such as nutritional supplements and vitamins could not be identified by the BCMA system, necessitating manual signing for administration. Of the 294 doses administered, 243 (83%) were scanned prior to administration. This represented 95% of scannable doses, with only 12 potentially scannable doses not being scanned. Of these 12 doses, 11 were subcutaneous enoxaparin doses prepared in the medicines room. While the boxes could have been scanned, four of the five nurses who administered enoxaparin chose to leave the box in the medicines room.Overall, 14 cases of a BCMA system warning were observed, representing 6% of doses scanned. One (0.4% of doses scanned) was an error in which the nurse had selected the wrong formulation, selecting co-amoxiclav tablets when injection was prescribed; the nurse rectified this by selecting the correct form. The remaining 13 comprised seven cases when the dose could not be identified from the barcode, three where a variable dose had been prescribed requiring manual entry of the exact dose given, two where half a tablet was needed for the dose and the system required manual amendment as it had detected an apparent overdose, and one where the dose had been detected as being administered too early that the nurse overrode. In each case, the nurse took corrective action to either manually amend the record or enter a reason for overriding the system.An example of the spaghetti diagram is provided for both the non-BCMA  and BCMAOur results suggest that introduction of BCMA does not affect drug round duration, although comparison is challenging due to more doses per patient being given on the BCMA ward. The reduced time per dose on the BCMA ward suggests that nurses were able to administer more doses in a similar time-frame. While the findings suggest that BCMA decreased the timeliness of medication administration, this may be because drug rounds generally began later on the BCMA ward. If they had begun at approximately the same time, it is likely that no such difference would have been observed. In fact, the lower time per dose with BCMA indicates there may be potential to improve timeliness since BCMA has streamlined and harmonised processes, thus reducing the amount of walking to the medicines room to gather and prepare doses. Our data suggest that BCMA has led to a 100% patient identification check, since scanning of a patient barcode is required to administer medication. However, the workaround of scanning a barcode from the patient’s notes instead of their wristband for side room patients was performed by three different nurses, suggesting that this may have become relatively common practice. This is potentially counterproductive as it could increase the risk of errors relating to the wrong patient.The high scan rate of medication is encouraging, and it can be deduced that nurses generally adhered to use of the BCMA system when administering doses. However, because the majority of nurses failed to scan enoxaparin boxes, it is not clear whether they were always aware that these could be scanned, or whether they chose not to scan these for practical reasons. As well as the potential patient safety benefits of increasing positive patient identification, we found that for one of 243 scanned doses scanning the medication barcode resulted in the nurse correcting an error that may otherwise have reached the patient.We explored the impact of BCMA as an addition to ePMA rather than a paper-based system and used different measures of workflow, limiting comparison to previous studies ,11. FindBCMA appears to make the medication administration process more uniform by requiring nurses to use the medicines cabinet in the COW, thus streamlining the process. It also has potential to reduce time spent on medication documentation by allowing nurses to sign once for all doses rather than individually. Despite this, total drug round time did not differ among our two wards. This may be because a greater number of doses were administered on the BCMA ward, and/or because scanning barcodes did take some time, especially for the patient’s wristband where manoeuvring the COW and then aiming the scanner correctly often appeared difficult. As BCMA is increasingly being introduced within inpatient settings , such fiThe high rates of active patient identification and medication verification are potentially advantageous to patient safety, although the latter could be improved if a greater proportion of identifiable medications were included in the database, and if nurses better understood which medications could be scanned. This may occur after a longer adjustment period or additional training. Conceivably, BCMA could also lead to complacency as our observations suggest that some nurses believed that they no longer needed to verify details such as expiry dates with the BCMA system.As one of few studies to explore the impact of BCMA on nursing workflow, the findings provide a useful basis for further research. A particular strength is that a single observer collected data and made descriptive assessments of workflow, thus supporting consistency of findings. Data collected through observation also overcomes the limitations of self-reporting. However, a limitation is that the study took place on two different wards. We had initially hoped to study the same ward before and after BCMA implementation but delays in BCMA implementation on the original study ward meant this was not possible in the time available. While the second ward was very similar to the first, there were differences which may have introduced bias. For instance, the difference in practice around drug round start times has likely affected our data on the timeliness of medication administration, the wards used different COWs, and there was a considerable difference in the number of doses given per drug round and per patient, which will have affected drug round duration. Other differences in practice cannot be ruled out. Data collection on the BCMA ward took place relatively soon after implementation, and staff may not have been fully familiar with the system. Our study is also relatively small and it is not known to what extent the findings may generalise to other institutions with different medication administration practices and BCMA systems. Finally, our findings may have been influenced by the effect of the observer on those observed , althougSubsequent research should be conducted on the same wards to enable direct comparison of workflow before and after BCMA introduction, as well as observing drug rounds at different times of day. Studies collecting data over a longer duration and with larger sample sizes would be advantageous, preferably several months after BCMA implementation to reflect practice after an initial adjustment period. Additional use of a time-motion or work sampling method to detect quantitative differences in time spent on various nursing activities would shed further light on the findings. The findings of this study suggest that BCMA does not adversely affect the length of time taken by nurses to administer medication. While our data suggest that medication administration was less timely with BCMA, this should be interpreted with caution and further research is required, studying the same wards pre- and post-BCMA introduction. Overall, BCMA appears to have led to less variability in how nurses undertake medication administration. The significant increase in active patient identification as well as high proportion of medications verified using the system represent potential benefits to patient safety.


Mixed methods ANOVAs were used to determine visual search strategies and efficiency and used to indicate the relationship between visual search and verbal commentary data. The speed of video playback was the main determinant of visual search behavior, significantly impacting the visual search rate and relative fixation duration at a number of areas of interest. The use of a visual pivot indicated all participants' visual search strategies were efficiency driven. Overall, the verbal commentary did not completely align with the eye tracking data and there were varying degrees of agreement with the identified technique related areas of interest for coaches and biomechanists. However, differences in visual search strategy and verbal commentary suggest that experiential knowledge impacts participants' observation and perception of sprinting technique.It is common for applied sport biomechanists and high-performance coaches to work closely together. A feature of this relationship is that both bring unique experiences and knowledge to the common goal of improving an athlete's performance. For sprint running, coaches and biomechanists place importance on different aspects of technique. The purpose of this paper was to determine if these differences in experiential knowledge impact coaches and biomechanists visual perception of sprinting technique. Sport biomechanists ( In high-performance sport, it is common for sport scientists, such as biomechanists, to work closely with coaches to improve an athlete's performance , of varying sprint abilities and backgrounds, were filmed sprinting to present to the coach and biomechanist participants. Four athletes were national-level male rugby union players (SD = 0.5 s). Rugby union is a sport where sprinting speed is crucial and sprinting sessions are a part of the players training schedule. The athletes were purposefully recruited with the aim of displaying variability of sprinting technique, allowing the participants opportunity to provide a variety of verbal comments.Ten athletes . The distance of each sprint effort was self-selected by the athletes to ensure they were not accelerating through the 10 m light gate zone. Athletes were filmed from the sagittal plane using 5 fixed Prosilica GE cameras  capturing at 100 Hz. Cameras were strategically placed so there was overlap in the field of view, allowing the videos to be synchronized and stitched together. Video from the fastest sprint effort from each athlete was edited to show the athlete running at a constant speed, removing accelerations and decelerations. Athletes wore plain dark clothing to minimize chances of identification or distraction by the participants.As biomechanists and coaches commonly use video for technique feedback, two speeds were chosen, through pilot testing, to closely reflect typical use  and adjustments made to their seating position and the viewing screen position to ensure comfort before the three-point calibration procedure that is standard to the eye tracker software was conducted. They were calibrated and viewed the videos on a 13-inch tablet . The calibration consisted of using a black screen with three white crosses at different edges of the screen to represent the entire viewing area. After calibration, participants viewed four familiarization videos and practiced the verbal responses with the opportunity to clarify any procedures with the researchers.For testing, participants were shown 20 videos, while their eye movements were recorded. After viewing each video, they were asked to provide a verbal response to the statement “Please describe your assessment of this athlete's technique.” There was no time limit for the verbal responses and participants were encouraged to recall as much as possible, however, prompting and probing questions from the researcher were limited. Verbal responses were recorded using the microphone built into the eye tracking glasses. After the response was provided, a 3 s countdown prompted the beginning of the next video. Participants were given a short non-timed break after 10 videos to adjust their position or rest, and if required, re-calibration took place. The duration of each data collection session varied from 20 to 45 min, depending on the length of verbal responses and break taken after 10 videos.Eye tracking data was analyzed using BeGaze  where individual fixations were identified, and fixation locations coded. Locations were coded as areas of interest: Arm, Arm (1), Head, Lower Leg, Lower Leg (1), Pelvis, Torso, Upper Leg, Upper Leg (1), Visual Pivot UB, Visual Pivot LB and Other. Visual Pivot locations are for upper or lower body only and not tied to a specific area outside the body and “(1)” denotes the limb behind the torso or pelvis not the left or right side. Inter-rater agreement for assessing fixation location was high (Cohen's kappa = 0.889)  effect sizes were used to support the ANOVA results.Participants' data was averaged for each video condition, with the non-altered speed denoted as “Fast” and the slow-motion condition denoted as “Slow.” The visual search variables collected were number of fixation locations, number of fixations per second and fixation duration. For each location, relative duration was calculated as the percentage of total time spent fixating on that location relative to the length of the clip. A 3 × 2 analysis of variance (ANOVA) was used to determine the effect of expertise (between-group) and video speed (within-group) on visual search behavior, statistical significance was set at The verbal commentary audio of sprinting technique was coded using a framework adapted from swimming technique research  = 518, p < 0.05, ηp2 = 0.98] and fixation rate . The average number of fixations for the fast video clips was 4.15 (SD = 0.95) and 20.93 (SD = 4.31) for the slow video clips. The average number of fixations per second for the fast video clips was 1.27 (SD = 0.28) and 1.36 (SD = 0.28) for the slow video clips.Video speed significantly affected the number of fixations  on this location compared to the expert coaches and biomechanists during the Fast video clips.Expertise did not significantly affect the relative duration of fixations at any of the 12 fixation locations. There was also no interaction between the speed and expertise variables, apart from the relative duration of fixations on the upper leg location. Developing coaches spent significantly less time fixating . The average number of comments for the fast video clips was 1.2 comments (SD = 0.55) and 1.86 comments (SD = 0.71) for the slow video clips.The number of verbal comments was significantly affected by video speed . Participants made significantly more comments related to the lower leg, lower leg (1), pelvis, upper leg and visual pivot upper body locations (SD = 12.9%) of possible sprinting technique features, with significantly more comments related to the head.As suggested by the visual search data, for the slow videos all participants mentioned a significantly high percentage of features, on average 40.4% . The presence of the visual pivot positions were an example of participants altering their visual search strategy and possibly relying on peripheral vision due to the time restricted nature of the task and to increase efficiency  Victoria University, Melbourne, Australia. The patients/participants provided their written informed consent to participate in this study.AW collected data, conducted analysis with DP, and wrote first draft of manuscript. DP, EP, and AD contributed to manuscript revision and approved the submitted version. All authors contributed to the idea and design of study.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.


Drinking alcohol during pregnancy can cause fetal alcohol spectrum disorders, including birth defects, behavioral disorders, and impaired cognitive development , marijuana, opioids (prescription pain reliever misuse and heroin use), and “other substances,” which included cocaine, hallucinogens, inhalants, methamphetamines, and the misuseData were weighted to adjust for nonresponse and to generate nationally representative estimates. Prevalence estimates and 95% confidence intervals (CIs) for past 12 months drinking, current drinking, and binge drinking were calculated overall and by sociodemographic and pregnancy characteristics . Prevalence estimates and 95% CIs for past 12 months and current drinking alone and with co-occurring substance use among pregnant respondents also were calculated. Analyses were conducted using SAS  with SUDAAN  to account for the complex sampling method used in NSDUH. This activity was reviewed by CDC and conducted consistent with CDC policies and procedures.Among 99,618 female respondents aged 12–44 years, 3,006 (3%) reported a current pregnancy. Among pregnant respondents, past 12 months drinking, current drinking, and binge drinking prevalence estimates were 64.7%, 9.8%, and 4.5%, respectively . Past 12Among respondents who were pregnant and reported drinking in the past 12 months, 41.7% also reported using at least one other substance in the past 12 months. The most commonly reported substances were tobacco (30.3%), marijuana (21.9%), and opioids (7.0%) . Among rOverall, 19.6% of respondents who were pregnant reported past 12 months drinking and tobacco use, 14.2% reported past 12 months drinking and marijuana use, 4.5% reported past 12 months drinking and opioid use, 2.7% reported current drinking and tobacco use, and 2.0% reported current drinking and marijuana use.During 2015–2018, approximately half of all pregnant respondents who reported current drinking (drinking in the past 30 days) (9.8%) also reported binge drinking (4.5%). Among pregnant females who reported current drinking, 38.2% also reported current use of one or more other substances, including tobacco, marijuana, opioids, and other substances. The estimates of current drinking and binge drinking among pregnant females are consistent with recent analyses using data from the 2015–2017 Behavioral Risk Factor Surveillance System, which reported current drinking and binge drinking estimates of 11.5% and 3.9%, respectively, among pregnant respondents aged 18–44 years (,Few population-based reports consider co-use of other substances among pregnant females who drink alcohol. In this report, current drinking overall and in combination with one or more other substances were substantially lower than past 12 months drinking, suggesting that females decrease their use after they know they are pregnant. Alcohol exposure during pregnancy can adversely affect fetal development, resulting in behavioral disorders, impaired intellectual development, and birth defects (The findings in this report are subject to at least four limitations. First, data are self-reported and therefore subject to social desirability bias; respondents might underreport substance use because of social stigma and legal implications. Second, because NSDUH only ascertains past 12 months and past 30 days substance use in a cross-sectional sample, patterns across individual pregnancies are unknown. Estimates of any substance use during the length of an entire pregnancy would likely be higher than estimates of past 30 days use. Third, limited sample size necessitated the suppression of some prevalence estimates. Finally, some pregnancies might not have been recognized at the time of the interview, resulting in misclassification by pregnancy status. Alcohol use and other substance use presumably are lower in recognized than in unrecognized pregnancies, resulting in underestimation of exposure levels.The U.S. Preventive Services Task Force recommends alcohol screening and brief behavioral counseling in primary care settings for all adults aged ≥18 years (Drinking alcohol during pregnancy can cause miscarriage, stillbirth, and fetal alcohol spectrum disorders; however, approximately one in nine pregnant females report current drinking. Little is known about the co-use of other substances by females who drink during pregnancy.Pregnant respondents in the first trimester reported higher current alcohol use than respondents in the second or third trimester. Among first trimester respondents, 19.6% reported current alcohol use and 10.5% reported binge drinking; among second or third trimester respondents, current drinking and binge drinking were reported by 4.7% and 1.4%, respectively. Approximately 40% of pregnant females reporting current drinking also reported current use of other substances.Co-use of other substances is common among females who drink alcohol during pregnancy. Screening and interventions for alcohol and other substances in pregnancy could improve the health of mothers and their children.


Lower respiratory tract infections (LRTIs) account for significant morbidity and mortality in patients admitted to hospitals worldwide, especially in children and elderly. The prevalent microorganisms and antibiotic susceptibility were investigated among LRTI patients from the pediatric ward, adult respiratory ward, and respiratory intensive care unit (RICU) in order to achieve more efficient treatment protocols and better recovery.In this retrospective cross-sectional study (January 2016 to December 2019), 4,161 positive culture samples out of 18,798 different specimens  from LRTI patients were analyzed for pathogen incidence and antibiotic sensitivity.Pseudomonas aeruginosa was the dominant pathogen in both the adult respiratory ward  and RICU , whereas Staphylococcus aureus  was the most common bacterium in the pediatric ward. Among the blood cultures, Gram-positive bacteria remained the major microorganisms involved in LRTIs, and the most frequent pathogen was Staphylococcus epidermidis  in the pediatric ward and Staphylococcus aureus  in adult respiratory ward. However, Gram-negative bacteria were the main pathogens in the RICU, of which Klebsiella pneumoniae  is the most prevalent. Pseudomonas aeruginosa of LRTI patients remained highly susceptible (>70%) to routine antibiotics in pediatric ward. However, it only had high susceptibility to amikacin, tobramycin, gentamicin in both the adult respiratory ward and RICU and its antibiotic sensitivity to meropenem and imipenem was moderate in the adult respiratory ward and mild (<30%) in the RICU. Staphylococcus aureus isolated from LRTI patients was highly susceptible to linezolid, daptomycin, teicoplanin, vancomycin, tigecycline, rifampicin, and trimethoprim/sulfamethoxazole in all three wards, moderately susceptible to gentamicin in both the adult respiratory ward and RICU and to clindamycin, oxacillin, moxifloxacin only in the adult respiratory ward.Among the respiratory tract cultures, the frequency of Gram-negative bacterial strains was higher than Gram-positive bacterial strains. Microbial distribution and their patterns of antibiotic susceptibility revealed a high divergence among LRTI patients admitted to different wards in this hospital. Thus, different antibiotic therapies should be considered for distinct age groups. Lower respiratory tract infections (LRTIs) are common among patients worldwide, and the most major cause of pneumonia and bronchiolitis in hospitalization. There are 3.5 million deaths attributed to and 79 million disability-adjusted life-years (DALYs) lost to LRTIs which are associated with high overall morbidity and mortality in adults, especially in patients older than 70 years old . The eldCurrently, irrational administration of antimicrobial agents and inappropriate antibiotics therapy introduced serious antibiotic resistance, increased hospital mortality rate, and healthcare-related expenditure for LRTI patients . ChineseAntibiotics can also be performed in surveillance programs to improve treatment protocols and indicate the trends in microbial resistance . HoweverWe conducted a retrospective cross-sectional study in Department of Pediatrics, Department of Respiratory Medicine and Department of RICU in Peking University First Hospital  from 1 January, 2016 to 31 December, 2019. A total of 4,161 positive culture samples from 18,841 different specimens  were obtained from patients diagnosed with LRTIs in pediatric wards, adult respiratory wards, and RICU . The res2-enriched atmosphere for 24–48 h at 37°C. Blood specimen was inoculated into a commercial culture bottle and analyzed by an automated monitoring system for bacterial detection . The incubation was continued until a positive result was observed or up to a maximum of 5 days.Microorganism cultures were conducted according to the routine standard operation procedure (SOP) by the Department of Clinical Laboratory in our hospital. Each respiratory tract specimen was inoculated on a blood agar media (Columbia A), a China blue lactose rosolic acid agar plate and a chocolate agar plate (with Vancomycin)  using the streak plate technique and incubated under a COPseudomonas aeruginosa, and AST-N335 for other Gram-negative bacteria according to the needs of clinicians. Interpretation of drug susceptibility results were based on the Clinical Laboratory Standards Institute  standard.Positive bacterial cultures from patients’ respiratory tract and blood samples collected within the first 24 h of admission were tested for type of pathogens and sensitivity to antibiotics. The microorganisms were identified to species level by standard biochemical methods including matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) method  and automated methods with the VITEK system . The bacterial isolates were tested for antimicrobial susceptibility performed by VITEK 2 Compact  or the manual Kirby–Bauer (K–B) disk diffusion method. The selection of VITEK susceptibility card types  was as follows: AST-P639 for Gram-Positive bacteria, AST-GN09 for 2) or Fisher exact tests were performed to compare differences between groups using SPSS version 16.0 software . A P < 0.05 was considered statistically significant.Continuous values are expressed as mean ± SD. Microbial distribution and antibiotic susceptibility data were presented as percentages. The Chi-square (χP < 0.05 each). The most prevalent resistance patterns were methicillin-resistant coagulase-negative staphylococci (MRCNS) (11.73%) in the pediatric ward, carbapenem-resistant Acinetobacter baumannii (CRAB) (6.99%) in the adult respiratory ward, and CRAB (19.63%) and carbapenem-resistant Enterobacteriaceae (CRE) (12.26%) in the RICU.From 1 January, 2016 to 31 December, 2019, the frequencies of reportedly positive cultures of LRTI patients were 344 (detection rate: 15.67%) out of 2,195 respiratory tract cultures and 125 (2.11%) out of 5,926 blood cultures in the pediatric ward; 726 (23.04%) out of 3,151 respiratory tract cultures and 47 (3.75%) out of 1,253 blood cultures in the adult respiratory ward; and 2,734 (63.59%) out of 4,299 respiratory tract cultures and 185 (9.37%) out of 1,974 blood cultures in the RICU. The prevalence of positive cultures of LRTI patients for different wards was summarized in More respiratory tract and blood specimens of LRTI patients from three wards were performed in all seasons (January to December) than in cold seasons (October to December), as evidenced by 804  of monthly mean respiratory tract cultures and 763  of blood cultures in all seasons, and 949  and 799  in cold seasons, respectively .Staphylococcus aureus , Streptococcus pneumoniae , Klebsiella pneumoniae , Pseudomonas aeruginosa , and Haemophilus influenzae . Other pathogens included Escherichia coli at 6.40%, Moraxella catarrhalis at 3.78%, and Enterobacter cloacae at 2.91%.The profile of the pathogens isolated from LRTI patients’ respiratory tract specimens was summarized in n = 111, 88.80%) accounted for more than 80% in blood specimens of patients admitted to the pediatric ward, and Staphylococcus epidermidis  was the most common pathogen. Staphylococcus hominis  was the second most common bacterium, followed by Streptococcus agalactiae  and E. coli .The types and numbers of bacterial strains isolated from blood cultures were shown in P. aeruginosa  was the most common pathogen. Acinetobacter baumannii  was the second most common bacterium, followed by K. pneumoniae , S. aureus , and Stenotrophomonas maltophilia .Among the respiratory tract specimens of LRTI patients  admittedS. aureus , S. epidermidis , K. pneumoniae , and S. hominis  in the adult respiratory ward.The profiles of the microorganisms isolated from blood cultures were shown in n = 2,264) accounted for 82.81% of isolations, and the four most predominant pathogens were P. aeruginosa , A. baumannii , K. pneumoniae , and S. aureus .The types and numbers of bacterial strains of the RICU patients’ the respiratory tract cultures were shown in K. pneumoniae , followed by S. hominis , A. baumannii , S. epidermidis , and Staphylococcus capltis .In the RICU blood cultures , the domP < 0.05) and adult respiratory ward (59.1%) (P < 0.05). P. aeruginosa was the dominant pathogen in both the adult respiratory ward and RICU, whereas S. aureus was the most common bacterium in the pediatric ward. Fungal pathogens in the pediatric ward, adult respiratory ward and RICU were respectively at 15.41%, 27.96% and 11.59%, and Candida albicans was the most prevalent in all three wards.Among the respiratory tract cultures , Gram-neS. epidermidis) was higher than Gram-negative bacterial strains in both the pediatric ward and adult respiratory ward, and the proportion of Gram-positive bacteria isolated from LRTI patients was significantly higher in the pediatric ward than in the adult respiratory ward and RICU (P < 0.05). However, Gram-negative bacteria remained the major pathogens involved in LRTIs from the RICU, accounting for 48.11% (n = 89). Fungal pathogens in the pediatric ward, adult respiratory ward and RICU were at 0%, 4.26%, and 12.43%, respectively. Candida albicans and Candida parapsilosis were respectively the most prevalent.Among the blood cultures , the freP. aeruginosa, A. baumannii, and K. pneumoniae was selected as shown in P. aeruginosa of LRTI patients remained highly susceptible (>70%) to all tested antibiotics in pediatric ward. However, only high susceptibility to amikacin, tobramycin, gentamicin, and moderate susceptibility to piperacillin/tazobactam, cefepime, ceftazidime, levofloxacin, ciprofloxacin were observed in both the adult respiratory ward and RICU. Moreover, its antibiotic sensitivity to meropenem and imipenem was moderate in the adult respiratory ward, and mild (<30%) in the RICU. A. baumannii remained highly or moderately susceptible to tested antibiotics in the pediatric ward, whereas it exhibited relatively low drug-susceptibility to antibiotics except tigecycline in the respiratory ward and RICU. K. pneumoniae was highly susceptible to tigecycline of patients hospitalized with LRTIs in all three wards. Its drug-susceptibility to other antibiotics, such as piperacillin/tazobactam imipenem, meropenem, amikacin, tobramycin and trimethoprim/sulfamethoxaz was relatively high in both the pediatric ward and adult respiratory ward, but mildly susceptible to routine antibiotics in the RICU.Drug-susceptibility of Gram-negative S. aureus, S. epidermidis, and S. pneumoniae was selected as shown in S. aureus isolated from LRTI patients was highly susceptible (>70%) to linezolid, daptomycin, teicoplanin, vancomycin, tigecycline, rifampicin, and trimethoprim/sulfamethoxazole in all three wards, moderately susceptible to gentamicin in the adult respiratory ward and RICU and to clindamycin, oxacillin, moxifloxacin in the adult respiratory ward. However, its sensitivity to other antibiotics was relatively low in the RICU. S. epidermidis was one of the main microorganisms in positive blood cultures. It exhibited high drug-susceptibility to gentamicin, moxifloxacin, clindamycin, linezolid, daptomycin, teicoplanin, vancomycin, tigecycline, and rifampicin, moderate sensitivity to trimethoprim/sulfamethoxazole, and mild sensitivity to benzylpenicillin, oxacillin, and erythromycin among patients with LRTIs in the pediatric ward, adult respiratory ward, and RICU. S. pneumoniae was the second most common bacterium in the pediatric ward, and its sensitivity to routine antibiotics except trimethoprim/sulfamethoxazole was high in the pediatric ward and respiratory ward.The antibiotic sensitivity of Gram-positive Respiratory infections account for significant morbidity, mortality, and healthcare-related expenditure in patients admitted to hospitals worldwide. LRTIs are common bacterial infections leading to large antibiotic use and hospitalization, which account for 3 to 5% of deaths in adults, especially in patients over the age of 60 years . It is rP < 0.05), consistent with a previous report indicating that the elderly have an increased risk of developing LRTIs compared to young adults and nosocomial infections are very prevalent in patients admitted to ICU , similar to the findings in an Indian hospital where the isolation rates were different for different biologic specimens: sputum culture (30.92%), BALF (75%), and blood culture (12.5%) . Furtherd to ICU .S. aureus and S. pneumoniae were the predominant bacteria, followed by K. pneumoniae and H. influenzae in our pediatric ward. Similarly in Africa and Asia, it is revealed that the commonest bacterial pathogens of children hospitalized with LRTIs were S. aureus, H. influenza, and S. pneumoniae , while A. baumannii was the predominant Gram-negative bacterium in the RICU (33.88%)  was relatively common organism isolated in ICU with respiratory infections (P. aeruginosa (26–50%) was the most frequent bacterium in ICU followed by K. pneumoniae (15%) as well  . In the fections , and rec as well .Staphylococcus sp., as S. aureus attributed to 21.8% in the adult and S. epidermidis attributed to 47.20% in children, indicating that it is of vital importance to reduce staphylococcal infections in our hospital. S. epidermidis (53.1%) was the most common species in blood cultures of infants with infections in the neonatal internal wards and neonatal intensive care unit (NICU). Some researchers observed that the most common organism identified in blood cultures from pediatric LRTIs was S. pneumoniae 77% , followed by S. hominis (8.65%) and A. baumannii (7.57%). Similar to previous reports in China  and adult respiratory ward (61.70%) were significantly higher than Gram-negative bacterial strains. The dominant pathogens of two wards both belonged to niae 77%  to 78.5%niae 77% , 92% of niae 77% . Anothereumoniae . Howevernicillin . Gram-nein China  and Euroin China , the comP. aeruginosa (CRPA), carbapenem-resistant K. pneumoniae (CRKP), etc.] have been increasing in recent years. In order to achieve appropriate therapeutics, updated epidemiology of antimicrobial sensitivity is required to support therapeutic guidelines. A Chinese multicenter epidemiology survey of pneumonia revealed that the detection rates of CRAB and CRPA were 78.9 and 70.7% (P. aeruginosa of LRTI patients remained highly susceptible (>70%) to routine antibiotics in pediatric ward, but only had high susceptibility to amikacin, tobramycin, gentamicin in both the adult respiratory ward and RICU. Moreover, its antibiotic sensitivity to meropenem and imipenem was moderate in the adult respiratory ward, but mild (<30%) in the RICU. A. baumannii remained highly or moderately susceptible to tested antibiotics in the pediatric ward, whereas it exhibited relatively low drug-susceptibility to antibiotics except tigecycline in the respiratory ward and RICU. A. baumannii is highly resistant to antimicrobials especially in ICU and older patients  is 87.8% (S. pneumoniae is reported very sensitive to penicillin (S. pneumonia was the second most common bacterium in the pediatric ward, and its sensitivity to the antibiotics except trimethoprim/sulfamethoxazole was high in the pediatric ward and adult respiratory ward.Inappropriate application of antibiotics will result in antimicrobial resistance further increasing the health-care cost and mortality. Antibiotic resistance is a rapidly increasing global emergency that calls for action from all of society. The resistance patterns such as CRAB and CRE [including carbapenem-resistant nd 70.7% . In the patients , and antpatients . K. pneuospitals . The pheis 87.8% . As MRSAis 87.8% , it remanicillin . A similPseudomonas sp., Acinetobacter sp., and Klebsiella sp., differs across various countries or regions with highest rate in Asia, and least in North America and Western Europe (The rate of Gram-negative infections including n Europe . High prHowever, our research has certain limitations, partly because it was only focused on LRTI patients from one tertiary hospital in Beijing, which might have led to enrollment bias considering the geographic location. Viruses have been identified in many pneumonia episodes; however, ascribing pathogenicity may be difficult unless they are invariably associated with disease . These iThe distribution of prevalent microorganisms and their patterns of antibiotic susceptibility revealed a high divergence among LRTI patients admitted to the pediatric ward, adult respiratory ward, and RICU in our hospital. Thus, different antibiotic therapies for distinct age groups were suggested, and overuse of broad-spectrum antibiotics should be avoided.All datasets generated for this study are included in the article/supplementary material.The studies involving human participants were reviewed and approved by Institutional Ethics Committee of Peking University First Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.ND and JD contributed to conceptualization, methodology, formal analysis, investigation, and writing the original draft. CH contributed to validation and reviewing and editing the manuscript. HL was involved in conceptualization, reviewing and editing the manuscript, and supervision. All authors contributed to the article and approved the submitted version.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.


In our studies, Mg2+ bind collagen to promote the proliferation and differentiation of osteoblasts through the expression of integrins and downstream signaling pathways. In order to clarify the biological behavior effect of 10 mM Mg2+/Col I coating, we performed 3--2,5-diphenyltetrazolium bromide (MTT), alkaline phosphatase (ALP), 4′6-diamidino-2-phenylindole (DAPI), Alizarin red staining and Rhodamine B-isothiocyanate (RITC)-labeled phalloidin experiments and found that 10 mM Mg2+ group, Col I-coating group, 10 mM Mg2+/Col I-coating group, respectively, promoted the proliferation and differentiation of osteoblasts, especially 10 mM Mg2+/Col I-coating group. We detected the mRNA expression of osteogenic-related genes  and the protein expression of signaling pathway , these results indicated that 10 mM Mg2+/Col I coating play an critical role in up-regulating the MC3T3-E1 cells activity. The potential mechanisms of this specific performance may be through activating via integrin α2β1-FAK-ERK1/2 protein-coupled receptor pathway.Type I collagen (Col I) is a main component of extracellular matrix (ECM). Its safety, biocompatibility, hydrophilicity and pyrogen immunogenicity make it suitable for tissues engineering applications. Mg Extracellular matrix (ECM) protein plays an important role in tissues repair and replacement . Type I 2+ is involved in bone development by enhancing the attachment and differentiation of osteoblasts and accelerating mineralization to enhance bone healing. However, Mg2+ concentration plays a key role in regulating bone formation, which exhibits concentration-dependent behavior and bone regeneration was associated ..2+-media2+/Col I-coating play a critical role in up-regulating the MC3T3-E1 cells activity. Moreover, Mg2+ played a dual role in integrin α2β1-Col I to promote the biological behavior of MC3T3-E1 cells. The potential mechanisms of this specific performance may be through activating via integrin α2β1-FAK-ERK1/2 protein-coupled receptor pathway. However, the underlying osteogenesis mechanisms of Mg2+/Col I not fully understood. We need a further research on the degradation and osteogenesis mechanisms of magnesium-based composites.In summary, our study demonstrated that the 10 mM Mg


Diphenylcyclopropenone (DPC) is an organic chemical hapten which induces allergic contact dermatitis and is used in the treatment of warts, melanoma, and alopecia areata. This therapeutic setting therefore provided an opportunity to study T cell receptor (TCR) repertoire changes in response to hapten sensitization in humans. Repeated exposure to DPC induced highly dynamic transient expansions of a polyclonal diverse T cell population. The number of TCRs expanded early after sensitization varies between individuals and predicts the magnitude of the allergic reaction. The expanded TCRs show preferential TCR V and J gene usage and consist of clusters of TCRs with similar sequences, two characteristic features of antigen-driven responses. The expanded TCRs share subtle sequence motifs that can be captured using a dynamic Bayesian network. These observations suggest the response to DPC is mediated by a polyclonal population of T cells recognizing a small number of dominant antigens. Diphenylcyclopropenone (DPC) is an example of a hapten, a small organic compound that reacts with biological macromolecules including proteins to form immunogenic conjugates. It is a potent skin sensitizer  and, sinAs part of an extended project to understand more fully the immunological events leading to ACD, and hence generate better tools for chemical risk assessment , we haveWe carried out TCRseq on unfractionated blood samples from 29 patients with alopecia .Samples were collected before  and at one to three time points after sensitization with DPC , taken aThe major T cell sub-populations   were quaWe next looked for evidence of changes in individual TCR sequences following exposure to DPC. We plotted the abundance of each TCR before sensitization and after sensitization (at PT1)  in almost all individuals but the magnitude of the response varied significantly between individuals . InteresWe plotted the number of PT1 expanded TCRs (abundance ≥8) in individuals with varying strengths of sensitization as a function of the initial skin response recorded at the site of application  and the sensitization/patch test scores at all time points is summarized in We hypothesized that the population of TCRs that are found at increased frequency post-exposure to DPC may be enriched for T cells which bind to DPC, or DPC-modified peptides. Antigen-specific sets of TCRs frequently share sequence features, including skewed use of V and J regions, and similarities in CDR3 sequence . The relWe also looked for potential clustering of CDR3 sequences of the expanded TCRs . We haveTaken together, the skewing of V and J genes, and the presence of clusters of similar TCRs were strongly suggestive that the expanded TCRs were enriched for TCRs responding to a limited number of specific epitopes.The skewed V/J usage and the TCR clustering are indicative of an antigen-specific response. We therefore explored whether the repertoire of expanded TCRs was sufficiently distinct from that of the unselected repertoire so that it could be used to distinguish DPC-expanded TCRs or sets of TCRs from the unexpanded repertoire. We used a dynamic Bayesian network (DBN) , a modelWe constructed a DBN to classify TCR beta CDR3 sequences into one of two classes: DPC-related CDR3s and randomly sampled pre-sensitization CDR3s from the same individuals. To construct the training/test sets for the model, we used the PT1 expanded CDR3 sequences (≥8) for the DPC class, which were at most 22 amino acids long (2019 beta sequences from 22 patients), and for the control class, a randomly generated set of the same number of TCRs from the combined pre-sensitization sequences. CDR3 sequences in the expanded set were excluded from the pre-sensitization (control) training set. The length of CDR3 sequences used in the model was capped at 22 in order to manage the computational complexity of the algorithm, while still utilizing all expanded sequences. To account for the variable length of the CDR3 sequences, the sequences were aligned by the first Cysteine of the CDR3 on the left, up to the final Phenylalanine of the CDR3, then ‘completed’ to length 22 using a dummy variable (‘amino acid number 21’). The DBN took as input the amino acid in each position in the CDR3 (converted to an integer 0–21 for each amino acid or dummy), the V and J genes, as well as position-specific triplet scores and class scores . The proposed probabilistic dependencies between these sequence features and antigen specificity are encoded into the network structure by directed edges . FurtherOne of the key features of the DBN is that each ‘slice’ of the network is position specific. This enables modeling local as well as global sequence features. For example, The DBN classifier could classify the full DPC/control test sets with mean accuracy of 94% from 10 repeats of 10-fold cross validation, and sets of 10 sequences with mean accuracy 77.7%  since the number of expanded TCRs at the first time point post-sensitization is greater than the number of expanded TCRs observed in unsensitized individuals sampled at similar time points, for the great majority of the patients tested. The functional link between TCR expansion and DPC exposure is also strengthened by the observed statistical correlation between the number of expanded TCRs and the vigor of sensitization, as measured by the patch test. Moreover, the expanded set of TCRs show strong evidence of V and J gene skewing, and of increased CDR3 clustering, both well-established signatures of an antigen-specific T cell response . FinallyDPC is considered a potent sensitizer . NeverthIn our study, low responders were not boosted by repeated exposure to DPC over many months. In fact, the response was usually maximal at the first patch test, suggesting that regulatory mechanisms come into play to limit the response in vivo. Similarly the initial TCR expansion did not continue to increase despite repeated exposure to antigen, and indeed the majority of TCRs showed very transitory responses, with individual TCR frequencies falling to around pre-sensitization frequencies by the next time point. Similar rapid expansion and subsequent contraction have been observed in response to live attenuated yellow fever vaccine , one of In conclusion, this study is the first analysis of in vivo TCR repertoire changes in response to a chemical allergen, and is one of only a handful of studies that document in vivo longitudinal changes in TCRrep in response to immunization in humans. We cannot rule out that the immune response in this population is different from healthy individuals, since all patients suffered from some form of alopecia, which is believed to have an autoimmune etiology . NeverthA total of 34 patients were recruited to this study (NRES Ethics Committee East of England – Cambridgeshire and Hertfordshire [14/EE/1067]). Participants were recruited from patients who had been diagnosed with alopecia, were aged between 18 and 70, identified as suitable for DPC treatment by a consultant dermatologist, and were now attending their first visit to the Alopecia Clinic at Salford Royal Hospital for DPC therapy. This study ran alongside patients’ prescribed DPC treatment . The study timeline in terms of treatment and sample collection is provided in Twenty-nine of the individuals who participated in the study provided blood samples for TCR sequencing (TCRseq), for between one and four of the study time points . Flow cytometry data was obtained for peripheral blood mononuclear cells (PBMCs) from 10 treated patients, and patch test data for 24 patients. The clinical response to treatment (in terms of hair regrowth) and the associated immunological changes will be discussed in a separate publication. A summary of the patient demographics and the samples collected is shown in As controls for the TCR sequencing, five healthy volunteers were bled three times, at day 0, 2 weeks, and 6 weeks. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the University College London Hospital Ethics Committee 06/Q0502/92.Sensitization to DPC was induced by application of 2% DPC (in acetone) to a 2 cm by 2 cm patch of skin on the upper inner arm. Sensitization at the site of application was assessed 14 days later and scored as described below (sensitization score). Patch testing at a remote site (upper back) was conducted 2 weeks (PT1), 6 weeks (PT2), and 24 weeks (PT3) after application of the sensitizing dose of DPC. Patch testing was performed on the skin of the upper back using Finn chambers (8 mm inner diameter) containing 0.01% DPC in acetone. After 6 days, patients’ reactions were scored as no reaction (−), weakly sensitized (+), strongly sensitized (++), or extremely sensitized (+++) according to standards set by the International Contact Dermatitis Research Group.The α and β chains of the TCR repertoire of 29 participants were sequenced using a method that starts with total RNA isolated from unfractionated whole blood, collected in Tempus Blood RNA tubes (Thermofisher #4342792) using the manufacturer’s protocol for RNA extraction. The pipeline introduces unique molecular identifiers attached to individual cDNA molecules to provide a quantitative and reproducible method of library preparation. Full details for both the experimental TCRseq library preparation and the subsequent computational analysis  using Decombinator are published in 7 cells/mL in 10% DMSO/90% human AB serum (Sigma Aldrich) at −80°C until required.PBMCs were isolated from 30 mL whole blood (diluted 1:1 in PBS) from 10 patients layered over an equal volume of Histopaque 1077 (Sigma Aldrich) according to the manufacturer's instructions. Cells were stored at a concentration of 5 × 10Flow cytometric analyses were performed on previously frozen cells using antibodies obtained from eBioscience, with single-stained controls used for compensation. Naïve and memory CD4+ and CD8+ T-cell subsets were identified as previously described . Naïve cStatistical analyses were performed using the statistical programming language R [R version 4.0.2 (2020-06-22)]. Mann–Whitney U-tests were used to compare between two unmatched groups and paired t-tests or Wilcoxon signed-rank tests between two paired groups. All statistical comparisons between more than two groups were done using Kruskal–Wallis non-parametric tests with post hoc Dunn Test and Benjamini–Hochberg correction for multiple testing. Statistical significance in all tests was accepted above the 0.05 threshold.To calculate which V and J genes were significantly over- or under-expressed in the DPC-expanded TCR sets, at both the individual patient level and for the entire expanded set, we generated 2*1000 independent random samples of equal-sized sets from the pre-sensitized individual/combined population, calculated the ratio between the two, and compared this with the ratio derived from the set of interest against a further 1000 random simulations. A significance level of 0.05 was then achieved for genes that ranked in either the top 50 ‘ratios’ (under-represented) or bottom 50 in the list (over-represented).https://github.com/innate2adaptive/Decombinator). The pairwise similarity between TCRs was measured on the basis of amino acid triplet sharing, which was calculated using the normalized string kernel function stringdot  from the Kernlab package (https://weblogo.berkeley.edu/logo.cgi).The CDR3 protein sequences of expanded TCRs were identified using the package CDR3translator X is the i’th node in time slice t, and t(i))Pa(X are the parents of t(i)X. The TCR DBN was built in MATLAB (2019b), using the Bayes Net Toolbox . Class scores for the test sets were calculated based on gene, amino acid, and triplet frequencies in the training sets. Nodes depicting specificity (DPC or control) were included as hidden (latent) variables  of the system  Toolbox . The DBN In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This manuscript presents a longitudinal analysis of the whole blood Tcell repertoire dynamics in patients with Alopecia Areata upon primary, secondary and chronic exposures to an allergic chemical agent. The authors have identified signatures of polyclonal T-cell response following sensitization involving stereotypical T-cell receptor genes. Aside from the specific case of T-cell response to the allergic agent in this study, the developed methods could be used for the general and important problem of identifying reactive Tcell populations to antigenic challenges.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Aleksandra Walczak as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Yuval Elhanati (Reviewer #2).Thank you for submitting your article "The clonal structure and dynamics of the human T cell response to an organic chemical hapten" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.This manuscript presents a longitudinal analysis of the whole blood TCR repertoire dynamics in patients with Alopecia Areata upon primary, secondary and chronic exposure to DPC treatment. The authors have identified signatures of polyclonal T-cell response following sensitization involving stereotypical T-cell receptor genes. Using a Dynamic Bayesian Network, the authors characterize a sequence based classifier that with a high accuracy can predict sensitization based on TCR sequence. Data collection and analysis are performed carefully and the results are presented clearly, but there are still some concerns that we would like to see addressed.1) Most of the analysis rely on recognizing the expanded clones. For this purpose TCRs were counted based on their fold change – 2, 4, 8 and 16. However, the fold change can be very sensitive to the sample size at each time point – smaller samples tend to have lower diversity, which translates into bigger clone frequencies. This bias in particular, is present for TCR clones with low multiplicities . For example, consider two replicate blood samples such that one is sequenced ten times deeper than the other. A lot of singletons (TCRs with count 1) should be present in the deep sample, whereas almost all of them should be absent in the other sample. To estimate frequencies however, authors adjust these absent counts from 0 to 1 and divide the adjusted counts by the total counts in each sample. As a result, these fluctuations due to sample depth would appear as 10-fold difference in frequency of the singleton clones between samples.– To gauge the validity of the analysis, it would be insightful if authors show more basic statistics for the fold change in clone sizes: what are pre-sensitization frequency and counts for expanding clones? Are there a lot of clonotypes that grow from 0 to 8 counts? What fraction of pre-sensitization high-frequency clones expand? One way to show this is to make scatterplots for clone counts in PS vs counts in PT1, ideally in log-scale; see DeWitt et al., 2015 Figure 2 as an example.– Given the concerns above, it is also necessary that authors provide a table with details on sample size in data (e.g. unique clonotypes and UMIs for each sequenced sample) and show that the results are robust to these differences. For example, do observed differences in Figure 2 hold, if all time points are down-sampled to the same number of UMI per sample?2) A more detailed description of the methods for analysis of clone size dynamics is necessary: Are clonal frequencies estimated after adding 1 to zero counts or are missing clones assigned a minimum frequency in a given sample (see the example in comment 1)?3) From the comparison of Figure 2 and Supplementary file 2 it seems that expansion analyses in α and β repertoires do not quite agree. In particular, for the α chain, there are no significant differences between control and patient groups. If there are strong clonal expansions, one expects a correlated fold increase in α and β chains that belong to the same clone. Can authors explain this discrepancy? Is there a correlation between the number of expanded clones in α and the β repertoires?4) Presentation of Figure 3 should be improved. For examples, clonal dynamics can be shown by connected lines over time. For comparison, it would be useful to show similar statistics in healthy individuals.5) Figure 4 is difficult to read, specifically the left panels in C, D and E. These plots highlight the dependence of the number of expanded TCRs on the expansion fold change, which is rather trivial and not the point the authors are making here. On the other hand, the differences between the different sensitization groups are difficult to see. A different visualization should be considered.6) Figure 5: Although V/J usage can reflect a response to DPC, the authors should first analyze whether the V/J usage is patient dependent or treatment dependent. For instance, this can be done by performing the same analysis on all clonotypes from patients versus healthy volunteers, expanded clonotypes in healthy individuals versus 1000 random clones in healthy individuals, etc… In addition, the use of box-plot is not readable for all the V and J. Better summarizing the information as heatmap.7) For DBN, it is not clear from the Materials and methods how the sequences are aligned since TCRs have variable length.8) To train a DBN, the authors construct the negative set from TCRs of healthy donors which were not in the expanded positive set. A natural negative set would be the patient-specific TCRs that were present in the pre-sensitized sample but did not expand. Authors should check how their results are robust to this choice of negative set.Also, TCRs are somewhat individual-specific and so using a negative set from different donors might be problematic and create an "easy" task for classification. This can be fixed by using donor specific training sets.What is the ratio of positive to negative set? Again, how robust is the performance of DBN to this training ratio. Authors should provide access to their training and test sets as a separate supplementary file.9) Can authors discuss in more detail the TCR features that are informative for DPC classification. Do the authors expect same classifier performance for other tasks , or high accuracy of DBN is limited to the special case of DPC-recognizing TCRs? This manuscript presents a longitudinal analysis of the whole blood TCR repertoire dynamics in patients with Alopecia Areata upon primary, secondary and chronic exposure to DPC treatment. The authors have identified signatures of polyclonal T-cell response following sensitization involving stereotypical T-cell receptor genes. Using a Dynamic Bayesian Network, the authors characterize a sequence based classifier that with a high accuracy can predict sensitization based on TCR sequence. Data collection and analysis are performed carefully and the results are presented clearly, but there are still some concerns that we would like to see addressed.1) Most of the analysis rely on recognizing the expanded clones. For this purpose TCRs were counted based on their fold change – 2, 4, 8 and 16. However, the fold change can be very sensitive to the sample size at each time point – smaller samples tend to have lower diversity, which translates into bigger clone frequencies. This bias in particular, is present for TCR clones with low multiplicities . For example, consider two replicate blood samples such that one is sequenced ten times deeper than the other. A lot of singletons (TCRs with count 1) should be present in the deep sample, whereas almost all of them should be absent in the other sample. To estimate frequencies however, authors adjust these absent counts from 0 to 1 and divide the adjusted counts by the total counts in each sample. As a result, these fluctuations due to sample depth would appear as 10-fold difference in frequency of the singleton clones between samples.We agree with this very important point. Indeed, the estimation of ratios is fraught with potential confounders of the type the reviewers raise. On the basis of the suggestions below, we have re-examined the raw data (what the reviewers refer to as “basic statistics”). We note  that actually almost all the repertoire activity we observe following sensitisation is focused on TCRs ABSENT pre-sensitisation. Rather than calculate frequencies, and impute missing values, we therefore now focus on those TCRs absent at PS, and expanded at PT1. For comparison, we also analyse the complementary group – those absent at PT1, and expanded at PS. In this way, we do not have to impute any missing values, do not have to calculate fold expansion, but can focus directly on the proportion of TCRs present at an expanded threshold at a particular timepoint. In all quantitative analyses we subsample all the repertoires to the same number of TCRs (defined by unique UMIs), so as to add additional robustness to the analyses.– To gauge the validity of the analysis, it would be insightful if authors show more basic statistics for the fold change in clone sizes: what are pre-sensitization frequency and counts for expanding clones? Are there a lot of clonotypes that grow from 0 to 8 counts? What fraction of pre-sensitization high-frequency clones expand? One way to show this is to make scatterplots for clone counts in PS vs counts in PT1, ideally in log-scale; see DeWitt et al., 2015, Figure 2 as an example.We thank the reviewer for this excellent suggestion. This way of visualisation is indeed very instructive and these plots are included in Figure 2, and in Supplementary file 3.– Given the concerns above, it is also necessary that authors provide a table with details on sample size in data (e.g. unique clonotypes and UMIs for each sequenced sample) and show that the results are robust to these differences. For example, do observed differences in Figure 2 hold, if all time points are down-sampled to the same number of UMI per sample?We include unique TCRs and total UMIs for each sample in Supplementary file 1. We show that the proportion of expanded TCRs is not correlated to the number of UMIs per sample. We also carry out repeat subsampling of the data to the lowest number of UMIs and calculate the correlation between expansion and patch score. The qualitative picture is the same, although as discussed in more detail in the manuscript, the expansion threshold at which the mean correlation becomes statistically significant changes a little bit.2) A more detailed description of the methods for analysis of clone size dynamics is necessary: Are clonal frequencies estimated after adding 1 to zero counts or are missing clones assigned a minimum frequency in a given sample (see the example in comment 1)?This issue is no longer relevant since, as discussed above, we do not use fold change any more, and do not have to assign values to missing points.3) From the comparison of Figure 2 and Supplementary file 2 it seems that expansion analyses in α and β repertoires do not quite agree. In particular, for the α chain, there are no significant differences between control and patient groups. If there are strong clonal expansions, one expects a correlated fold increase in α and β chains that belong to the same clone. Can authors explain this discrepancy? Is there a correlation between the number of expanded clones in α and the β repertoires?Expanded α and β TCR numbers are indeed closely correlated  (now included in Supplementary file 3). Occasionally there are small differences in statistics for α and β; we presume this reflects sampling effects or some other technical variation between samples.4) Presentation of Figure 3 should be improved. For examples, clonal dynamics can be shown by connected lines over time. For comparison, it would be useful to show similar statistics in healthy individuals.We have redrawn these clonal dynamics as lines and included healthy volunteers.5) Figure 4 is difficult to read, specifically the left panels in C, D and E. These plots highlight the dependence of the number of expanded TCRs on the expansion fold change, which is rather trivial and not the point the authors are making here. On the other hand, the differences between the different sensitization groups are difficult to see. A different visualization should be considered.We have completely redrawn this figure, focusing on the correlation between number of expanded TCRs and sensitization scores. We believe the results are now clear.6) Figure 5: Although V/J usage can reflect a response to DPC, the authors should first analyze whether the V/J usage is patient dependent or treatment dependent. For instance, this can be done by performing the same analysis on all clonotypes from patients versus healthy volunteers, expanded clonotypes in healthy individuals versus 1000 random clones in healthy individuals, etc… In addition, the use of box-plot is not readable for all the V and J. Better summarizing the information as heatmap.We agree with the reviewers that V/J usage may be patient dependent. For this reason, we have compared the expanded set of each individual against unexpanded sets from the same individual. This shows that certain genes are over-represented in the expanded TCRs compared to background for the individual. We have rearranged the plots to make them more readable.7) For DBN, it is not clear from the Materials and methods how the sequences are aligned since TCRs have variable length.We have added an explanation of how the sequences are aligned.8) To train a DBN, the authors construct the negative set from TCRs of healthy donors which were not in the expanded positive set. A natural negative set would be the patient-specific TCRs that were present in the pre-sensitized sample but did not expand. Authors should check how their results are robust to this choice of negative set.Also, TCRs are somewhat individual-specific and so using a negative set from different donors might be problematic and create an "easy" task for classification. This can be fixed by using donor specific training sets.We have changed the control set to TCRs from the pre-sensitization repertoires of the same donors as the expanded set. As suggested by the reviewer, this is indeed a more difficult classification task, and we have found that the DBN is very good at telling individuals apart !!! With the new control set, classifying individual sequences is somewhat harder for the DBN, but it is still very successful at classifying sets of sequences. We have included both the results for sets and single sequences in the new Figure 7C.What is the ratio of positive to negative set? Again, how robust is the performance of DBN to this training ratio. Authors should provide access to their training and test sets as a separate supplementary file.The positive and negative sets are the same size, and running the model with shuffled class labels produces around 50% accuracy. We have added this to Materials and methods. The positive and negative sets are submitted as Supplementary files.9) Can authors discuss in more detail the TCR features that are informative for DPC classification. Do the authors expect same classifier performance for other tasks , or high accuracy of DBN is limited to the special case of DPC-recognizing TCRs?We are writing a manuscript fully focused on the DBN and we would prefer to leave the discussion of other data sets etc. to this other manuscript, as it is not really relevant to this manuscript.


Similar to broader health disparities, Latinx adolescents have higher rates of high-risk sexual behavior resulting in pregnancy rates that are 2 times higher and sexually transmitted infection rates that are 5 to 8 times higher than non-Hispanic, white adolescents. Novel approaches are needed to reduce high-risk sexual behavior among Spanish-speaking Latinx adolescents who represent the fastest-growing group of US immigrants.This study aimed to partner with Spanish-speaking Latinx adolescents in a participatory design process to develop and test a Spanish-language video game intervention to decrease high-risk heterosexual behavior.This is an iterative, two-phase, mixed methods study. In phase 1, we conducted focus groups with Spanish-speaking Latinx adolescents to elicit feedback on the content and format of an existing English-language video game. Feedback was then incorporated into an expanded and culturally adapted Spanish-language video game. In phase 2, we pilot tested the feasibility, acceptability, and preliminary efficacy of the new Spanish-language video game intervention by measuring known antecedents to sexual behavior  assessed at enrollment and 12-week follow-up. We applied a thematic analysis to examine focus group feedback and a bivariate analysis to analyze pre- and postquantitative data.P=.007). Other variables of sexual behavior antecedents did not differ significantly between baseline and 12-week follow-up.In phase 1, 15 Spanish-speaking Latinx adolescents provided feedback for further video game development. A Spanish-language video game was then produced and tested in phase 2. We recruited and enrolled 24 Spanish-speaking Latinx adolescents aged 15 to 17 years. Participants played the video game for an average of 4.2 hours during monitored sessions. Pilot testing demonstrated feasibility and acceptability; 65% (3/20) of participants stated that they would play it again, and 65% (3/20) said they would recommend it to friends. Condom-specific knowledge did significantly increase between baseline and follow-up (An iterative participatory design process in partnership with Spanish-speaking adolescents produced an innovative and acceptable Spanish-language video game intervention aimed at decreasing high-risk sexual behavior in adolescents. Pilot testing demonstrated preliminary feasibility and yielded essential information for further video game development. Adolescents are susceptible to high-risk sexual behaviors, such as having vaginal intercourse without a condom, multiple sexual partners, and intercourse under the influence of drugs and alcohol. Such behaviors expose individuals to an increased risk of sexually transmitted infections (STIs) and unintended pregnancy . HoweverLatino and Hispanic are often used interchangeably. The US Census defines “Hispanic or Latino as a person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race” [Understanding the experiences of the US Latinx adolescent community is vital, given that Latinx adolescents currently contribute to the fastest-growing group of immigrants in the United States . Accordiof race” . Neitherof race” . In thisof race” . Millennof race” . Given tExisting public health efforts to decrease high-risk sexual behavior among adolescents are informed by an epidemiologic and cultural understanding of risk factors. Conventional interventions are often delivered within school-based curricula or self-directed learning modules. Recently, there has been a pressing need to diversify approaches aimed at decreasing adolescent high-risk sexual behavior and a call for creative solutions ,9. TechnWe designed a 2-phase pilot study in direct partnership with Spanish-speaking Latinx adolescents. In phase 1, we conducted focus groups with Spanish-speaking Latinx adolescents to elicit feedback on the existing English-language video game  and emplintention to engage in a specific behavior predicts that behavior. The Health Belief Model stresses the importance of individuals’ self-efficacy to accomplish a given behavior, perceptions of susceptibility to a given health problem, and knowledge of and beliefs in the effectiveness of an intervention. The Social Cognitive Theory proposes that knowledge is insufficient to change behavior and emphasizes that observational learning, such as seeing modeled behavior and its consequences or, more importantly, providing individuals with the opportunity to practice skills (as can occur in video game simulation), may enhance behavioral change. An adolescent brain development theory, namely emotional and social self-efficacy [We conducted a pilot study with a mixed methods design that built on an existing English-language video game prototype utilizing a theory-driven framework  to targeefficacy  and impuefficacy , was alsefficacy -38, infoThe existing English-language video game previously was developed and tested with black or Latinx adolescents aged 15 to 17 years, who suggested that the video game setting be a party scene populated by avatars  . The plaThis study was conducted in 2 phases: a formative phase (phase 1) and a testing phase (phase 2). During phase 1, we conducted focus groups with Spanish-speaking (English-proficient) Latinx adolescents to modify and shape the existing English-language video game with maximum input from our target audience. Then the video game was adapted and modified in response to participants’ feedback and suggestions. In phase 2, we evaluated the following aspects of feasibility and acceptability of the gameplay experience: usability, engagement, satisfaction, game completion, and preliminary efficacy.Type of video game: role-playing, simulation, educationalPlayer’s objective: Become a sex expert (sexpert). Unlock information and answer trivia to gain points, accumulate avatars, and reach higher video game levelsVideo game design:PartyGame starts at a party where player controls multiple avatars , conversing, making decisionsPlayer guided to unlock key information about sex and answer trivia questions, both of which earn pointsPlayer makes decisions for avatars regarding alcohol/marijuana use, sex, using a condom and/or effective contraceptionIf player uses alcohol or marijuana, a condom and/or effective contraception may not be availableParty resultsFor every avatar that was sexually active, player spins a wheel to view the consequences of decisions, including possible pregnancy Player is presented with a choice to go to a clinic for sexually transmitted infection (STI) testing and/or to discuss and choose contraceptionAfter receiving test results, the player is led to a conversation with a sexual partner to discuss results and any concernsAdvancementAdvance to next level, gain more avatars, have options to get STI testing, and obtain condoms and/or contraception before the next partyPresented with new scenarios and choices at the next level, as mentioned earlierVideo game content: Known antecedents to sexual behavior including intentions, self-efficacy, risk perceptions and susceptibility, and knowledgeComponents targeting behavior change:Skill building via simulations of difficult discussions about sex and decision making in scenarios like real lifeModeling short- and long-term consequences of decisions Incentivization by earning points within the video gameTransferrable skills:Communication: discuss whether to have sex, sex refusal, contraception negotiation, and HIV/STIs and pregnancy testing and resultsEvaluating risk: how the use of alcohol and marijuana can influence sexual risk takingDecision making: communication, take pregnancy tests, STI testing and treatment, and condoms and contraceptionProblem solving: where and how to obtain pregnancy tests, STI testing and treatment, and condoms and contraceptionWe partnered with a racially, ethnically, and socioeconomically diverse public high school in New Haven, Connecticut. Participants were recruited through flyers at the school and during presentations in English as a second language classes. Enrollment criteria for both phases included female or male adolescents who identified as Latino or Hispanic, aged 15 to 17 years, fluent in Spanish, and proficient in English. There were no enrollment criteria related to prior sexual experience. In phase 1, only nonidentifying demographic information was collected. Parents were notified of their adolescents’ desire to participate in the study and were given the chance to decline participation on behalf of their children. Verbal assent from adolescents was required for participation. In phase 2, potentially identifying demographic information was collected, and both parental and adolescent written consent was required for participation. The study protocol was approved by the Yale University Human Investigation Committee and was granted a certificate of confidentiality from the Department of Health and Human Services to guarantee the protection of participants’ privacy.Bilingual moderators trained in conducting focus groups facilitated all phase 1 focus group sessions, which lasted approximately 75 min each. Focus groups were conducted separately by those of self-identified same gender to maximize comfort, minimize self-consciousness, and encourage open discussion . First, Adolescents participating in phase 2 played the video game weekly at monitored 1-hour sessions for a total of 6 weeks. Video gameplay was done individually on personal tablets. Participants were encouraged not to talk or interact during video gameplay time and were monitored to ensure individual, independent gameplay. Tablet devices were numbered, and participants were given the same tablet each time they played. Self-administered, paper survey assessments were given at 3 time points: upon phase 2 enrollment, immediately after the last gameplay session, and at 12-week follow-up. We collected baseline demographic information at the time of enrollment. Given the short time frame of the study period and small sample size of the pilot study, preliminary efficacy of the video game was assessed with variables known to be antecedents of high-risk sexual behavior including intentions, self-efficacy, risk perceptions, and sexual knowledge. These were assessed at the time of phase 2 enrollment, immediately after the last gameplay session, and at 12-week follow-up. We also collected data on actual sexual behaviors such as frequency of intercourse, condom use, contraception use, number of partners, intercourse under the use of drugs and alcohol, and diagnoses of STIs and pregnancy at enrollment and follow-up. Survey questions came from validated tools, including the CDC Youth Risk Behavior Surveillance, the safer sex intention scale, and self-efficacy for negotiating condom use -44.Video game acceptability and feasibility were assessed with qualitative and quantitative data. Qualitative feedback was collected during focus groups after the completion of gameplay. A total of 4 focus groups were conducted by the same bilingual moderators as in phase 1 and were audio-recorded, translated, transcribed, and analyzed using a thematic analysis to identify key concepts. Quantitative data on gameplay acceptability and feasibility, including how long each participant played for, maximum game level reached, and the number of sessions attended were collected as part of the survey following the completion of gameplay. Additional quantitative survey data were collected following the completion of gameplay and 12 weeks after exposure to the video game to assess video game visual acceptability, like/dislike of the video game, connection to the avatars, frustrations with the video game, and desire to play the video game again or recommend to friends.true/false/not sure answers, and the total percentage correct was reported. Baseline and 12-week follow-up assessment scores were compared with a Student t test, and P values were calculated.We assessed demographics and participant characteristics using descriptive statistics. The preliminary efficacy of the video game was assessed by examining mean summary scores for intentions, self-efficacy, risk perception, and knowledge. Individual questions were measured using 5-point Likert scales where 4=I strongly agree, 3=I agree, 2=I am not sure, 1=I disagree, and 0=I strongly disagree. The statements were worded in the affirmative. For example, “If I have sex without contraception (something to prevent pregnancy), I would probably get pregnant (or get someone pregnant).” Higher scores indicated an improvement in the respective categories; 1 question was reverse coded. Mean summary scores were calculated for the variables measuring the preliminary efficacy of the video game within the following categories: intentions, self-efficacy, risk perception, and knowledge. Mean summary scores for intention (0-64), self-efficacy (0-44), and risk perception (0-44) were cumulative, based on Likert values 0 to 4 for individual questions. Mean knowledge scores (0%-100%) were calculated as the percentage correct. We calculated the Cronbach α for each scale to examine internal consistency. Cronbach α scores ranged from .70 to .80, demonstrating good overall reliability. Knowledge questions were asked with do-overs, and the chance to answer trivia questions, which allowed them to test their knowledge and gain new information. Participants recommended modifying the existing video game, as opposed to developing a new game. Key suggestions to generally improve the English-language video game included adding content to increase gameplay time, decreasing the length and overall amount of text, adding more animation to avatars, and increasing the number of video game background settings . One major Latinx-specific theme that emerged and that teens recommended to be incorporated into the video game was the importance of involving family members and close friends in the decision-making process around dating, sex, contraception, and pregnancy. This underlines the importance of familismo, the loyalty that Latinx individuals often feel toward extended family over personal needs that is present in Latinx culture [fatalismo, the belief that individuals cannot control when or if pregnancy occurs (even if contraception is used) because it is part of their destiny regarding pregnancy [A total of 15 Spanish-speaking Latinx adolescents participated in 4 phase 1 focus groups, each consisting of 4 to 6 participants. Out of 15 participants, 7 (47%) were female, and 12 (80%) participants were born outside of the continental US. After 30 min of gameplay, participants overwhelmingly liked the premise of the video game, including the process of simulated decision making using avatars, virtually experiencing consequences of those decisions, getting feedback on the decisions, having opportunities for  culture . Particiregnancy , and theColegio del Sexperto, which means Sexpert High School. The goal of the 12-level video game is to become a sex expert (sexpert), which players do by working through multiple simulated scenarios to practice decision making around sexual behavior and experience consequences of those decisions, all while accumulating knowledge  of those decisions. Upon completion of each level, players have simulated conversations in which they disclose STI and/or pregnancy test results to sexual partners, friends, and family members. They are given opportunities to seek (or decline) treatment for STIs and to obtain condoms and other forms of contraception before starting the next party.Through a se party . Each lePlayer-guided avatars also answer trivia questions that address the main thematic learning objectives of the video game . Players gain points by answering trivia questions correctly, and a minimum of trivia questions must be answered correctly to obtain enough points to advance to the next level.In phase 2, we recruited and enrolled 24 Spanish-speaking Latinx adolescents. The average age of participants was 15.8 years (range 15-17), and there were equal numbers of male and female participants . Out of On average, participants played 4.2 hours of the video game over the 6-week time period. Out of 24 participants, 14 (58%) reached level 5, and 2 (8%) participants successfully completed all 12 levels of the video game. At the 12-week follow-up, 65% (13/20) participants reported that they enjoyed playing the video game, would play it again, and would tell their friends to play the video game (see the table in P=.12). On the basis of the finding that most players  did not get past level 5 of the video game, we explored how content was distributed throughout the video game. An in-depth review revealed that condom-specific information, although a central focus of the video game overall, was heavily represented in the first 3 levels of the video game, and 92% (22/24) participants played through the levels in which condom-specific information was addressed. Therefore, we performed a subanalysis of condom-specific questions. The mean summary scores for condom-specific measures were compared between baseline and 12-week follow-up. Condom-specific knowledge scores significantly increased from 29.6% to 47.4% correct  of participants reported any previous sexual experience at baseline (compared with 48.6% of Hispanic high school students nationwide) . TestingOur findings support a growing body of evidence showing that serious games or video games designed for health-related topics have the potential to influence sexual health and behaviors, ,51 and tAs is the nature of a pilot study, our study is limited by its small sample size, lack of control group, and relatively short follow-up interval. Recruitment criteria did not include prior sexual experience; this allowed for a larger recruitment pool but potentially limited our ability to reach an audience that might benefit most from a video game–based intervention. Although our study did include a certificate of confidentiality to guarantee the protection of privacy, the contemporary political climate’s anti-immigrant rhetoric may have contributed to distrust among our target audience of Spanish-speaking Latinx adolescents, which likely hindered the study recruitment and participant retention. Teachers who assisted with participant recruitment noted that some otherwise eligible adolescents expressed hesitancy in joining a research study because they or their family members were undocumented immigrants. Similarly, we had difficulty maintaining ongoing contact with our participants because of the reluctance to share contact information and conflicts between our testing period and the school holiday schedule. As a result, only a small number of participants ultimately completed the immediate postgameplay survey, which limited our ability to compare scores between time points and our analysis of preliminary efficacy. Consistent assurance of anonymity is crucial as are alternative ways of contacting participants . Future studies should take special care to create strong partnerships with community members to garner trust and acceptance, especially when working with vulnerable populations. Finally, opportunities exist to improve the mechanics of the video game to decrease the number of levels and make it less challenging, which could increase exposure to the game content. Larger clinical trials are needed to test the intervention against a control group before promoting this intervention on a larger scale.Through an iterative intervention design in partnership with our target audience, we developed an innovative and usable Spanish-language video game aimed at decreasing high-risk heterosexual behavior in Latinx adolescents. Findings from this pilot study support the growing body of evidence that serious video games may be used to both increase knowledge and modify behaviors. Our study addresses current gaps in research on culturally sensitive games for health . Pilot t