This paper proposes a self-learning Monte Carlo tree search algorithm (SL-MCTS), which has the ability to continuously improve its problem-solving ability in single-player scenarios. SL-MCTS combines the MCTS algorithm with a two-branch neural network (PV-Network). The MCTS architecture can balance the search for exploration and exploitation. PV-Network replaces the rollout process of MCTS and predicts the promising search direction and the value of nodes, which increases the MCTS convergence speed and search efficiency. The paper proposes an effective method to assess the trajectory of the current model during the self-learning process by comparing the performance of the current model with that of its best-performing historical model. Additionally, this method can encourage SL-MCTS to generate optimal solutions during the self-learning process. We evaluate the performance of SL-MCTS on the robot path planning scenario. The experimental results show that the performance of SL-MCTS is far superior to the traditional MCTS and single-player MCTS algorithms in terms of path quality and time consumption, especially its time consumption is half less than that of the traditional MCTS algorithms. SL-MCTS also performs comparably to other iterative-based search algorithms designed specifically for path planning tasks. Path planning is a critical problem in logistics and robotics and has been further applied to many areas  is an iterative approach which executes random sampling in the simulation and collects action statistics to enable educated choice in subsequent iterations. Since the number of simulations in each iteration can be considered the number of agents searching in the state space, it is also regarded as a collective intelligence algorithm , and the white grids represent free space (areas where the robot can move). To identify obstacles, white grid cells are represented by 0, whereas back grid units are represented by 1.This paper utilizes the grid model to form the robot's working environment for path planning tasks. As shown in X, where XO denotes the set of obstacles and XE contains all the feasible. The origin and destination are respectively denoted as ms and md. The relationship between all feasible nodes (XE) is denoted as G = , where M ∈ XE and E is the edges to neighbor nodes of M. AE(m) = {m′∣ ∈ E, m≠m′} represents all feasible neighbors of node m. N(AE(m)) is the number of the feasible nodes of m. The cost of each edge is recorded as 1. Therefore the path planning can be described as an agent starting from position xs at time step t0 to position xd. At time step t1, the agent selects action a1 and moves to the next state s1. After T steps, the agent reaches the position xd in ST. The sequential solution is path = , a0 = xs, aT = xd, at ∈ AE(at−1)), and the path length is St. The neural network predicts the state value vt and the selection probabilities pt for each state St. The choice of action at + 1 is together determined by vt and pt, executing action at + 1 and transferring to the next state St + 1. This process continues until the agent reaches the end.We model the search process of path planning as a Markov Decision Process (MDP). The process can be described as shown in To explicitly compare the differences between the framework of traditional MCTS and that of SL-MCTS, we describe the flow of the traditional MCTS algorithm in this part. For the family of traditional MCTS algorithms, their steps are similar.Two fundamental concepts guide the search process of MCTS algorithms: (1) the true value of an action can be approached by a large number of stochastic simulations; (2) these values can be effectively used to adjust the policy to the best-preferred strategy. MCTS builds a search tree to estimate the values of the moves. These estimates  become more and more accurate as the iterative search increases. Generally, the basic MCTS algorithm has four main processes . The selection probability p of each node is output after the search (line 10). At the end of the task, the solution score z is evaluated by comparing it with the optimal historical model (line 13), which means the quality of paths. The training process is shown in lines 15–20. In the training process, the parameters of PV-Network are updated, which makes the select probability p and state value v closer to the search probability π and path quality score z of previous SL-MCTS (line 16). Finally, these new network parameters are used in the next iteration of self-learning to make the search direction of SL-MCTS more accurate. The map and historical path information are fused as input state St. The selection probability p is a vector. It enables the quick search process of SL-MCTS to be more efficient than MCTS. The state value v is a scalar representing the path quality in each direction predicted at this position. It guides SL-MCTS toward the best-preferred strategy.t, the agent is at node mt. Regard mt as the root node. One iteration of SL-MCTS at time step t is as follows:The pipeline of SL-MCTS is shown in mt is not a leaf, the agent uses Tree Policy to descend through the search tree until the most urgent expandable node is found. The Tree Policy of SL-MCTS is represented by Eqs (1) and (2). Equation (1) balances between exploitation ) of search.1. Selection. If mt is the location of agent in the search tree; st is the environment information at mt; x is the child of mt, x ∈ AE(mt); P is the selection probability of each child node x and is one of the predictions of PV-Network; AE(mt) is a set of legal action for mt; N(mt) is the visit count of mt; N(x) is the visit count of x; Cpuct>0 is a hyperparameter, which means the amount of exploration performed; mnext is the branch selected for further exploration.where mt is a leaf node, the available neighbor node(s) AE(mt) are added to expand the search tree.2. Expansion. If v and the selection probability p in the iteration.3. Evaluation. PV-Network predicts the state value Q are backpropagated through the search tree to update nodes' statistics. The Q value corresponds to the aggregate reward of all rollouts that pass through this state. The statistics are update by Eqs 3) and (4):4. Backpropagation. The visited count and action value andmn is one node in the search tree. mn before it is updated; N(mn) is the visited number of mn; The state value v is one of the outputs of PV-network.where mt + 1 is selected from node mt based on the search probability π of SL-MCTS:When the iteration limit has been reached, the next move a is the child node of mt; N(a) is the visited count of node a.where SL-MCTS differs from the Simulation phase of the traditional MCTS algorithm. PV-Network replaces the rollout process in the traditional MCTS algorithm and can predict the selection probability of the feasible nodes and the state value. SL-MCTS has a more efficient search process and a more accurate search direction.p and the state value v. The backbone consists of three convolutional layers, and the kernel size is 3 × 3 with stride one and activated by the ReLU function. This network utilizes the convolutional layers to extract local information on the map, followed by fully connected layers to extract global information. The number of channels of these three convolutional layers in the backbone is 32, 64, and 128, respectively. The output of the backbone is used as input to the policy branch and value branch. The policy branch outputs a vector p. The value branch outputs a scalar, v.The architecture of PV-Network is shown in St is n×n×4 where n×n is the map size. The input comprises four binary feature matrices. The first matrix represents the start position of the task .the task , Layer 0Self-learning is the process of SL-MCTS generating data for training and gradually improving its decision-making ability by learning those data. Firstly, the initial model is recorded as the optimal historical model. Then, the quality of SL-MCTS's solutions is evaluated using the optimal historical model. A higher score is given to the solution of the current model if it is better than the existing model. As a result, the current model is recorded as the optimal historical model, and the optimal historical mode is generally updated during the training. The data for model training is generated based on the solutions and scores. Repeating the above process, SL-MCTS improves its ability to find the optimal path and generates better training data.m0 and mE, and the parameters of the PV-Network fθ are denoted by θ. The initialization state of each task is noted as s0. The Evaluation process of SL-MCTS makes sampling based on the predicted selection probabilities p and the state value v by the network fθ. Then, SL-MCTS selects a node m1 to move and transfer from s0 to s1. The search finishes until the endpoint xd is reached. As shown in path. The quality of its path is evaluated by the result of the optimal historical model to get a score z. The optimal historical model is the best model based on the evaluation method of the Elo rating system (details in Section 3.2) during the training process. SL-MCTS with the optimal historical model produces a result of pathb. The path score is calculated depending on Eqs (7) and 8). path is split into data of the format  based on the number of nodes. These data are independent and are stored in the training data set. In the training process, SL-MCTS solves many random tasks and generates data. Lastly, PV-network is trained by randomly sampling the training data set in a small batch. This method of splitting data can significantly break the association between paths and improve the algorithm's stability.The detail of the self-learning framework is shown in The loss function of PV-Network is:c is a hyperparameter controlling the level of L2 weight regularization, which is to prevent overfitting and controls the contribution of the regularization term to the loss function. The network parameters θ are adjusted based on the loss function Eq. (6) to minimize the error between the predicted state value v and path score z and to maximize the similarity between the selection probability p and the search probability π.where p ← (1−ε1)p + ε1ηa, where s is the state, a is legal action, and p is the predicted selection probability of each a. ε1 is set to 0.5, and it is used to encourage the exploration of different actions. Dirichlet noise is also added into the search probability π ← (1−ε2)π + ε2ηa, where ηa ~ Dir(0.3) and ε2 is 0.25, to encourage SL-MCTS to explore every feasible node during the training process. The higher ε2 is, the more different states are explored and thus enhance the data diversity of the PV-Network.To expand the range of exploration of SL-MCTS in the training process and avoid falling into the local optimal trap, Dirichlet noise is added to the selection probability In the path planning task, the path evaluation is not only related to whether the endpoint is reached but also considers the length of the path. Using only Euclidean distance or Manhattan distance is not reasonable to evaluate path quality. This method can not reflect the existence of obstacles on the line between two points and provides the agent with ambiguous feedback that does not reflect changes in the quality of its solution. Therefore, SL-MCTS generates a path score representing the current problem-solving ability by comparing their results with the optimal historical model. The path score is given by Eqs (7) and (8):path receives a score under zero, which means that similar decisions are discouraged. In contrast, if the result of Eq. (7) exceeds 0, indicating that the path length of the optimal historical model is longer than that of the current model, path receives a score above zero, which means that those similar decisions are encouraged. Furthermore, if SL-MCTS with the current model fails to reach the destination, this path receives a score, −1. The evaluator of SL-MCTS is dynamically adjusted according to the update of the optimal historical model during the training process.where γ ∈  is < 0, it denotes that the solution of the optimal historical model is better than the solution of the current model. l, a is the feasible space for each node, and k is the number of simulations per search process. For the traditional MCTS algorithm, the maximum search depth in the rollout process is d, and its computational complexity is denoted as As there are many different tasks in path planning, it is difficult to assess the computational complexity accurately. The computational complexity of SL-MCTS is analyzed by referring to the calculation method in Yonetani et al.  and Qi ei et al. . After tThis section provides detailed descriptions on the experimental settings, parameter adjustments, and evaluation methods. We conducted the training process of SL-MCTS on maps with different scales and analyzed the variability of its problem-solving capability. Additionally, we compared the performance of SL-MCTS with other advanced single-player MCTS algorithms and collective intelligence algorithms. Furthermore, we verified the generalization of SL-MCTS on random layout maps with specific obstacle densities and the dynamic environmental map. Finally, we conducted ablation experiments to explore the impact of different simulation times on SL-MCTS. The open-source code, experimental data, and detailed visualizations of the experimental data and results can be found in Liu .Cpuct is −3, and its initial multiplier (lrm) is 1.0. To avoid updating the policy parameters too much at each training iteration, the KL divergence  are used to calculate their KL divergence based on the result of Eq. (9). lrm is adjusted by Eq. (10).These experiments were implemented in Python 3.7 using PyTorch. They were executed on a high-performance computing server, using two GeForce RTX 2080 SUPER GPUs for algorithm training in parallel and CPUs that are 3.20 GHz with 16GB memory. The number of simulations of SL-MCTS is set to 30 and Nielsen,  is used kltarg is 0.02.where the parameter In order to investigate the performance of SL-MCTS on environmental maps of varying scales, we conducted experiments on 6 × 6 and 16 × 16 maps, respectively. The size of the training data set is 10,000. If the data set is completely full, older data is automatically removed as newer data are added Positive samples are defined as those paths that reach the destination and achieve equal to or shorter lengths than the optimal historical model's results. To provide a high-quality training data set for the initial training process of SL-MCTS and rapidly promote the ability of SL-MCTS, the positive sample and negative sample is stored by a 1:1 ratio in the training data set at the initial stage of training.In this paper, SL-MCTS algorithm compares with variants of MCTS like UCB1  differs from its expectation of Ea, the level Ra is adjusted as follows:where K is the hyperparameter, which means the range of changes in Elo rating. In this paper, the algorithm's high rating means that it wins more times than its opponent in the tournament, i.e. most of its path lengths are shorter than its opponent's.where To assess the performance of SL-MCTS on the path planning problem, we compared the average path length, time consumption, the standard deviation of path lengths (SD-L) and time consumption (SD-T), visited range and the percentage of successfully solved tasks (Success rate). A smaller average path length reflects a better solution quality of the algorithm. Average time consumption reflects the algorithm's efficiency in solving problems. SD-L and SD-T reflect the variation of the algorithm in the quality and efficiency of solutions. The visited range represents the ratio between the number of visited nodes and the total number of feasible nodes in the map. The success rate is defined as the proportion of successfully completed tasks to the total number of tasks and serves as one of the criteria of the algorithm's problem-solving performance. We also employed the Mann-Whitney U test as a significance test to determine the mean difference between the experimental results for algorithms. The significance level is set to 0.05.SL-MCTS's self-learning performances in two scale environmental maps are respectly present. One hundred tasks with different origins and destinations are randomly selected as a tournament from each environment. We used the Elo rating to illustrate the variation of SL-MCTS's problem-solving ability. The initial rating of the Elo rating system (Detailed in Section 3.2) is set to 1,000.Figure 10). As traditional MCTS  of algorithm, which is calculated by:To further verify the variation of SL-MCTS's path-finding capability in the self-learning process, we randomly selected 50 tasks in the 6 × 6 map as a test set and compared the average total path length of SL-MCTS at different training stages with that of MCTS-50  and other algorithms. In Task 1, ACO-30-30 is determined to be the best method. The results of the significance test show that there is no significant difference between AS-30-30 and SL-MCTS-30. In Task 2, the optimal solution of SL-MCTS-30 is 13, which is the same as PPACO-30-30, second only to SP-MCTS and SP-MCTS-CRIPPA-150. The average path length of SL-MCTS is 16.20, only 0.7 longer than that of SP-MCTS-CRIPPA-150 and PPACO-30-30, but the exploration space of SL-MCTS is only 19.9%, one fifth of other algorithms. Additionally, the average consumption time for SL-MCTS was the shortest amongst all algorithms, taking only 5.34s. PPACO-30-30 is determined to be the best method. The results of the significance test show that there is no significant difference between PPACO-30-30 and SL-MCTS-30. In task 3, the optimal solution of SL-MCTS-30 is 18, which is only second to the ant colony algorithms. Moreover, SL-MCTS explores only 28.90% of the environment space and solves the problem in just 25.48 s, making it a highly efficient algorithm. In conclusion, SL-MCTS with a simulation count of 30 performed significantly better than traditional MCTS and SP-MCTS algorithms with simulation counts of 50 or 150. Its performance is comparable to that of ACO, which is proficient at solving path planning tasks. The experimental results show that under the guidance of the PV-Network, SL-MCTS converges faster than other MCTS algorithms. However, SL-MCTS is considerably more efficient than ACO in terms of time consumption and search space for most tasks, with time consumption of less than half and search space only one fifth that of ACO. It is meaningful to mention that some MCTS algorithms are unable to solve the complex path planning problem (such as SP-MCTS-CRIPPA), mainly because most traditional MCTS algorithms are designed for game scenarios and not proficient at solving path planning tasks. However, with the proposed method in this paper, SL-MCTS has made significant improvements over MCTS algorithmsRatiolength) and time consumption (Ratiotime), which were calculated by:This section aims to evaluate the potential of SL-MCTS in tackling tasks in previously unseen environmental maps. Two sets of experimental maps, each with three different obstacle densities, were constructed based on the two map sizes. Fifty tasks of random starting and ending points were selected on each map to form the test set of each map. The map size is 6 × 6, defined as MAP 1, and 16 × 16, defined as MAP 2. The number of SL-MCTS's simulations is 30. Maps are named Sparse Map 1, Moderate Map 1, Dense Map 1, Sparse Map 2, Moderate Map 2 and Dense Map 2 according to the density of obstacles in the maps . In i is the number of the testing tasks. A represents SL-MCTS and B represents MCTS-50. A lower ratio indicates that SL-MCTS performs better than MCTS-50 in terms of path quality or time consumption. The success rate is defined as the proportion of successfully completed tasks to the total number of testing tasks.where Ratiolength value is about 0.90 on the set of maps for MAP 1 and about 0.76 on that for MAP 2. The Ratiotime value is about 0.5 both on maps 1 and 2. These experiments indicate that SL-MCTS performs significantly better than MCTS-50 in terms of path quality, particularly in environments with a map size of 16. Furthermore, SL-MCTS completes the same task using only half of the time computation required by MCTS-50. SL-MCTS performs better on MAP2 than on MAP1, which may be due to the larger search space and greater number and variety of obstacles on MAP2, making tasks more challenging and enabling SL-MCTS to demonstrate its superior capabilities. In general, these experiments demonstrated that SL-MCTS not only is able to find the tasks' solutions on the new maps but also completes them with half the time required by MCTS-50, particularly for tasks with shorter lengths.In We conducted additional experiments on random maps with different obstacle distributions. By comparing the proposed algorithm's performance in solving the same task in these diverse environmental maps, we further assessed SL-MCTS's ability to adapt to novel environmental maps. We chose two test tasks: one on a map with a size of 6, with a starting point at  and an ending point at ; the other on a map of size 16 with a starting point at  and an ending point at 14, 14). The considerable span of both tasks on their respective maps allowed us to examine different obstacle distributions. , 14. TheAccording to the results in This section presents the effect of PV-Network on the SL-MCTS algorithm with a different number of simulations. Thirty tasks are randomly selected from the 16 × 16 map as a test set. The variation of the total length and the total time consumption of SL-MCTS-30 and MCTS-30 was compared on the test set. As shown in Finally, we tested the performance of SL-MCTS in a dynamic obstacle environment to deal with stochastic environments. In addition to the eight actions shown in Inspired by the idea of “self-player” for two-player zero-sum games, this paper proposes a self-learning single-player MCTS, named SL-MCTS, to continually enhance the problem-solving ability of agents in single-player scenarios. The main contributions of this paper include constructing the self-learning framework for single-player scenarios and designing an efficient evaluation method to assess the quality of the agent's strategies in the learning process. In the experiment section of this paper, a widely-renowned robot path planning scenario was utilized to validate the efficacy of SL-MCTS. In the self-learning process, the increasing Elo ratings of SL-MCTS show that the “self-learning” method for the single-player task is effective. The performance of SL-MCTS is also compared with that of MCTS, SP-MCTS, SP-MCTS-CRIPPA, and the currently popular collective intelligence algorithms in many different tasks. The results demonstrate that SL-MCTS can find better solutions with fewer iterations than other iteration-based algorithms, which indicates the convergence speed of SL-MCTS is faster. Additionally, in terms of time consumption, the speed of SL-MCTS in solving problems is faster than other comparative algorithms. It can solve problems in less than one-third of the time required by other algorithms. These indicate that the guidance of the PV-Network greatly improves the search efficiency and the resulting quality of SL-MCTS in path planning tasks. Furthermore, we validated the adaptability of SL-MCTS in many new environmental maps. The results show that SL-MCTS can find solutions with better quality in half the time required by MCTS-50. This experiment demonstrates that the problem-solving ability of SL-MCTS is universal across different environmental maps. Finally, we validated SL-MCTS's adaptability in a dynamic environment. The experimental results show that it can successfully solve tasks in dynamically complex scenes. In conclusion, this paper demonstrates that the mechanism of “self-learning” can be applied in single-player scenarios. It provides a new way for the agent with learning capabilities to break through its ceiling of problem-solving ability. Comparative experiments have confirmed that SL-MCTS can alleviate the common issues of slow convergence, poor search quality and inefficient search in traditional MCTS algorithms, while also significantly improving search speed.In the future, we will further explore applying self-learning with other collective intelligence algorithms. We will also try to extend self-learning to improve the performance of the robotic arms in the continuous action space of the path planning problem.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.WL provided the original motivation and idea. YL further developed and implemented the idea, conducted the experiments, and produced the initial manuscript. WL and YL engaged in a thorough discussion and revision of this manuscript. YM, KX, and JQ checked the results and provided writing advice for the manuscript. ZG was responsible for the resources and revision of the manuscript and provided financial support. All authors contributed to the article and approved the submitted version.


Gardnerella vaginalis. No significant reduction was found for the remaining groups, including Escherichia (E.) coli. Moreover, we found a notable increase in resistant bacteria after antibiotic treatment. In conclusion, broad-spectrum antimicrobial treatment exhibited substantial efficacy in eradicating the majority of pathogens in our cohort. However, certain pathogens, notably E. coli, showed resilience. Given E. coli’s prominent role in infectious peripartum complications, our findings underline the challenges in antibiotic management post-pPROM and the need to establish international guidelines, particularly regarding emerging concerns about antibiotic resistances.Preterm premature rupture of membranes (pPROM) stands as a primary contributor to preterm deliveries worldwide, closely linked to consequential infectious peripartum complications, including chorioamnionitis and early-onset neonatal sepsis. As a prophylactic measure, individuals following pPROM routinely undergo antibiotic treatment. The aim of this study was to evaluate changes in the vaginal microbial colonization after antibiotic treatment following pPROM. Therefore, we retrospectively assessed the impact of antibiotic treatment on the maternal vaginal microbial colonization in 438 post-pPROM patients delivering before 29 gestational weeks. Vaginal samples were collected for microbiological analysis before and after antibiotic administration and analysed for seventeen pre-defined microbial groups. We observed eradication in eleven microbial groups, including beta-hemolytic streptococci group B and  Preterm birth is defined as delivery before 37 gestational weeks. Extremely preterm infants are born before 28 gestational weeks, very preterm infants between 28 and 32 weeks, and moderate to late preterm infants are born between 32 and 37 gestational weeks . PretermThe causes for preterm birth are diverse; it occurs either due to spontaneous labor with intact amniotic membrane, the preterm premature rupture of membranes (pPROM) or iatrogenic, early labor induction, or cesarean section due to a maternal or fetal indication . The patIn industrialized countries, the survival rates of preterm infants born at 24 gestational weeks are reported to range from 50% to 90% at 28 weeks . The risPPROM is defined as the rupture of the membranes before the onset of labor and before 37 gestational weeks. This condition complicates about 3% of all pregnancies and causes approximately 25 to 30% of all preterm deliveries ,7. The mEschericia (E.) coli, coagulase-negative staphylococci, enterococci, and Candida species.Numerous studies have investigated the vaginal microbial colonization among individuals with pPROM in recent years ,12,13,14E. coli isolates in this collective.In the last decades, antibiotic resistance has become an increasing challenge throughout the world, with previous antibiotic consumption being a common promoting factor . AccordiThis study aimed to assess the impact of routinely administered antibiotic treatment on the vaginal microbiota of patients post-pPROM and to evaluate whether antibiotic prophylaxis promotes the colonization of (multi-)resistant pathogens.This is a retrospective cohort study, conducted at our tertiary perinatal center at the Medical University of Vienna. We included all women with delivery post-pPROM before 29 gestational weeks following antibiotic treatment who were admitted between 1 June 2009 and 31 December 2018 at our department. Those with triplet or higher-grade multiple pregnancies, as well as those with fetal chromosomal anomalies, fetal malformations, and/or congenital metabolic disorders were excluded from the analyses. This study was conducted in accordance with the Declaration of Helsinki and Good Scientific Practice guidelines and approved by the ethical committee of the Medical University of Vienna (application number: 2224/2020).TM; Copan Italia S.p.A., Brescia, Italy). Culture results were interpreted as positive when they showed evidence of any microbial colonization. Additionally, a vaginal swab was assessed by Gram-staining for rapid diagnosis of vulvovaginal candidosis or bacterial vaginosis.PPROM was diagnosed either by visualization of amniotic fluid in speculum examination or by detection of either insulin-like growth factor binding protein 1 or placental alpha microglobulin-1 in the vaginal fluid. Vaginal swabs for microbiological analysis were then collected from the posterior fornix vaginae and the cervical canal for the detection of microbes with an according antibiogram. All swabs used were liquid multipurpose flocked swabs . These parameters included maternal age, singleton or twin pregnancy, gestational age at delivery, latency period between pPROM and delivery, antibiotic regimen, and application of fetal lung maturation and/or tocolysis. According to the most frequent pathogens, we pre-defined 17 microbial groups for analysis in our study; these groups are presented in p < 0.05, using the McNemar test for related samples. Resistant and multi-resistant pathogens were separately evaluated. We performed descriptive statistics for maternal and perinatal characteristics, with metric variables being described as mean and standard deviation (SD), and categorical variables as absolute and relative frequencies. Power calculations were not required due to the exploratory design of this study. Statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 27.0 .We used absolute and relative frequencies of each microbial group before and after antibiotic treatment, as well as a 95% confidence interval calculated for the relative frequencies. Changes in the vaginal microbial colonization after antibiotic treatment were evaluated by comparing each participant’s first and last culture result. The significance level was defined at We identified a total of 471 eligible women with pPROM before 29 gestational weeks. Out of these, 33 cases were excluded due to the absence of any antimicrobial treatment. In these cases, antibiotic treatment was not administered due to various factors, with the most prevalent being rapid delivery after pPROM. The remaining 438 cases were forwarded to the analyses of our study. Out of this cohort, 79 of 438 women (18%) had a twin pregnancy, whereof 6 twin infants were excluded as at least one of the twins fulfilled the exclusion criteria. In these cases, we only included the maternal as well as the other twins’ neonatal data in the analysis.In our study cohort, the median maternal age at delivery was 32.1 (SD ± 6) years. The 511 included neonates had a median birthweight of 929.3 (SD ± 252.7) grams. Among these, 216 (42.3%) were female, while 295 (57.7%) were male. The median latency period between pPROM and delivery was three days. Tocolysis was administered in 422 (96.3%) cases, and antenatal steroids for fetal lung maturation in 433 (98.9%) cases. Cesarean section was performed in 383 (87.4%) cases. During their admission to the neonatal intensive care unit, a total of 57 (11.2%) neonates unfortunately died. Detailed maternal and neonatal characteristics are shown in Baseline vaginal swabs were collected for microbial analysis from 245 of 438 women (55.9%) prior to the initiation of antibiotic treatment, yielding positive results in 240 (98%) of these cases. The follow-up swab was obtained from 396 patients (90.4%), with positive cultures observed in 247 (62.4%) women.Among the 438 patients comprising the study cohort, 300 (68.5%) received antibiotic monotherapy. Ampicillin emerged as the most frequently used antibiotic agent, administered in 349 cases (79.7%), followed by clindamycin and azithromycin, which were administered in 86 (19.6%) and 42 cases (9.6%), respectively. A comprehensive list of all prescribed antibiotics is presented in We obtained comprehensive culture results both pre- and post-antibiotic therapy for 226 of 438 (51.5%) patients. In 11 out of the 17 defined microbial groups, a statistically significant reduction in the number of patients with positive culture results for the respective microbial group was observed following antibiotic treatment. No statistically significant increase was detected for any of the investigated microbial groups. The exact results are shown in Lactobacillus species, coagulase-negative streptococci, Corynebacterium species, Enterococcus species, beta-hemolytic streptococci group B, and Gardnerella vaginalis following antibiotic treatment with respective p-values of <0.001. The abundance of other beta-hemolytic streptococci (p = 0.021) and Viridans streptococci (p = 0.007) also significantly decreased after the administration of antibiotic therapy.Comparing the baseline with the follow-up swabs, we found a statistically significant reduction in the abundance of Ureaplasma species (p = 0.289), Mycoplasma hominis (p = 0.549), E. coli (p = 0.627), other enterobacterales (p = 0.134), Staphylococcus aureus (p = 0.219), and Gram-positive anaerobes (p = 0.169).No statistically significant changes were identified for Candida albicans and non-albicans Candida. Following antibiotic treatment and, when indicated, antifungal therapy, a statistically significant decrease in positive culture results was observed, with p-values of <0.001 for Candida albicans and 0.004 for non-albicans Candida. Only one patient (0.4%) exhibited a persistent colonization with Candida spp. during the course of treatment.Additionally, we analyzed cultures regarding p-value of 0.024. Notably, only a singular instance of a positive culture result for a multi-resistant pathogen was recorded throughout the study, occurring post-antibiotic treatment , whereas in 27 patients (12%), culture results were initially negative, later transitioning to positive in the follow-up swab. This increase in resistant pathogens following antibiotic treatment achieved statistical significance with a reatment .E. coli despite antibiotic treatment.There are multiple pillars in the management of pPROM, one of which entails the administration of antibiotics to mitigate the risk of amniotic infection . Our stuAmong our study collective, the predominant antibiotic agent was ampicillin, followed by clindamycin and azithromycin. To date, there are no internationally recognized guidelines available defining a first-line antibiotic regimen for women with pPROM . HoweverStaphylococcus spp., particularly among those treated for urinary tract infections [E. coli subsequent to antibiotic treatment for respiratory tract infections [Lactobacillus spp. or Prevotella spp. was observed following antibiotic treatment with ampicillin, amoxicillin, or azithromycin. The study did, however, reveal a significant reduction in certain species such as Weeksella or Lachnospira spp., alongside an increase in others, including Peptostreptococcus and Tissierellaceae ph2.The effect of the antibiotic treatment on the vaginal ecosystem in pregnancy has been a topic of scientific interest for many years. In a study conducted by Stokholm et al. , the autfections . Consonafections  reached fections . To datefections  conductefections , no signGardnerella vaginalis and beta-hemolytic streptococci Group B. These findings indicate a normalization of a disrupted vaginal microbial colonization and the elimination of vaginal infections. However, we also found a significant reduction in the colonization with Lactobacillus spp., indicating an iatrogenic disturbance in cases with a normal vaginal microbiota, potentially increasing susceptibility to pathogen overgrowth [Candida spp. in post-pPROM women undergoing antibiotic prophylaxis. Additionally, in cases of pre-existing fungal colonization, our results show that, when coupled with an appropriate antifungal treatment, a significant eradication of Candida spp. can still be achieved.Our assessment of post-pPROM women revealed a substantial eradication of various microbial groups, including ergrowth . It is nergrowth ,25. HoweMycoplasma spp., Gardnerella vaginalis, E. coli, and Candida spp. [E. coli or Mycoplasma hominis, were not sufficiently eliminated. This observation aligns with the existing literature: In 2019, Li et al. [E. coli isolates in pPROM patients.Chorioamnionitis is a dreaded complication in women following pPROM, which is thought to derive from ascending vaginal pathogens after the amniotic barrier is diminished . Frequenida spp. . The priida spp. . While ti et al.  reportedEscherichia coli and group B streptococci being the most frequent [Hemophilus influenzae, coagulase-negative staphylococci, Candida spp., enterobacteria, listeria, or anaerobes are also associated with EONS but are known to be less prevalent [E. coli by the prescribed antibiotic regimen in our patient cohort. These findings are especially relevant with regards to the related literature: Tsai et al. [E. coli isolates in newborns with E. coli-associated EONS exhibited ampicillin resistance.As a result of pPROM and chorioamnionitis, affected infants are at risk to acquire early onset neonatal sepsis (EONS), diagnosed in about 20 out of 1000 infants born prior to 29 weeks of gestation . In our revalent . Our fini et al.  conducteMoreover, there are studies suggesting that colonization with antimicrobial-resistant pathogens may increase in women with pPROM after antibiotic treatment . SimilarE. coli-associated EONS [As previously mentioned, presently, there is no international consensus regarding the optimal first-line antibiotic treatment for patients post-pPROM . Howeverted EONS , these fted EONS  thus proWe are aware of the strengths and limitations of our study. Firstly, this is a retrospective study and, unfortunately, the complete microbial sets were only available for 226 of our cases. While this cohort still provided a substantial sample size for facilitating robust statistical significance for our analysis, it is imperative to recognize the potential for performance and selection bias due to the retrospective study design. As a strength, the administration of a diverse range of antibiotic agents in our cohort enhances the generalizability of our findings beyond local treatment guidelines. The broad spectrum of the microbial groups that we analyzed also allows a comprehensive presentation of the changes in the vaginal ecosystem after antibiotic treatment. Regrettably, due to insufficient data, our analysis did not extend to multi-resistant pathogens, and we are aware that this is a clear limitation of our work.E. coli, which emphasizes the challenge of achieving comprehensive microbial eradication in this challenging situation. However, in the light of the “antibiotic stewardship”, we consider antimicrobial resistance as a harmful downside of unnecessary or incorrect antibiotic treatments that have the potential to negatively affect both the mother and the newborn. Internationally validated guidelines are warranted to clearly define a regimen of the respective first-line antibiotic agents in women with pPROM. Our study herewith highlights the necessity to establish these, particularly in the context of emerging concerns about antibiotic resistances.Antibiotic treatment in women with pPROM eradicates the majority of microbial groups with a notable persistence of


Immunoglobulin-G4-related disease (IgG4-RD) is a fibro-inflammatory condition that can impact any organs/tissues, including the vascular systems, resulting in aortitis/periaortitis/periarteritis (PAO/PA). The complex nature of this disease and limited understanding have led to potential delays in identifying and managing irreversible organ damage. Herein, we report a 17-year-old female with hyper IgG4 disease, sclerosing mesenteritis, short stature, and insulin resistance who presented with symptoms of fever, epigastric pain, left flank pain, vomiting, dizziness, decreased urine output, and diarrhea. Imaging studies revealed an arterial wall thickening of the ascending aorta and aortic arch, splenic abscesses, and enlarged lymph nodes, consistent with IgG4-related aortitis. Treatment with steroids and antifungal agents was initiated. However, the patient developed septic shock and multi-organ failure requiring inotropes and mechanical ventilation. Ascending aortic aneurysm rupture, in this case, probably led to the patient's demise, but unfortunately, no autopsy was done to confirm it. This case highlights the importance of identifying and addressing vascular involvement in IgG4-RD to prevent irreversible organ damage and mortality. Immunoglobulin G4-related disease (IgG4-RD) is a heterogeneous fibro-inflammatory condition in which it is characterized by hyper IgG4 serum levels and lymphoplasmacytic infiltrations of the involved organs, which can affect any organ or tissue, including the vascular system. For example, the aorta, iliac artery, and carotid artery are the commonly involved blood vessels in IgG4-related diseases resulting in conditions like aortitis/periaortitis/periarteritis (PAO/PA) . AlthougA 17-year-old female known case of hyper IgG4 disease, sclerosing mesenteritis, short stature, and insulin resistance since childhood presented to the emergency department complaining of fever, epigastric pain, left flank pain, vomiting, dizziness, decreased urine output, and diarrhea for five days. She has a negative history of sore throat, cough, runny nose, or chest pain. On bedside clinical assessment, she was conscious with mild respiratory distress and exhibited signs of septic shock; her initial vitals were blood pressure of 97/50, heart rate of 138, temperature of 38.8° C, respiratory rate of 28, and oxygen saturation of 100% on room air.C-reactive protein and sedimentation rate were elevated. Chest X-ray was congested with wide mediastinum and left pleural effusion, as shown in Figure The patient was prescribed prednisolone 60 mg to control the inflammatory process and for the known response for IgG4 disease to steroids. She was advised to remain on this dose for three weeks and then gradually taper off. Anidulafungin was administered for a total of 10 days. For managing sepsis, meropenem and vancomycin were administered for a total of 14 days. Enoxaparin 80 mg bid was administered for PE. Surgical interventions for the aorta were not considered during the event due to the high risk of bleeding and rupture. Lastly, upon discharge, the patient was started on mycophenolate, vitamins C and D, alendronate, and oriented colchicine.A few months later, the patient was admitted for two months with septic shock and multi-organ failure, coagulopathy, lactic acidosis, and significant hemoglobin drop. She was admitted to the intensive care unit with mechanical ventilation. Thereafter, she was extubated and shifted to bilevel positive airway pressure (BiPAP). This was followed by multiple attacks of septic shock and multi-organ failure, requiring inotropes and mechanical ventilation. TTE and TEE showed severely dilated left ventricle (LV) with an ejection fraction (EF) of 20% and global LV hypokinesia. Consequently, given her multi-organ failure and overall prognosis, she was designated as "not for resuscitation" care after a discussion with her family in the appropriate manner. The possibility of sudden death due to aortic rupture was highlighted in that delicate discussion. Nevertheless, the patient showed improvements in her renal functions and stabilization of her hemodynamics, leading to extubation to BiPAP. Unfortunately, the patient experienced sudden cardiac arrest in the form of pulseless electrical activity (PEA). The cause of death was likely due to the rupture of an ascending aortic aneurysm, as noted in the death report. No autopsy was done.A decade ago, the first documented cases of vascular involvement in IgG4 disease emerged, with subsequent observations indicating a high prevalence of involvement in the abdominal and thoracic aorta, manifested as aortitis or periaortitis . HoweverIt has been shown that males were commonly found to have IgG4 aortitis compared to females . The etiRegarding treatments, vascular involvements in IgG4 disease were reported to be not an exception from the use of steroids . HoweverIn conclusion, even though the clinical manifestations of vascular inflammations in IgG4 disease can be challenging and may result in delayed diagnosis, physicians should be vigilant and aware of the vascular involvements in such patients, as the commonly involved parts are the aorta followed by iliac and coronary arteries. Nevertheless, with new advanced techniques, it could contribute to rapid recognition and appropriate management of such cases.


COVID-19 vaccines play a key role in ending the pandemic. Unraveling the immunological phenomena involved in offering protective immunity is the cornerstone of achieving such success. This perspective evaluates the possible mechanisms and implications of IgG4 production in response to mRNA-based COVID-19 vaccines. Undoubtedly, the ongoing COVID-19 pandemic has stimulated unprecedented research on several aspects of this infectious disease. Despite the clear success in developing efficacious vaccines against COVID-19, the virus keeps evolving, and we must respond accordingly. As has always been the case in the vaccinology field, basic immunological studies are pivotal to unraveling the known unknowns and to fine-tune new vaccine development. As if the original antigenic sin and ongoing immune escape due to the emergence of new (sub)variants were not enough issues for long-term vaccine-induced protective immunity, it has recently been reported  that rep+ T cell responses. The soluble Ag has three different ways to reach the lymph nodes (T cells or deliver the Ag to follicular dendritic cells (FDCs) in GC light zones; (ii) dendritic cells at the injection site can (macro)pinocytose the shed Ag, process, migrate to the ipsilateral lymph nodes , and there, present to naïve T cells. The latter event, however, does not lead to loading FDCs with the native Ag; (iii) last but not least, soluble Ag that is shed from the deltoid muscle cell surfaces or released from disrupted cells due to local inflammatory response (mediated by pathogen- or danger-associated molecular patterns) into the extracellular environment. Then they are drained to the ipsilateral lymph nodes  via afferent lymphatics. After entering the subscapular sinus, Ags find their way to the deeper parts of the lymph nodes, and again cognate B cells pick them up and take them to GCs.It is known that mRNA-based vaccines lead to more spike antigen (Ag) shedding from cell membranes, even leading to a period of antigenemia, that is, the presence of cell-free circulating Ag , 4. Thisph nodes : (i) mulAdditionally, the processes mentioned above are accentuated when preexisting antibodies from previous infection or vaccination(s) bind the Ag. The resultant immune complex can also activate complement through classical pathway, producing local C3a and C5a. It is shown that follicular B cells express C3aR1 and C5aR1, which can signal these B cells to undergo class-switch recombination (CSR) in GCs in an autocrine fashion (T cells to favor CSR over IgG4 (Even months after vaccination, the key to retaining GC reaction seems to rely on the immune complex reservoir on the surface of FDCs as produced through the mechanisms mentioned earlier. Maintaining and replenishing FDC surface reservoir is much more efficiently accomplished when complement components opsonize the Ag. It has recently been shown that spike Ag can directly activate complement via its lectin and alternative pathways , 7. Spikver IgG4 . IgG4 prver IgG4 -15 in ver IgG4 . The shiver IgG4 .At this evolutionary juncture, IgG4 is probably still figuring out its role in the immune response. The presence of IgG4, or lack thereof, does not necessarily insinuate protection from an infection or an exuberant immune response, a notion that is learned from chronic parasitic infections and IgG4-related diseases. Despite IgG4’s high levels in certain circumstances, the clinical picture remains not significantly different than when levels are within normal limits, as it appears that IgG4 is a by-product of another ongoing process. Undoubtedly, more in-depth research would shed light on the role of this new evolutionary member of the immune response.


Cannabis is a multifaceted plant with numerous therapeutic properties on one hand, and controversial psychotropic activities on the other hand, which are modulated by CB1 endocannabinoid receptors. Δ9-Tetrahydrocannabinol (Δ9-THC) has been identified as the main component responsible for the psychotropic effects, while its constitutional isomer cannabidiol (CBD) has shown completely different pharmacological properties. Due to its reported beneficial effects, Cannabis has gained global popularity and is openly sold in shops and online. To circumvent legal restrictions, semi-synthetic derivatives of CBD are now frequently added to cannabis products, producing "high" effects similar to those induced by Δ9-THC. The first semi-synthetic cannabinoid to appear in the EU was obtained through cyclization and hydrogenation of CBD, and is known as hexahydrocannabinol (HHC). Currently, there is limited knowledge regarding HHC, its pharmacological properties, and its prevalence, as it is not commonly investigated in routine toxicological assays. In this study, synthetic strategies were explored to obtain an excess of the active epimer of HHC. Furthermore, the two epimers were purified and individually tested for their cannabinomimetic activity. Lastly, a simple and rapid chromatographic method employing a UV detector and a high-resolution mass spectrometer was applied to identify and quantify up to ten major phytocannabinoids, as well as the HHC epimers, in commercial cannabis samples. Δ9-Tetrahydrocannabinol (Δ9-THC), which was identified by Raphael Mechoulam in the early 1960s, is the primary phytocannabinoid responsible for the psychotropic effects of cannabis. The constitutional isomer of Δ9-THC, cannabidiol (CBD), is the second most abundant phytocannabinoid found in cannabis and does not exhibit any narcotic activity. CBD is predominantly present in cannabis cultivated for industrial purposes, commonly referred to as hemp varieties. Other so-called “minor” phytocannabinoids have been isolated and characterized, and some of them have demonstrated euphoriant activity similar to Δ9-THC. Among the recently discovered THC-type phytocannabinoids, such as Δ9-tetrahydrocannabiphorol (Δ9-THCP)2, Δ9-tetrahydrocannabihexol (Δ9-THCH)3, and Δ9-tetrahydrocannabibutol (Δ9-THCB)4, the heptyl homologue has exhibited an unequivocal cannabinomimetic activity even higher than Δ9-THC in animal experiments2.Cannabis is the most widely used illicit drug, with an estimated 4% of the population (15–64 years old) in 2019 reported to have used it, according to the World Drug Report 20219-THC as the main psychotropic component of cannabis, chemical and pharmaceutical research focused on modifying the lead compound to obtain more potent analogues5. Additionally, the discovery of the endocannabinoid receptors CB1 and CB2 has led to the identification of new scaffolds, enabling the development of increasingly potent synthetic cannabinoids (SCs). However, such compounds, which have limited therapeutic utility, are generally synthesized in illegal laboratories and sold for recreational use. Recently, over 250 synthetic compounds targeting the endocannabinoid system have been developed in clandestine laboratories6. These compounds are typically added to products often referred to as “Spice” or “K2” and distributed to end-users7. In contrast to THC, which has a relatively large safety margin when used recreationally and requires higher doses to cause serious adverse effects8, the use of SCs has been documented as completely unsafe, with a range of dangerous side effects9.After the identification of Δ10. These varieties can have high levels of CBD, which lacks cannabinomimetic properties and has been recognized as a potent antiepileptic drug11. However, when CBD is treated with acids, it cyclizes to form Δ9-THC and Δ8-THC12. Both THC isomers are subject to international restrictions and are scheduled under Schedule II of the United Nations Convention on Psychotropic Substances13. To address the marketing of controlled substances, the development of semi-synthetic derivatives of CBD has become a widely employed strategy in the USA. The first semi-synthetic cannabinoid (SSC) reported in the EU and monitored as a novel psychoactive substance (NPS) by the EU Early Warning System since October 21, 2022, is hexahydrocannabinol, better known as HHC14. HHC (hexahydrocannabinol) was first described in 1940 by Adams15, but it has only recently gained attention from toxicologists and analysts16. Despite its long history, few experimental studies have investigated its effects, particularly in humans. HHC is often sold as a “legal” alternative to illegal THC, and it is commonly sprayed onto or mixed with cannabis products marketed as “legal highs”14.Low-THC cannabis varieties (containing up to 0.3% THC) have recently been legalized in the USAR and 9S, while the configuration at C10a and C6a remains unchanged. Early studies on HHC’s biological activity in animals, although compromised by the low purity of the material used, indicated that HHC exhibited lower marijuana-like activity compared to THC18. Subsequent studies using better-characterized material revealed that the R epimer is the one responsible for cannabimimetic activity, while the S epimer is either devoid of or has less psychotropic effect19.HHC has three stereogenic centres, which theoretically give rise to eight stereoisomers. However, in practice, HHC is consistently found as two epimers: 920. The study found that the epimer with the equatorial methyl substituent ((R)-HHC) induced severe stupor, ataxia, immobility, and other effects indicative of high potency, even at low doses. The epimer with the axial methyl group ((S)-HHC) induced drowsiness and decreased motor activity at higher doses20. However, the authors noted that the compound used was not pure, and impurities of the other epimer could have influenced the results20.The psychotropic activity of HHC epimers was evaluated in a study by Mechoulam et al. in 1980, which involved administering the individual epimers of HHC to rhesus monkeysR epimer in certain industrial hemp products16. Therefore, it is important to investigate the formation of one epimer over the other starting from CBD and analyze the epimeric ratio in HHC-containing cannabis products. The present study aims to evaluate the epimeric excess resulting from different synthetic strategies and determine the concentrations of both epimers in commercial HHC-containing cannabis products. To achieve these goals, high-performance liquid chromatography coupled with a diode array detector and high-resolution mass spectrometry (HPLC–DAD-HRMS) was employed. In addition to HHC epimers, ten other phytocannabinoids commonly found in cannabis extracts were analyzed, including CBD, Δ9-THC, Δ8-THC, CBG, CBC, their corresponding carboxylated native precursors (cannabidiolic acid (CBDA), tetrahydrocannabinolic acid (THCA), cannabigerolic acid (CBGA), and cannabichromenic acid (CBCA)), and the THC oxidation product cannabinol (CBN) , while Δ8-THC was obtained when CBD was treated with p-toluensulfonic acid (pTSA) for a longer duration (18 h)21. Subsequent reduction of the double bond on the terpene group resulted in a mixture of the two HHC epimers. Hydrogenation of Δ9-THC yielded an excess of the S epimer compared to the R epimer (approximately 2:1 ratio), whereas hydrogenation of Δ8-THC gave a 3:1 epimeric ratio in favour of the R epimer21.The synthesis of HHC epimers was carried out by subjecting CBD to acidic treatment. Depending on the reaction conditions, cyclization and hydrogenation of CBD can lead to the formation of either ΔpTSA for specific durations, followed by hydrogenation without purification of the intermediates. HHC was obtained as the product in both reactions, but with different epimeric ratios depending on the reaction conditions. Specifically, the reaction with HCl for 2 h resulted in a 57:43 S/R ratio, while the reaction with pTSA for 18 h yielded a 61:39 R/S ratio -HHC and (9R)-HHC, were isolated using semi-preparative HPLC, achieving purity grades exceeding 95% and 99%, respectively, which were sufficient for conducting the biological tests. UV, MS, and NMR analyses were performed to characterize both epimers. The UV spectra were found to be superimposable  of a fragment at m/z 193.1223 in HESI + mode. This fragment likely corresponds to the resorcinol group attached to one carbon atom of the reduced terpene moiety, with the oxygen atom no longer included in the pyran ring -HHC, while the second eluted epimer corresponds to (9R)-HHC -HHC/(R)-HHC mixture with a 3:1 ratio, demonstrating that the signals indicated as “major” corresponded to the S isomer, while those indicated as “minor” corresponded to the R isomer22. The findings of Gaoni et al. and Archer's work support the characterization of the HHC epimers based on the NMR spectra. Gaoni et al. suggested that the isomer labeled as VIa, which exhibited a proton signal at C10a (C3 in the old numbering system) appearing “as a broad doublet centered at 2.85 ppm”, corresponded to the isomer with the methyl group at C9 (C1 in the old numbering system) in the axial position ((S)-HHC). Conversely, the “broad doublet centered at 3.05 ppm” in structure VIb corresponded to the isomer with the methyl group at C9 in the equatorial position ((R)-HHC)21. In the current study, a peculiar shift in the NMR spectrum was observed for the hydrogen attached to C10α, with a change from 2.85 ppm in (S)-HHC to 3.03 ppm in (R)-HHC. Additionally, the hydrogen atom of C10a exhibited a shift from 2.67 ppm in the S epimer to 2.49–2.37 ppm in the R epimer. These observed shifts are consistent with the data reported in the previously mentioned works22, further confirming the assignment of the stereochemistry of the HHC epimers in this study.The individual HHC epimers, , while the R epimer was found in the range of 17.13% to 26.46%.The method was applied to analyze all phytocannabinoids and HHC epimers in ethanolic extracts of two hemp biomass samples (HHC-1 and HHC-2), one HHC hashish sample (HHC-3), and a pure HHC sample (HHC-4) . Extraction of the analytes was performed using ethanol, as previous studies have demonstrated its superior ability to extract this class of compounds9-THC and Δ8-THC, with the former being predominant. On the other hand, THCA and CBCA were present in trace amounts. The cannabinoid concentrations in the analyzed samples are reported in Table All extracts exhibited a prominent presence of CBDA and CBD, along with lower amounts of CBGA, indicating that the inflorescence originated from industrial hemp varieties. Specifically, CBDA ranged from 2.8 to 7.18%, and CBD ranged from 1.20 to 2.60%. CBGA was also detected in low concentrations, ranging from 0.06 to 0.26%. Additionally, CBN was consistently present in all samples, with concentrations ranging from 0.05 to 0.21%. All samples showed the presence of both Δm/z 319.2629 and [M-H]- at m/z 317.2488, displaying higher intensity in positive ionization mode . This compound, with a predicted molecular formula of C21H34O2, presented a characteristic fragment at m/z 139.1480, which differed by two hydrogen atoms from that of HHC (137.1324) and by four hydrogen atoms from CBD- and THC-like cannabinoids (135.1174). This fragment could likely be attributed to the terpene moiety. Based on the chromatographic data, which showed a longer retention time compared to CBD, and the HRMS fragmentation pattern, a CBD-like structure with all saturated C–C bonds on the terpene moiety could be proposed. As its retention time fell between THC and HHC species, the presence of an additional free hydroxyl group could explain its higher hydrophilicity compared to HHC, while the complete saturation of the terpene moiety could account for its higher lipophilicity compared to THC. This compound is likely to derive from the hydrogenation of residual CBD starting material in the reaction mixture, known as H4CBD. The spectroscopic data aligns with the findings reported by Collins et al., who synthesized and characterized both HHC and H4CBD.Isomers of HHC could be tentatively identified in all extracts by HRMS, along with other oxidation derivatives of HHC. Since no standard was available for these compounds, only the molecular formula could be hypothesized . Interestingly, the sample of pure HHC exhibited a unique peak at 12.96 min with a precursor ion [M + H] + at R)-HHC and (9S)-HHC was evaluated using a tetrad of behavioral tests on mice. These tests assess spontaneous activity, catalepsy (immobility index), analgesia, and changes in rectal temperature -HHC at a dose of 10 mg/kg significantly reduced the spontaneous activity of mice in the open field test compared to the vehicle-treated group. The distance covered by mice in the open field was significantly decreased by (9R)-HHC , whereas (9S)-HHC did not affect locomotion  -HHC administration increased the latency of moving from the catalepsy bar, indicating a decrease in cataleptic behaviour. Although the difference was not statistically significant compared to the vehicle-treated group, there was a trend towards reduced catalepsy  for (9R)-HHC -HHC demonstrated a significant antinociceptive effect, indicating analgesic properties. The latency to respond to the hot plate stimulus was significantly increased by (9R)-HHC  compared to the vehicle-treated group (13.768 ± 2.367 s) -HHC did not induce catalepsy  or analgesia .In the hot plate test, (9 s) Fig. d. In conR)-HHC and (9S)-HHC showed a trend towards decreasing body temperature, indicating hypothermic effects. However, the difference was not statistically significant compared to the vehicle-treated mice. (9R)-HHC exhibited a greater trend towards decreasing body temperature  compared to (9S)-HHC , but further analysis is needed to establish statistical significance -HHC possesses cannabimimetic activity, as evidenced by decreased locomotor activity, decreased catalepsy, increased analgesia, and a trend towards hypothermia. In contrast, (9S)-HHC did not significantly affect these behavioural parameters.These results suggest that  or “spice” have emerged as illegal drugs. These SCs are structurally distinct from Δ9-THC and have been marketed as alternatives to cannabis.Since the discovery of Δ11. The World Health Organization (WHO) has suggested that CBD should not be classified as a scheduled substance, as it lacks the intoxicating properties associated with THC or other SCs27.In recent years, there has been a resurgence in the cultivation of cannabis for industrial purposes, particularly due to the high concentrations of CBD, which is non-psychoactive and possesses various therapeutic properties. CBD is used in medicinal products for the treatment of certain types of childhood epilepsyIn some European countries, the recreational use of industrial hemp inflorescence as a substitute for high-THC cannabis has become a widespread phenomenon known as “light cannabis”. However, the legal status of such products remains unclear in European legislation. In parallel, the USA have allowed the legal marketing of all cannabis products with THC levels below 0.3% with the Farm Bill Act in 2018.9-THC and Δ8-THC. By removing the double bond at either the C9 or C8 position, a new stereogenic center is generated, resulting in either the R or S epimer. Mechoulam had suggested that it is possible to obtain an excess of the R epimer from Δ8-THC and an excess of the S epimer from Δ9-THC21.Alongside the emergence of SCs, there has been the appearance of semi-synthetic cannabinoids (SSCs) in the United States. These compounds are derived from chemical modifications of cannabinoids extracted from cannabis. One such compound, HHC, is the hydrogenation product of ΔR epimer has a higher affinity for the CB1 receptor, while the S epimer has poor affinity19. This suggests that different epimeric mixtures with varying intensities of biological effects can be obtained based on the synthetic procedure.In vitro studies have indicated that the 9-THC or Δ8-THC under acidic conditions, depending on factors such as reaction time, catalyst type, and reaction conditions21. As a result, the production of Δ8-THC and Δ9-THC as semi-synthetic products from CBD extracted from hemp has increased14. However, both Δ8-THC and Δ9-THC are controlled substances and not legally marketed. Therefore, the subsequent reduction of the double bond to form semi-synthetic HHC has allowed for a legal alternative that offers similar psychoactive effects to THC. HHC is currently being sprayed onto hemp products and openly sold to provide the desired “high” effects of cannabis.CBD has been shown to isomerize to either ΔR epimer were consistently higher than those of the S epimer in all samples.The scientific studies on HHC are limited, highlighting the need for further investigation. The researchers’ work demonstrates that HHC can be obtained from CBD, and the reaction conditions can selectively favour the production of one epimer over the other. Using a validated analytical method, the presence of HHC was identified in two commercial samples of hemp biomass (HHC-1 and HHC-2) and one hemp hashish sample (HHC-3). The concentrations of the 9-THC and Δ8-THC in the biomass extracts and pure HHC mixture were below 0.5%, while the HHC hashish sample contained around 2% of these phytocannabinoids. The presence of Δ9-THC and Δ8-THC could be attributed to residues from the HHC synthesis process, although a natural origin cannot be ruled out. The relatively high levels of HHC compared to CBD and THC in the samples suggest its exogenous origin rather than being naturally occurring28.The hemp biomass samples also contained significant concentrations of other phytocannabinoids, particularly CBDA and CBD, indicating that they were derived from industrial hemp. The levels of ΔR epimer was consistently found in excess of the S epimer, it is likely that HHC was synthesized starting from CBD with Δ8-THC as the reaction intermediate, rather than using Δ9-THC. Analysis of the pure HHC sample (HHC-4) revealed the absence of CBDA and CBD, but the presence of CBN, Δ8-THC, and Δ9-THC as impurities alongside the main component HHC. The R epimer was slightly in excess, indicating a similar synthetic route.Furthermore, since the R)-HHC significantly affected two out of the four behaviours in the tetrad test: hypolocomotion (reduced spontaneous activity) and analgesia (pain relief). These findings suggest a partial CB1-mediated psychotropic effect. However, the potency of HHC in inducing the tetrad effects was lower than that of Δ9-THC and Δ9-THCP, a recently discovered THC analog.To assess the cannabimimetic activity of HHC, the tetrad test in mice was conducted. Due to the limited availability of the two epimers, only one dose (10 mg/kg) was tested, similar to the dose used for THC in the tetrad test. The results showed that ; dichloromethane (CH2Cl2); cyclohexane (CE); chloroform (CDCl3); ethanol (EtOH). Stock solutions (1 mg/mL) of certified reference cannabinoid standards of CBDA, Δ9-THCA, CBGA, CBCA, CBD, CBG, CBC, CBN, as well as of Δ9-THC and Δ8-THC (500 µg/mL) were bought from Cayman Chemical . Stock solutions (1 mg/mL) of CBDH, Δ9-THCH, (9R)-HHC and (9S)-HHC were obtained by properly diluting the pure compounds in-house synthesized.Ethanol 96%, acetonitrile (ACN), and formic acid (FA) were all LC–MS grade and were bought from Sigma Aldrich (USA). Ultrapure water was obtained with a water purification system . Chemicals and solvents employed in the synthetic process were reagent grade and used without further purification. The following abbreviations for common organic solvents were adopted: diethyl ether  of the European Union Treaty (GUCE 26/10/2012).Samples of hemp biomass (HHC-1 and HHC-2), HHC hashish (HHC-3) and pure HHC were purchased from Baked Bologna . The use of 2CO3 and subject to hydrogenation with an H-Cube ThalesNano flow reactor  according to the following experimental conditions: 3 mm 10% Pd/C cartridge, 30 °C, 20 bar, 1 mL/min. The crude product mixture showed a 57:43 S/R HHC mixture along with other by-products. The solvent was evaporated and the crude of the reaction was purified with semipreparative HPLC–UV . An isocratic elution was employed with mobile phase 80% ACN (with 0.1% FA) and 20% MilliQ water (with 0.1% FA) at a flow rate of 7.5 mL/min. The UV trace was followed at 228 nm and the compounds of interest were obtained with a purity higher than 95% (12 mg for (9R)-HHC and 8 mg for (9S)-HHC).CBD (0.7 mmol) was dissolved in 20 mL of absolute EtOH containing 0.05% HCl and refluxed for 2 h. The resulting crude was neutralized with a saturated solution of Na2Cl2, pTSA  was added at room temperature, under a nitrogen atmosphere. The reaction was stirred in the same conditions for 48 h. After that, the mixture was diluted with Et2O and washed with a saturated solution of NaHCO3. The organic layer was collected, washed with brine, dried over anhydrous Na2SO4 and concentrated. The obtained crude was subject to hydrogenation as described for the synthetic procedure b to give a crude product with a 61:39 R/S HHC mixture The two epimers were purified with semi-preparative HPLC as reported above (11 mg for (9S)-HHC and 8 mg for (9R)-HHC).To a solution of CBD (0.7 mmol) in 15 mL of anhydrous CH1H and 100.62 MHz for 13C) or a Bruker 600 spectrometer (working at 600.130 MHz for 1H and 150.902 MHz for 13C). Monodimensional spectra were acquired with a spectral width of 8278 Hz (for 1H NMR) and 23.9 kHz (for 13C NMR), a relaxation delay of 1 s and a number of transients of 32 and 1024 for 1H NMR and 13C NMR, respectively. NMR spectra were acquired in CDCl3 and chemical shifts (δ) were registered in ppm with respect to that of the residual solvent (δ = 7.26 ppm for 1H and δ = 77.20 ppm for 13C); coupling constants are reported in Hz, splitting patterns are expressed as singlet (s), doublet (d), triplet (t), quartet (q), double doublet (dd), quintet (qnt), multiplet (m), broad signal (b).NMR spectra were recorded either on a Bruker 400  δ 6.25 , 6.07 , 4.61 , 2.92–2.79 , 2.67 , 2.46–2.38 , 2.11 , 1.68–1.61 , 1.58–152 , 1.46–1.44  1.36 , 1.33–1.28 , 1.13 , 1.09 , 0.88 . 13C NMR  δ 155.38, 154.77, 142.65, 110.63, 110.19, 107.79, 77.03, 50.09, 36.33, 35.59, 32.40, 31.77, 30.76, 29.49, 28.06, 27.76, 27.10, 23.26, 22.73, 19.30, 18.98, 14.20.1H NMR  δ 6.25 , 6.08 , 4.64 , 3.03 , 2.49–2.37 , 1.87–1.80 , 1.67–1.60 , 1.59–1.52 , 1.46–1.44 , 1.36 , 1.34–1.24 , 1.16–1.07 , 1.06  0.94 , 0.88 , 0.78 . 13C NMR  δ 155.15, 154.83, 142.74, 110.44, 110.23, 107.77, 77.14, 49.30, 39.16, 35.71, 35.58, 33.03, 31.75, 30.76, 28.24, 27.94, 22.78, 22.73, 19.22, 14.21.A Vanquish Core system  with binary pump, vacuum degasser, thermostated autosampler and column compartment, and diode array detector (DAD) was interfaced to an Exploris 120 Orbitrap mass analyzer with a heated electrospray ionization source (HESI). The chromatographic separation was achieved on a Poroshell 120 EC C18   with an isocratic elution at 70% ACN for 20 min and a washing step at 98% ACN for 3 min. The column was re-equilibrated at 70% ACN for further 3 min for a total run time of 26 min at a constant flow rate of 0.5 mL/min.29. The mass analyzer was operated in both full scan (FS) and data-dependent acquisition (DDA) mode. In FS mode the resolution was set at 60,000 FWHM , the RF lens level at 70%, the maximum injection time 54 ms, the m/z range at 150–750, and the isolation window at m/z 1.2. In DDA mode the resolution was set at 30,000 FWHM, the maximum injection time at 22 ms, the m/z range at 50–750, the isolation window at m/z 1.2, and the stepped normalized collision energy (NCE) at 20–40-10029. The injection volume was 5 µL. The analyses were acquired with Xcalibur 3.0 and processed using Chromeleon 7 for the UV traces and TraceFinder 54.0 for the MS traces .The HESI parameters were optimized in previous works for cannabinoids: spray voltage 4200 kV and 3800 kV for HESI + and HESI- mode respectively, sheath gas 70 au, auxiliary gas 5 au, sweep gas 0.5 au, ion transfer tube temperature 390 °C and vaporizer temperature 150°C24. The extracts HHC-1 and HHC-2 from hemp inflorescence were analysed after 100 × dilution with mobile phase, while the extract HHC-3 from HHC hashish was 1000 × diluted. The sample of pure HHC (HHC-4) was injected at the concentration of 10 µg/mL obtained by dissolving 10 mg of the sample in 1 mL of ACN and preparing serial 10 × dilutions with mobile phase up to the desired concentration.Calibration solutions of all phytocannabinoid standards were prepared by diluting the stock solutions with ACN to get the final concentrations indicated in Table m/z of the precursor ion in both HESI + and HESI- mode was extracted with a 5-ppm error from the HRMS TIC and used for calibration.Quantification of cannabinoids was accomplished with both UV and MS data. The UV chromatograms were extracted at 228 nm. The exact n = 4). They were divided into two groups: one group received (9R)-HHC at a dose of 10 mg/kg dissolved in a vehicle  via intraperitoneal (i.p.) administration, and the other group received only the vehicle as a control. The same animals were used for all four behavioural tests.In the experiment, male C57BL6/J mice at 7 weeks old were used (R)-HHC on hypomotility (measured using the open field test), hypothermia (measured by monitoring body temperature), antinociception , and catalepsy (assessed through the bar test) were assessed. These tests were performed following the procedures described by Metna-Laurent et al.30.The effects of  followed by Tukey’s multiple comparisons test.Body temperature. The measurement of body temperature was performed after immobilizing the mouse. A probe was gently inserted 1 cm into the rectum, and the temperature was recorded once it stabilized. The probe was cleaned with 70% ethanol and dried with a paper towel between each mouse to prevent cross-contamination.Open field. The open field test was conducted 30 min after administering the drug (or vehicle). The apparatus used for the test was cleaned with a 70% ethanol solution before each behavioural session. Naïve mice were randomly assigned to different treatment groups. The mice were placed in an open field arena (dimensions: length × width × height: 44 × 44 × 30 cm), and their ambulatory activity  was recorded and analyzed for a duration of 15 min. An automated behavioural tracking system called Any-maze, specifically the Video-tracking software by Ugo Basile, was used to record and analyze the behaviours.Bar test. The bar test was conducted to assess catalepsy. A glass rod measuring 40 cm in length and 0.4 cm in diameter was horizontally elevated 5 cm above the surface. The mouse’s forelimbs were positioned on the bar while the hind legs remained on the floor of the cage, ensuring that the mouse was not lying down on the floor. The chronometer was started when the mouse held onto the bar with both forelimbs, and it was stopped when the mouse descended from the bar  or after 5 min (cut-off time). Catalepsy was measured as the duration in seconds that the mouse held the elevated bar.Hot plate. Each mouse was placed on a hot plate (Ugo Basile), which was kept at the constant temperature of 52 °C. Licking of the hind paws or jumping were considered as a nociceptive response (NR) and the latency was measured in s 85 min after drug or vehicle administration, taking a cut-off time of 30 s to prevent tissue damage. The hot plate test was performed to evaluate antinociceptive effects. Each mouse was placed on a hot plate set at a constant temperature of 52 °C. The licking of hind paws or jumping were considered nociceptive responses (NR), and the latency to respond was measured in seconds. The measurement was taken 85 min after drug or vehicle administration, with a cut-off time of 30 s to prevent tissue damage.In the described experiments, male C57BL/6 mice from Charles River  were used. The mice weighed between 18 and 20 g. The mice were acclimated to the laboratory conditions for at least 1 week before the start of the experiments. The laboratory maintained a 12-h light/dark cycle with the lights turning on at 6:00 A.M. The temperature in the facility was maintained at 20–22 °C, and the humidity was kept at 55–60%. The mice were housed in cages with three mice per cage. They had access to standard chow and tap water ad libitum, meaning they could eat and drink freely.31. Animal care and welfare were the responsibility of trained personnel who adhered to the relevant Italian and European regulations. Every effort was made to minimize the number of animals used in the experiments and to prevent any unnecessary suffering or harm to the animals during the course of the study.  and the European Commission  regarding the protection of animals used for research purposes. The experimental methods described in the study also followed the ARRIVE guidelines, which provide recommendations for reporting animal researchdy. Fig. .Supplementary Information.


To describe patterns of inappropriate antibiotic prescribing at US children’s hospitals and how these patterns vary by clinical service.Serial, cross-sectional study using quarterly surveys.Surveys were completed in quarter 1 2019–quarter 3 2020 across 28 children’s hospitals in the United States.a.m. on institution-selected quarterly survey days.Patients at children’s hospitals with ≥1 antibiotic order at 8:00 Antimicrobial stewardship physicians and pharmacists collected data on antibiotic orders and evaluated appropriateness of prescribing. The primary outcome was percentage of inappropriate antibiotics, stratified by clinical service and antibiotic class. Secondary outcomes included reasons for inappropriate use and association of infectious diseases (ID) consultation with appropriateness.P < .001); this association was most pronounced for hospitalist, PICU, and surgical and medical subspecialty services.Of 13,344 orders, 1,847 (13.8%) were inappropriate; 17.5% of patients receiving antibiotics had ≥1 inappropriate order. Pediatric intensive care units (PICU) and hospitalists contributed the most inappropriate orders . Surgical subspecialists had the highest percentage of inappropriate orders (22.5%), and 56.8% of these were for prolonged or unnecessary surgical prophylaxis. ID consultation in the previous 7 days was associated with fewer inappropriate orders (15% vs 10%; Inappropriate antibiotic use for hospitalized children persists and varies by clinical service. Across 28 children’s hospitals, PICUs and hospitalists contributed the most inappropriate antibiotic orders, and surgical subspecialists’ orders were most often judged inappropriate. Understanding service-specific prescribing patterns will enable antimicrobial stewardship programs to better design interventions to optimize antibiotic use. Inappropriate antibiotic prescribing contributes to the emergence of resistant pathogens and adverse drug events and may also result in treatment failure. A 2016–2017 point-prevalence survey of antibiotic use in hospitalized US children, conducted by 32 Sharing Antimicrobial Reports for Pediatric Stewardship (SHARPS) Collaborative hospitals, documented that 35% of admitted children had at least 1 active antibiotic order at any point in time. Of these patients, 26% received antibiotics that were considered suboptimal by antimicrobial stewardship programs (ASPs). This study also reported that routine ASP practices would not have reviewed nearly half of these suboptimal antibiotic orders. Additional characterization of inappropriate antibiotic prescribing is needed to target pediatric antimicrobial stewardship interventions to further improve antibiotic prescribing. To address this gap, the SHARPS Collaborative revised its point-prevalence survey to capture additional facets of antibiotic prescribing. This study further describes patterns of inappropriate prescribing, with a focus on the impact of clinical services.A majority of hospitalized children receive antibiotics during their admission. In this iteration, 28 hospitals participated, and each hospital completed up to 7 quarterly surveys from January 2019 to September 2020. Institutional review board (IRB) approval was obtained from Children’s Mercy–Kansas City  and at hospitals that did not adopt central IRB approval through CMH. The study population consisted of patients admitted to the children’s hospital or pediatric ward at 8:00 a.m. on the day of each quarterly survey with an active order for an enteral, parenteral , or inhaled antimicrobial.Data for this study were derived from a point-prevalence survey completed by the SHARPS Collaborative. The methodology and findings from the original survey were previously published.1 online); “other” included any additional reasons that antimicrobial use was considered inappropriate, based on ASP site-specific practices, and ASP members elaborated the reasoning using a free-text explanation. Only 1 inappropriate reason could be chosen for each antimicrobial, and ASP physicians and pharmacists were instructed to select the most important reason.For all eligible antimicrobial orders, ASP physicians and/or pharmacists classified whether antimicrobial use was inappropriate (“yes” or “no”), using the following standardized definition for inappropriate use: “The patient should not be receiving this drug via this route at this time.” For inappropriate antimicrobials, additional detail on why the antimicrobial was considered inappropriate was collected using fourteen prespecified reasons  physician within the previous 7 days.2 and 3 online), so 2019 and 2020 data were pooled for analysis. After initial analysis of antimicrobial prevalence, all further analyses were restricted to antibiotics prescribed for infectious use (treatment or prophylaxis), excluding drugs prescribed for noninfectious indications. The primary outcome was the percentage of inappropriate antibiotic use, stratified by antibiotic class and clinical service. Antibiotics and clinical services were grouped for analysis (Supplementary Tables 4 and 5 online). Secondary outcomes included frequencies of reasons for inappropriate use and association of ID consultation with appropriate antibiotic prescribing. The Pearson χ test was used for categorical tabulations. All analyses were completed using R version 4.2.1 software .Due to the emergence of COVID-19 in 2020, data were stratified by calendar year to assess differences in patient demographic data, frequency of drug use, and distribution of clinical services potentially attributable to pandemic-related changes in pediatric hospitalizations. No meaningful differences were identified  had ≥1 active antimicrobial order. In total, 18,389 active antimicrobial orders for both infectious and noninfectious indications occurred across all surveyed quarters. Antibiotics accounted for 78.2%  of these orders. Infectious indications accounted for 13,672 antibiotic orders (95.0%), and further analysis was limited to these orders.Among the 9,263 patients receiving antibiotics, 5,930 (64.0%) patients had 1 active antibiotic order; 2,730 patients (29.5%) had 2 active orders; and 603 patients (6.5%) had ≥3 active orders. The median age was 4.0 years , and 4,937 (53.3%) were male (Table P = .36), by season , or by year . The PICU and hospitalists cared for the most patients receiving at least 1 inappropriate antibiotic: 330 (21.0%) and 292 (18.0%) of 1,622 patients, respectively.Assessment for inappropriate use was performed on 13,344 antibiotic orders (97.6%). Of these, 1,847 (13.8%) were considered inappropriate, and 17.5% of patients receiving antibiotics were prescribed ≥1 inappropriate antibiotic order. The percentage of antibiotics judged inappropriate did not vary significantly between hospitals that completed 5 or more quarters of the survey vs those that did not  Fig. . Of thesHospitalists contributed the second highest number with 314 inappropriate orders (17.0%), and 14.3% of all hospitalist orders were classified as inappropriate. Intravenous third-generation cephalosporins  and clindamycin  accounted for the largest number of inappropriate hospitalist orders, while, by proportion, inappropriateness was highest among oral third-generation cephalosporins  and metronidazole .Medical subspecialties ordered 2,055 antibiotics, with 14.1% (290 orders) considered inappropriate. Pulmonary services contributed the most inappropriate orders , mostly consisting of aminoglycosides , antipseudomonal β-lactams , and intravenous third-generation cephalosporins . Gastroenterology contributed the second highest number of inappropriate orders , with antipseudomonal β-lactams  and vancomycin  most often inappropriately prescribed.Surgical subspecialties had the highest percentage of inappropriate use (22.5%). Across individual subspecialties, urology had the highest percentage of inappropriate antibiotic use at 33.3% (24 of 72 orders), followed by plastic surgery at 28.9% (22 of 76 orders), otolaryngology at 28.6% (16 of 56 orders), and neurosurgery at 24.1% (47 of 195 orders). Nearly half of inappropriate orders from surgical subspecialties were first-generation cephalosporins .6 (online).Hematology-oncology (H-O) services had the lowest rate of inappropriate use (7.93%). Sulfamethoxazole and trimethoprim and antipseudomonal β-lactams were the most frequently ordered H-O antibiotics and were rarely classified as inappropriate: 8 (0.90%) of 884 orders and 42 (6.20%) of 678 orders, respectively. A full table of percentages of inappropriate orders by antibiotic classification and clinical service is provided in Supplementary Table The services contributing the greatest number of inappropriate antibiotic orders  frequently had the same leading reasons for inappropriate use: treatment for bacterial infection was not indicated, or the spectrum was too broad either empirically or was based on results of cultures, antibiotic susceptibility testing, or rapid diagnostics Table . ConversThe greatest number of inappropriate prescriptions were for antipseudomonal β-lactams . Across all services, most inappropriate antipseudomonal β-lactam orders were due to the agent being too broad-spectrum, either empirically or based on culture/susceptibility data . Unnecessarily prolonged treatment duration was the next most common reason .First-generation cephalosporins were the second most common inappropriately prescribed antibiotics, contributing 12.5% of total inappropriate orders. Across all services, 133 (57.8%) of all 230 inappropriate first-generation cephalosporin orders were due to unnecessarily prolonged surgical prophylaxis, followed by no prophylaxis indicated . Of 893 antibiotic orders for surgical prophylaxis, 32.2% were considered inappropriate.In 91 inappropriate orders for intravenous third-generation cephalosporins (41%), antibiotics were unnecessary . An additional 92 inappropriate intravenous third-generation cephalosporin orders (41.4%) were considered inappropriate due to the spectrum being too broad.Overall, 23.6% of clindamycin orders were considered inappropriate, with most inappropriate orders prescribed by PICUs and hospitalists . Across all services, the leading reasons for inappropriate clindamycin use were that spectrum of the agent was too broad  and that the drug could have been administered orally rather than intravenously .P < .001)  of all 13,234 antibiotic orders. Overall, inappropriate use was significantly less common when ID had recently consulted  or protocolized care . Additionally, surgical prophylaxis orders were rarely associated with ID consultation and comprised the majority of surgical subspecialists’ prescribing and inappropriate antibiotic use. However, differential uptake of ID recommendations by service is also possible, with adherence to ID recommendations ranging from 35% to 90% in prior studies. These findings may represent opportunities to improve antibiotic prescribing through increased ID consultation and/or enhanced uptake of ID recommendations. Notably, 10.0% of orders prescribed within 7 days of an ID consultation were still considered inappropriate, suggesting there may be opportunity for improvement among ID consultants as well.Prior interventional and observational studies have shown that ID consultation in the acute-care setting is associated with reduction of antibiotic use and improvement in antibiotic appropriateness.Strengths of this study include multicenter and multiseasonal data collection, a clearly defined definition of antibiotic appropriateness, and detailed data at both the antibiotic and patient level. However, the study was limited by variable patient enrollment and hospital participation, meaning that the results could be more representative of larger hospitals or those that participated in more surveys. Another limitation is that ID consultation was defined as having an ID consultant note in the chart in the past 7 days. Thus, some antibiotic orders may have been entered after ID consultation was complete, resulting in an incorrect association of ID consultation with the appropriateness outcome for such orders. There may also be bias by ASP providers to judge orders reviewed by ID more favorably. Lastly, the determination of antibiotic inappropriateness used only data from chart review. Even with a provided definition of “inappropriate,” ASP teams may have judged orders differently due to varying expertise and institutional practices, and chart review is not always sufficient to adequately understand clinician rationale.In summary, inappropriate antibiotic prescribing remains common among hospitalized children and varies by clinical service. By exploring inappropriateness across antibiotic type and clinical service, we enabled identification of more focused areas for ASP intervention. This study specifically highlighted opportunities for improvement among surgical specialties with prolonged or unnecessary surgical prophylaxis and for the PICU and hospitalists with the use of broad-spectrum antibiotics and overtreatment of infections. ASPs should consider stratifying antibiotic use data by clinical services to further refine and target ASP guidelines and interventions.


Non-academic partners can be vital in successful public engagement activities on antimicrobial resistance. With collaboration between academic and non-academic partners, we developed and launched an open-access web-based application, the ‘antibiotic footprint calculator’, in both Thai and English. The application focused on a good user experience, addressing antibiotic overuse and its impact, and encouraging immediate action. The application was unveiled in joint public engagement activities. From 1 Nov 2021 to 31 July 2022 (9 month period), 2554 players estimated their personal antibiotic footprint by using the application. The non-academic partners included Greenpeace Thailand, PunchUp World and World Animal Protection Thailand. From the outset, the multiple partners had detailed discussions about the scope of the application.In 2021, we developed an open-access, web-based application, the ‘antibiotic footprint calculator’, in Thai and English, using the concept of carbon footprint calculators.1 Working with non-academic partners proved beneficial as they cross-checked and commented at all development and piloting stages on whether the application was simple, straightforward and non-technical. Our non-academic partners also had extensive experience in public engagement in other fields that became an asset in designing the application and related activities. To avoid diluting the message, we focused on addressing the overuse of antibiotics, and encouraging individual actions to potentially reduce the overuse of both direct and indirect consumption of antibiotics. Indirect consumption is defined as antibiotic consumption via animals bred for food.5The new application focused on good user experience, addressing antibiotic overuse and its impact, and encouraging immediate action Figure . We follwww.antibioticfootprint.net/calculator) calculates both direct and indirect antibiotic consumption. Users, or ‘players’, can decide to give answers to simplified questions  or more detailed questions . Direct consumption is estimated by multiplying the total number of days that a player consumed antibiotics in the past 12 months with the number of grams of antibiotics that an average person consumes per day when taking a course of antibiotics in that particular country. In the standard mode, indirect consumption is estimated by asking players to compare their consumption with the average meat consumption of individuals in their country. In the advanced mode, indirect consumption is estimated by multiplying the kilograms of meat consumed by a player in the past 12 months with the milligrams of antibiotics used per kilogram population correction unit (PCU) of animal meat (i.e. mg/PCU). Consumption of beef, chicken, pork, fish and other seafoods were included in the meat consumption.The antibiotic footprint calculator  and for animals (in tons and mg/PCU) were from official and open-access data published by each country.Our recommended immediate actions are listed in Table Ethical permission for this study was obtained from the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University (TMEC 23-002).The application was piloted with the Bangkok Health Research and Ethics Interest Group, a public advisory group based in Thailand, and their inputs were used to improve the content and flow of the application.The application was officially released on 1 November 2021 with promotional activities during Thailand World Antibiotic Awareness Week. The application was distributed to members of Greenpeace Thailand and World Animal Protection Thailand via e-mail, and also promoted during both partners’ other activities relating to antimicrobial resistance. MORU and other regional headquarters of Greenpeace and World Animal Protection also promoted the application on their websites, and via Twitter and other social media platforms in English.n = 2134) completed the quiz to receive an estimate of their personal antibiotic footprint. Most players who finished the quiz were from Thailand , followed by the UK  and India  (Table n = 640) identified themselves as male, 67.2% (n = 1434) as female, 0.7% (n = 15) as other and 2.1% (n = 45) preferred not to answer. Nearly half of the players  identified themselves as gen Z (born between 1997 and 2012), followed by millennials , gen X , baby boomers , gen alpha  and silent generation , while 0.4% (n = 9) preferred not to answer. Among countries who had a total number of players more than five, the players from Russia had the highest median antibiotic footprint at 26.1 g/year (n = 50 players) and those from Switzerland had the lowest median antibiotic footprint at 0.9 g/year (n = 6 players).As of 31 July 2022 (9 month period), based on Google Analytics and anonymous data collection, 3247 people accessed the website, of which 65.7% ,n = 5), as those were the first options in the drop-down menus) and their answers may not have been accurate. A self-reporting bias in meat consumption is also common.14 The availability of open-access data on national antibiotic consumption in human and animal sectors is still limited.5 Similar to carbon footprint calculators,9 formulas to estimate individual antibiotic footprints could be improved based on data availability and future studies. Statistics on the estimated antibiotic footprint of players cannot be directly extrapolated to the general population.15 Although more than 2500 players completed the quiz on the application, evaluation of its impact is still limited. It is likely that the addition of more local languages and local partners could improve its engagement with other non-native English-speaking countries. Further development, evaluation and studies on antibiotic footprint calculators  are required.The application has a number of limitations. Some players might play more than once or not answer accurately. It is possible that some players were just testing the application (e.g. those who answered that they were from Afghanistan and were gen alpha (Overall, we would like to highlight potential benefits of public engagement activities with collaboration between multiple academic and non-academic partners. Diverse partners bring different sets of expertise and networks and tremendously improve engagement activities or events related to antimicrobial resistance.