De novo design of modular and tunable protein biosensors.

Naturally occurring protein switches have been repurposed for the development of biosensors and reporters for cellular and clinical applications1. However, the number of such switches is limited, and reengineering them is challenging. Here we show that a general class of protein-based biosensors can be created by inverting the flow of information through de novo designed protein switches in which the binding of a peptide key triggers biological outputs of interest2. The designed sensors are modular molecular devices with a closed dark state and an open luminescent state; analyte binding drives the switch from the closed to the open state. Because the sensor is based on the thermodynamic coupling of analyte binding to sensor activation, only one target binding domain is required, which simplifies sensor design and allows direct readout in solution. We create biosensors that can sensitively detect the anti-apoptosis protein BCL-2, the IgG1 Fc domain, the HER2 receptor, and Botulinum neurotoxin B, as well as biosensors for cardiac troponin I and an anti-hepatitis B virus antibody with the high sensitivity required to detect these molecules clinically. Given the need for diagnostic tools to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)3, we used the approach to design sensors for the SARS-CoV-2 spike protein and antibodies against the membrane and nucleocapsid proteins. The former, which incorporates a de novo designed spike receptor binding domain (RBD) binder4, has a limit of detection of 15 pM and a luminescence signal 50-fold higher than the background level. The modularity and sensitivity of the platform should enable the rapid construction of sensors for a wide range of analytes, and highlights the power of de novo protein design to create multi-state protein systems with new and useful functions.

Single-cell analysis of treatment-resistant prostate cancer: Implications of cell state changes for cell surface antigen-targeted therapies.

Targeting cell surface molecules using radioligand and antibody-based therapies has yielded considerable success across cancers. However, it remains unclear how the expression of putative lineage markers, particularly cell surface molecules, varies in the process of lineage plasticity, wherein tumor cells alter their identity and acquire new oncogenic properties. A notable example of lineage plasticity is the transformation of prostate adenocarcinoma (PRAD) to neuroendocrine prostate cancer (NEPC)-a growing resistance mechanism that results in the loss of responsiveness to androgen blockade and portends dismal patient survival. To understand how lineage markers vary across the evolution of lineage plasticity in prostate cancer, we applied single-cell analyses to 21 human prostate tumor biopsies and two genetically engineered mouse models, together with tissue microarray analysis on 131 tumor samples. Not only did we observe a higher degree of phenotypic heterogeneity in castrate-resistant PRAD and NEPC than previously anticipated but also found that the expression of molecules targeted therapeutically, namely PSMA, STEAP1, STEAP2, TROP2, CEACAM5, and DLL3, varied within a subset of gene-regulatory networks (GRNs). We also noted that NEPC and small cell lung cancer subtypes shared a set of GRNs, indicative of conserved biologic pathways that may be exploited therapeutically across tumor types. While this extreme level of transcriptional heterogeneity, particularly in cell surface marker expression, may mitigate the durability of clinical responses to current and future antigen-directed therapies, its delineation may yield signatures for patient selection in clinical trials, potentially across distinct cancer types.

Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability.

BACKGROUND AND AIM: Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. METHODS: A still image-driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real-world applicability, the model was validated using 113 10-s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2-3)" or "inadequate (BBPS 0-1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter-rater variability was analyzed between the AI model and endoscopists. RESULTS: The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. CONCLUSIONS: The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.

Cytokine receptor γc effectuates the generation of proinflammatory innate CD8 T cells by non-classical MHC-I molecules.

Innate CD8 T cells correspond to a population of terminally differentiated effector T cells that phenotypically appear as antigen-experienced memory cells and functionally resemble proinflammatory CD8 T cells, expressing copious amounts of IFNγ. Innate CD8 T cells, however, are distinct from conventional effector-memory CD8 T cells as they acquire functional maturity during their generation in the thymus. Understanding the molecular mechanisms that drive their thymic development and differentiation is an intensely studied subject in T cell immunity, and here we identified the cytokine receptor γc as a critical mediator of innate CD8 T cell generation that promotes their selection even in the absence of classical MHC-I molecules. Consequently, overexpression of γc resulted in a dramatic increase of innate CD8 T cells in KbDb-deficient mice. We mapped its underlying mechanism to the expansion of IL-4-producing invariant NKT cells, so that it is the increased availability of intrathymic IL-4 which augments the selection of innate CD8 T cells. Collectively, these results unravel the selection of innate CD8 T cells being mediated by non-classical MHC-I molecules and being modulated by the abundance of the γc cytokine, IL-4.

Activated T cells secrete an alternatively spliced form of common γ-chain that inhibits cytokine signaling and exacerbates inflammation.

The common γ-chain (γc) plays a central role in signaling by IL-2 and other γc-dependent cytokines. Here we report that activated T cells produce an alternatively spliced form of γc mRNA that results in protein expression and secretion of the γc extracellular domain. The soluble form of γc (sγc) is present in serum and directly binds to IL-2Rß and IL-7Rα proteins on T cells to inhibit cytokine signaling and promote inflammation. sγc suppressed IL-7 signaling to impair naive T cell survival during homeostasis and exacerbated Th17-cell-mediated inflammation by inhibiting IL-2 signaling upon T cell activation. Reciprocally, the severity of Th17-cell-mediated inflammatory diseases was markedly diminished in mice lacking sγc. Thus, sγc expression is a naturally occurring immunomodulator that regulates γc cytokine signaling and controls T cell activation and differentiation.

Heart failure in a cat due to hypertrophic cardiomyopathy phenotype caused by chronic uncontrolled hyperthyroidism.

A 16-year-old castrated male Persian cat was presented with weight loss, anorexia and dyspnoea. Tachycardia and tachypnoea were observed upon presentation. The cat was previously diagnosed with hyperthyroidism and left ventricular hypertrophy and received methimazole, but was subsequently not followed up and treated appropriately. Thoracic radiography revealed mild pleural effusion, interstitial lung pattern, moderate cardiomegaly and moderate-to-severe dilation of the pulmonary artery and pulmonary vein. On echocardiography, the left ventricular hypertrophy, identified earlier, shoed partial regression. Therefore, the previous myocardial hypertrophy was diagnosed as a hypertrophic cardiomyopathy phenotype related to hyperthyroidism. ST-segment elevation was identified on electrocardiography, and the thyroid profile examination revealed increased total thyroxine and free thyroxine and decreased thyroid-stimulating hormone levels, suggesting myocardial injury and uncontrolled hyperthyroidism, respectively. In addition, normal N-terminal pro-B-type natriuretic peptide and high cardiac troponin I levels were found. Based on these findings, the observed congestive heart failure was considered as a sequel of myocardial injury caused by uncontrolled hyperthyroidism. Clinical signs resolved after intravenous administration of furosemide and butorphanol, oxygen supply and thoracocentesis. Furosemide and pimobendan were additionally administered, and the cat was discharged. This case demonstrates that myocardial damage due to chronic uncontrolled hyperthyroidism may cause heart failure in cats.

Updated Review Article: Cyclin-Dependent Kinase 4/6 Inhibitor Impact, FDA Approval, and Resistance Pathways.

Objective: To describe the mechanism of cyclin-dependent kinase (CDK) 4/6 inhibitors, mechanisms of resistance, and summarize various clinical trials used to determine the efficacy and safety of CDK4/6 inhibitor used for the treatment of hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-), advanced or metastatic breast cancer. Data Sources: An extensive literature search using PubMed and notable sources was performed (2016 to February 2022) using the following search terms: CDK4/6 inhibitors, palbociclib, abemaciclib, ribociclib, CDK4/6 inhibitor resistance, FAT1 gene, luminal A breast cancer, luminal B breast cancer, HR+/HER2- breast cancer. Abstracts from conferences, national clinical trials, and drug monographs were reviewed. Study Selection and Data Extraction: Relevant clinical studies or those conducted in humans and updated clinical trials were considered. Data synthesis: The various clinical trials reviewed and results have led to numerous studies and expansions of U.S. Food and Drug Administration (FDA) approval. Although the use of CDK4/6 inhibitors has improved progression-free survival in patients with HR+, HER2- breast cancer, studies have shown that resistance pathways can cause cells to be insensitive to CDK4/6 inhibitors, leading to continued cell proliferation. Conclusions: CDK4/6 inhibitors are recommended as first-line therapy in combination with endocrine therapy for patients with HR+/HER2- advanced breast cancer. However, mutations and acquired resistance can occur that affect a patient's response to treatment. Additional research needs to be conducted on strategies to overcome resistance and determine how ethnicity plays a role in resistance pathways.

Protein abundance of the cytokine receptor γc controls the thymic generation of innate-like T cells.

Innate-like T (iT) cells comprise a population of immunoregulatory T cells whose effector function is imposed during their development in the thymus to provide protective immunity prior to antigen encounter. The molecular mechanism that drives the generation of iT cells remains unclear. Here, we report that the cytokine receptor γc plays a previously unappreciated role for thymic iT cells by controlling their cellular abundance, lineage commitment, and subset differentiation. As such, γc overexpression on thymocytes dramatically altered iT cell generation in the thymus, as it skewed the subset composition of invariant NKT (iNKT) cells and promoted the generation of IFNγ-producing innate CD8 T cells. Mechanistically, we found that the γc-STAT6 axis drives the differentiation of IL-4-producing iNKT cells, which in turn induced the generation of innate CD8 T cells. Collectively, these results reveal a cytokine-driven circuity of thymic iT cell differentiation that is controlled by the abundance of γc proteins.

Smartphone Sensor-Based Human Motion Characterization with Neural Stochastic Differential Equations and Transformer Model.

With many conveniences afforded by advances in smartphone technology, developing advanced data analysis methods for health-related information from smartphone users has become a fast-growing research topic in the healthcare field. Along these lines, this paper addresses smartphone sensor-based characterization of human motions with neural stochastic differential equations (NSDEs) and a Transformer model. NSDEs and modeling via Transformer networks are two of the most prominent deep learning-based modeling approaches, with significant performance yields in many applications. For the problem of modeling dynamical features, stochastic differential equations and deep neural networks are frequently used paradigms in science and engineering, respectively. Combining these two paradigms in one unified framework has drawn significant interest in the deep learning community, and NSDEs are among the leading technologies for combining these efforts. The use of attention has also become a widely adopted strategy in many deep learning applications, and a Transformer is a deep learning model that uses the mechanism of self-attention. This concept of a self-attention based Transformer was originally introduced for tasks of natural language processing (NLP), and due to its excellent performance and versatility, the scope of its applications is rapidly expanding. By utilizing the techniques of neural stochastic differential equations and a Transformer model along with data obtained from smartphone sensors, we present a deep learning method capable of efficiently characterizing human motions. For characterizing human motions, we encode the high-dimensional sequential data from smartphone sensors into latent variables in a low-dimensional latent space. The concept of the latent variable is particularly useful because it can not only carry condensed information concerning motion data, but also learn their low-dimensional representations. More precisely, we use neural stochastic differential equations for modeling transitions of human motion in a latent space, and rely on a Generative Pre-trained Transformer 2 (GPT2)-based Transformer model for approximating the intractable posterior of conditional latent variables. Our experiments show that the proposed method can yield promising results for the problem of characterizing human motion patterns and some related tasks including user identification.

Verification of Tensile Force Estimation Method for Temporary Steel Rods of FCM Bridges Based on Area of Magnetic Hysteresis Curve Using Embedded Elasto-Magnetic Sensor.

The free cantilever method (FCM) is a bridge construction method in which the left and right segments are joined in sequence from a pier without using a bottom strut. To support the imbalance of the left and right moments during construction, temporary steel rods, upon which tensile force is applied that cannot be managed after construction, are embedded in the pier. If there is an excessive loss of tensile force applied to the steel rods, the segments can collapse owing to the unbalanced moment, which may cause personal and property damage. Therefore, it is essential to monitor the tensile force in the temporary steel rods to prevent such accidents. In this study, a tensile force estimation method for the temporary steel rods of an FCM bridge using embedded Elasto-Magnetic (EM) sensors was proposed. After the tensile force was applied to the steel rods, the change in tensile force was monitored according to the changing area of a magnetic hysteresis curve, as measured by the embedded EM sensors. To verify the field applicability of the proposed method, the EM sensors were installed in an FCM bridge pier under construction. The three sensors were installed in conjunction with a sheath tube, and the magnetic hysteresis curve was measured over nine months. Temperature data from the measurement period were used to compensate for the error due to daily temperature fluctuations. The estimated tensile force was consistent with an error range of ±4% when compared with the reference value measured by the load cell. Based on the results of this experiment, the applicability of the proposed method was demonstrated.

Domino-like water transport on Tillandsia through flexible trichome wings.

Tillandsia usneoides in epiphytic bromeliads takes up water through absorptive trichomes on the shoot surface under extreme environmental conditions. Although previous studies revealed the way by which T. usneoides absorbs water and prevents water loss, its water transport remains unclear. We characterized structures of trichome wings of T. usneoides. Wing length-to-thickness ratio of 136 and trichome interval (d)-to-wing length (l) ratio (d/l) smaller than 1 caused the water film to flatten the wings sequentially, resulting in domino-like water transport. A hinge-like linkage between wing and outer ring cells and the wing size longer than the elastocapillary length (LEC ) brought about this unique reconfiguration, which is the flattening and recovery of wings. Tillandsia usneoides transported water rapidly on the surface as the water film propagated on the exterior trichomes with flexible wings and the transport distance at the macroscopic scale grew as tx with x = 0.68 ± 0.04, unlike the conventional scaling of t0.5 . Empirical and theoretical investigations proved our assumption that external water transport with the domino-like effect predominated over internal vascular transport. Biomimetic trichome wings simulated the domino-like water transport, highlighting the important role of flexible wing arrays.

Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.

Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents.

The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still remains a challenging task in computational biology. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Here we frame the co-evolution of predators and preys in an ecosystem as allowing agents to learn and evolve toward better ones in a manner appropriate for multi-agent reinforcement learning. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys.

WHO implementation workshop on guidelines on procedures and data requirements for changes to approved biotherapeutic products, Seoul, Republic of Korea, 25-26 June 2019.

The first global workshop on implementation of the WHO guidelines on procedures and data requirements for changes to approved biotherapeutic products adopted by the WHO Expert Committee in 2018 was held in June 2019. The workshop participants recognized that the principles based on sound science and the potential for risk, as described in the WHO Guidelines on post-approval changes, which constitute the global standard for product life-cycle management are providing clarity and helping national regulatory authorities in establishing guidance while improving time-lines for an efficient regulation of products. Consequently, the regulatory situation for post-approval changes and guideline implementation is changing but there is a disparity between different countries. While the guidelines are gradually being implemented in some countries and also being considered in other countries, the need for regional workshops and further training on post-approval changes was a common theme reiterated by many participants. Given the complexities relating to post-approval changes in different regions/countries, there was a clear understanding among all participants that an efficient approach for product life-cycle management at a national level is needed to ensure faster availability of high standard, safe and efficacious medicines to patients as per the World Health Assembly Resolution 67.21.

Fracture of Standard Titanium Mandibular Reconstruction Plates and Preliminary Study of Three-Dimensional Printed Reconstruction Plates.

PURPOSE: A cohort review was performed to compare the effect of a number of variables on mandible reconstruction plate (R-plate) survival and to identify the potential risk factors for plate fracture. We also reported our preliminary results of 3-dimensional (3D) printed reconstruction plates. PATIENTS AND METHODS: The data from patients who had undergone mandibular reconstruction using reconstruction plates were evaluated for age, gender, mandibular resection indication, defect site and length, remaining occluded teeth, reconstruction plate type, simultaneous soft or bone tissue reconstruction, and radiotherapy. The plate survival rate was estimated using the Kaplan-Meier curve, and the variables were compared using the log-rank (Mantel-Cox) test. Multifactorial risk correlation was determined using logistic regression analysis. RESULTS: The study included 159 patients who had been followed for 97 ± 5.4 months. Of the 159 patients, 22 had experienced plate fracture that had occurred within 20 months. Most of the plate fractures had occurred near the mandibular bone stump, passing through the shoulder of the plate hole or the bridge between the subsequent plate holes. The overall survival was 86.2%. Patients with few occluded teeth (type I) had a significantly greater R-plate survival rate compared with those with many occluded teeth (P = .045). Laterocentral "LC" defects had a significantly lower survival rate (44.4%) compared with lateral "L" defects (84.5%; P = .00). The survival rates with soft tissue (88.7%) or bone tissue reconstruction (100%) were significantly different compared with that for R-plate alone (40%; P = .000 and P = .004, respectively). Four patients received 3D printed R-plates and were followed for 2 to 8 months (mean, 4 months) with no complications. CONCLUSIONS: Patients with many remaining occluded teeth, LC defect, and the absence of simultaneous soft or bone tissue reconstruction were associated with a lower plate survival rate. Bending of the plate increased the incidence of plate fracture, and the use of 3D printed customized R-plates seems a valuable alternative.

rhBMP-2 Pre-Treated Human Periodontal Ligament Stem Cell Sheets Regenerate a Mineralized Layer Mimicking Dental Cementum.

The periodontal complex consisting of alveolar bone, cementum, and periodontal ligaments (PDL) supports human teeth through the systematic orchestration of mineralized tissues and fibrous tissues. Importantly, cementum, the outermost mineralized layer of dental roots, plays an essential role by bridging the inner ligaments from the dental root to the alveolar bone. When the periodontal complex is damaged, the regeneration of each component of the periodontal complex is necessary; however, it is still challenging to achieve complete functional regeneration. In this study, we tried to control the regeneration of cementum and PDL by using a human PDL stem cell (hPDLSC) sheet engineering technology with the pretreatment of recombinant human BMP-2 (rhBMP-2). Isolated hPDLSCs obtained from extracted human teeth were pretreated with rhBMP-2 for in vitro osteogenic differentiation and grafted on the micro/macro-porous biphasic calcium phosphate (MBCP) blocks, which represent dental roots. The MBCPs with hPDLSC sheets were implanted in the subcutaneous layer of immune-compromised mice, and rhBMP-2 pretreated hPDLSC sheets showed higher mineralization and collagen ligament deposition than the no-pretreatment group. Therefore, the rhBMP-2-hPDLSC sheet technique could be an effective strategy for the synchronized regeneration of two different tissues: mineralized tissue and fibrous tissues in periodontal complexes.

Plasticity, dynamics, and inhibition of emerging tetracycline resistance enzymes.

Although tetracyclines are an important class of antibiotics for use in agriculture and the clinic, their efficacy is threatened by increasing resistance. Resistance to tetracyclines can occur through efflux, ribosomal protection, or enzymatic inactivation. Surprisingly, tetracycline enzymatic inactivation has remained largely unexplored, despite providing the distinct advantage of antibiotic clearance. The tetracycline destructases are a recently discovered family of tetracycline-inactivating flavoenzymes from pathogens and soil metagenomes that have a high potential for broad dissemination. Here, we show that tetracycline destructases accommodate tetracycline-class antibiotics in diverse and novel orientations for catalysis, and antibiotic binding drives unprecedented structural dynamics facilitating tetracycline inactivation. We identify a key inhibitor binding mode that locks the flavin adenine dinucleotide cofactor in an inactive state, functionally rescuing tetracycline activity. Our results reveal the potential of a new tetracycline and tetracycline destructase inhibitor combination therapy strategy to overcome resistance by enzymatic inactivation and restore the use of an important class of antibiotics.

Penetration of a bubble through porous membranes with different wettabilities.

Porous structures with various surface wettabilities have been used to handle gas bubbles underwater for practical applications, such as separation, collection, detachment, and migration of the bubbles. Despite the increasing interest in porous structures, the effects of surface wettability on the behaviors of bubbles at porous surfaces have not been fully understood. Herein, we aim to examine the entire dynamics from collision to disappearance of a bubble through a porous membrane with different surface wettabilities. We divided the dynamics into three stages based on the characteristic behaviors such as bubble bouncing and contact line variation. Bubble dynamics is dominated by the existence of air layers covering the membrane surface. Bubbles on hydrophilic and hydrophobic membranes, which do not retain air layer, show the same removal pattern; they bounce on the surfaces, and then penetrate the membranes with pinned and moving contact line in sequence. In contrast, bubbles immediately penetrate the superhydrophobic membrane following the spread along the air layer. The characteristic time for bubble removal depends on the wettability, which affects the membrane permeability. The experimental characterization and theoretical analysis achieved in this work would improve the physical understanding of bubble dynamics on porous membranes and allow a proper design in bubble-related applications.

Efficacy of Pharmacotherapy for Smoking Cessation in Adolescent Smokers: A Meta-analysis of Randomized Controlled Trials.

INTRODUCTION: This study aimed to evaluate the efficacy of pharmacotherapy for smoking cessation among adolescent smokers by using a meta-analysis of randomized controlled trials (RCTs). METHODS: PubMed, EMBASE, and Cochrane Library were searched from the inception to January 20, 2018. We included RCTs of pharmacotherapy for smoking cessation among adolescent smokers aged less than 20 years. Data were pooled using a random-effects meta-analysis. The primary outcome measures were a smoking abstinence rate and its relative risk (RR) at the longest follow-up period in each study validated by biochemical markers. RESULTS: Among a total of 1035 articles searched, nine RCTs, which involved 1188 adolescent smokers aged 12-20 years with 627 in the intervention group and 561 in the control group, were included in the final analysis. In the random-effects meta-analysis of all the nine trials, pharmacotherapy showed a increased abstinence rate (RR = 1.62; 95% confidence interval [CI] = 1.08 to 2.44, I2 = 0.0%), compared with the control group. Subgroup meta-analyses by follow-up period showed an increased abstinence rate at 4 weeks (RR = 1.87; 95% CI = 1.22 to 2.87; n = 4) and a nonsignificantly increased abstinence rate during the longer term follow-up periods at 8, 12, 24, and 52 weeks. CONCLUSIONS: The current meta-analysis suggests that pharmacotherapy can be considered as an aid for smoking cessation in the short-term period among adolescent smokers. However, further large RCTs are warranted to determine its long-term efficacy and safety. IMPLICATIONS: In this meta-analysis of nine RCTs with 1188 adolescent smokers aged 12-20 years, pharmacotherapy showed an increased abstinence rate, compared with the control group. In the subgroup meta-analyses by follow-up period, it showed the increased abstinence rate at 4 weeks and no efficacy on abstinence during the longer term follow-up periods up to 52 weeks. Further large RCTs are warranted to determine the long-term efficacy and safety of pharmacotherapy in adolescent smokers.