The COVID-19 era has been characterized by the politicization of health-related topics. This is especially concerning given evidence that politicized discussion of vaccination may contribute to vaccine hesitancy. No research, however, has examined the content and politicization of legislator communication with the public about vaccination during the COVID-19 era.The aim of this study was to examine vaccine-related tweets produced by state and federal legislators during the COVID-19 era to (1) describe the content of vaccine-related tweets; (2) examine the differences in vaccine-related tweet content between Democrats and Republicans; and (3) quantify (and describe trends over time in) partisan differences in vaccine-related communication.We abstracted all vaccine-related tweets produced by state and federal legislators between February 01, 2020, and December 11, 2020. We used latent Dirichlet allocation to define the tweet topics and used descriptive statistics to describe differences by party in the use of topics and changes in political polarization over time.We included 14,519 tweets generated by 1463 state legislators and 521 federal legislators. Republicans were more likely to use words  and topics  that were focused on the successful development of a SARS-CoV-2 vaccine. Democrats used a broader range of words  and topics  that were more aligned with public health messaging related to the vaccine. Polarization increased over most of the study period.Republican and Democratic legislators used different language in their Twitter conversations about vaccination during the COVID-19 era, leading to increased political polarization of vaccine-related tweets. These communication patterns have the potential to contribute to vaccine hesitancy. As of December 2021, the COVID-19 pandemic has resulted in over 45 million infections and 780,000 deaths in the United States .It has been hypothesized that this geographic variation in vaccination may be the result of the politicization of public health topics during the COVID-19 pandemic . Survey The politicization that has characterized the COVID-19 era is especially concerning because there is evidence that politicized vaccine-related communication may contribute to vaccine hesitancy . For exaThe existing research has established partisan differences in the way that political figures communicated with the public about SARS-CoV-2 -15. MuchWe previously found that the arrival of the COVID-19 pandemic was associated with a dramatic increase in the volume of legislator’s vaccine-related tweets . Here, wWe used Quorum, a public affairs software platform that stores policy-related documents, to gather all vaccine-related tweets produced by state or federal legislators between February 1, 2020, and December 11, 2020. We defined February 1, 2020, as the arrival date of COVID-19 in the United States based on the United States’ declaration of a public health emergency  and restriction of global air travel  ) contained a COVID-19–related term, and 11.8% of tweets (n=1706) referenced a non–COVID-19 infectious disease . The included tweets were generated by 1984 unique legislators. The majority of the included legislators were state representatives (73.7% [n=1463]) as opposed to federal representatives (26.3% [n=521]). In terms of political party, 63.7% (n=1264) of the included legislators were Democrats, 35.1% (n=696) were Republicans, and 1.2% (n=24) were independent or undesignated.P<.001). Republican vaccine tweets were also less likely than Democratic vaccine tweets to reference a non–COVID-19 disease , and more likely to reference COVID-19 .Vaccine-related tweets generated by Republicans were less likely than vaccine-related tweets generated by Democrats to be retweets  in vaccine-related tweets included “operation warp speed,” “record time,” “innovation”, and “China.” Words and phrases more frequently used among Democrats (vs Republicans) included “anti-vaxxers,” “flu,” “communities,” “public health,” and “free” . To accoP<.001, and topics in each section are listed in order of decreasing magnitude of partisan difference).We included 20 topics in our final analysis -3. The tPolarized partisan communication decreased between February and April 2020 but increased for most of the study period (May through November) before trending down slightly during the first 11 days of December 2020. The increase in polarized communication was driven by several topics that demonstrated a widening gap in mean topic representation by political party over the study period. The topics that demonstrated a widening partisan gap with higher representation among Democrats included (1) President Trump; (2) influenza; (3) local, free non–COVID-19 vaccine clinics; and (4) state and local vaccine distribution plans. The topics that demonstrated a widening partisan gap with higher representation among Republicans included (1) Operation Warp Speed success and (2) COVID-19 relief package debate .Several topics demonstrated decreasing partisan gaps over the study period, including the impact of political pressure on vaccine safety. While Democrats were more likely to discuss this topic early on, Republican engagement with this topic increased to match that of Democrats toward the end of the study period . Topics We examined vaccine-related Twitter communication from state and federal legislators during the COVID-19 pandemic. We found that Republicans and Democrats used different words, phrases, and topics to discuss vaccination during the COVID-19 era. Republicans discussed vaccination using a narrow set of topics focused on progress toward the development of the SARS-CoV-2 vaccine. Democrats, on the other hand, were engaged in a more wide-ranging conversation covering a broad set of vaccine-related topics that were aligned with public health messaging related to the vaccine. We also identified patterns in legislator discussion of vaccination  that have the potential to contribute to SARS-CoV-2 vaccine hesitancy.The language used by Republican legislators about vaccination during the COVID-19 era was narrowly focused on the successful development of a SARS-CoV-2 vaccine. This was illustrated in both the keywords  and topics  that were associated with Republicans. Overall, fewer topics were associated with Republicans, and the keywords used by Republicans were more highly partisan than those used by Democrats. Both findings are consistent with the use of more focused, consistent messaging in the Republican party. In addition, Republicans were more likely than Democrats to explicitly reference COVID-19 in their tweets and were almost half as likely as Democrats to discuss vaccination for non–COVID-19 infectious diseases. This is consistent with our previous paper in which we demonstrated that, prior to COVID-19, Republican legislators were only minimally engaged in Twitter discussion about vaccination, but their engagement increased markedly with the arrival of the pandemic . We hypoDemocrats used a broader set of topics to discuss vaccination during the COVID-19 era. Democrats were more likely to tweet about non–COVID-19 infectious diseases and tweeted about a larger number of topics than Republicans. They used a wide range of keywords  and topics  to discuss vaccination. These topics were also more consistent with COVID-19–related public health messaging in the lay and academic press, much of which discussed vaccine affordability, the ongoing importance of non–COVID-19 vaccines, vaccine distribution and access, and concerns about vaccine hesitancy -30. The In this study, we also described patterns of vaccine-related communication from legislators that have the potential to contribute to vaccine hesitancy. The COVID-19 pandemic created an opportunity for either (1) mobilization of political leaders around a shared understanding of the importance of the vaccine or (2) an increase in polarization of the already politically polarized topic of vaccination. While there was a nadir in polarization of vaccine-related communication early in the pandemic (April 2020), the bulk of the study period was notable for increased polarization among federal and state legislators. This finding is concerning given literature suggesting that polarization in vaccination discussion may contribute to vaccine hesitancy . PreviouIn addition to the rise in politically polarized communication during the study period, we also noted the emergence of topics that have been associated with mistrust of vaccines. For example, the topic “Impact of political pressure on vaccine safety” was initially primarily discussed by Democrats. However, by the second half of the pandemic, Republicans had joined the conversation, and the topic was again increasing in mean representation. This finding is concerning given experimental evidence that suggests that exposure to this topic may be associated with decreased belief in the importance of the COVID-19 vaccine . Survey The use of natural language processing methods for monitoring politicians’ communication may have implications for improving the quality of public health-related messages on Twitter. This is especially relevant given the increasing pressure on social media platforms to monitor public officials’ discourse following President Trump’s use of misinformation during the COVID-19 pandemic and eventual deplatforming . The cloWhile Twitter has been used to study legislator communication about COVID-19, to our knowledge, this is the first study to examine how legislators used Twitter to communicate with the public about vaccination in the COVID-19 era . Other sRepublican and Democratic legislators engaged in substantively different conversations about vaccination on Twitter during the COVID-19 era, which led to an increase in political polarization of vaccine-related tweets throughout much of the pandemic. Republicans were engaged in a focused conversation about the successful development of a vaccine, and Democrats used a broader range of topics, which was more consistent with public health messaging about vaccination. These patterns have the potential to contribute to vaccine hesitancy, and future research is needed to determine the real-world impact of political communication on COVID-19 vaccine uptake.


In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers  solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of  The BLADE sequence  magnetic resonance imaging (MRI)  or MultiPreviously, BLADE MRI was noted to exhibit less sharp imagery than Cartesian k-space acquisition ,9, as a However, image reconstruction of the non-Cartesian data has been challenging, since no simple direct inverse transform is available. Many regularization methods have been proposed. For instance, over-complete sparse transforms, i.e., the stationary wavelet transforms, may be implemented to overcome the common blocky or oil-painting artifacts, but it requires significant effort to implement the complex algorithms. Recent optimization algorithms could be used to solve L1 minimization problems, e.g., the alternating direction method of multipliers (ADMM) , the fasApart from recent dedicated regularization methods, image reconstruction using efficient total-variation (TV) regularization is obstructed by concerns about the suitability of TV for clinical MRI. Apparent stair-casing or oil-painting artifacts have been identified in TV regularization ,15. HoweFurthermore, a crucial factor in parallel imaging is the smoothness of the coil estimation methods, which greatly influence the quality of the complex-valued imagery. Phase singularity has appeared in the recent literature, such as deep-learning frameworks  or phaseThis study is a continuing effort to bridge the gap between recent methodological developments ,35 and iIndex tensor notation has provided a handy tool for annotating multi-dimensional operators beyond second order matrices. The adoption of the multi-dimensional indexed tensors is useful to the non-Cartesian MRI acquisition modes. In this study, we investigated the index tensor notation for non-Cartesian BLADE MRI encoding method. The technical details are outlined in The in vivo study was approved by the regional institutional review board . Data were retrieved from patients who received head scanning for clinical indications but with negative findings, excluding patients with positive findings such as traumatic injury, tumor, stroke or infectious diseases. Patients’ genders, ages and identifiable personal information were removed after data acquisition because this minimized the risks of data breach and it is not necessary to store the information for image quality assessment. The experimental design of this study is illustrated in wixtools .The computer simulation tested the single-coil image reconstruction to evaluate the numerical performance of different algorithms, using a sagittal T1-weighted head MRI as a ground truth image. The k-space (Fourier domain) data were generated from a high-resolution T1 weighted turbo-spin echo sagittal brain MRI using the Python non-uniform fast Fourier transform (PyNUFFT) package . Images The coil sensitivity profiles were computed from the center of k-space. The RSS method divides the complex-valued low-resolution images by the root-sum-squared of all channels. The power iteration implements the method in , which fWe applied the above coil sensitivities generated by RSS, power iteration, and tensor iteration to image reconstruction. The parameters of LSMR were 100 iterations without a damping factor because applying a damping factor (the L2 regularization) on the image incurs the risk of over-suppressing the components with high noise levels. The L1TV-LAD were based on Equation (A15) using the split-Bregman method  and LAD In vivo BLADE rawdata allow parallel imaging reconstruction to be performed. The tensor iterations are performed around the center of the k-space trajectory. This is simply performed by using the NUFFT and then multiplying the data with the sampling density compensation function with the mask function, and an adjoint operation of the NUFFT. The eigenvector of each voxel was normalized to the L2 norm along the coil dimension.We performed 100 iterations for LSMR and the L1TVLAD algorithms. The LSMR algorithm uses the hybrid CPU-GPU computing method and the GPU acceleration component is encapsulated in a PROPELLER2D_gpu class. The L1TV-LAD algorithms performed all the iterations on the accelerator with minimal data transfer between GPU and the host. A rawdata set of 22 slices and 6 coils was reconstructed within 1 min of computation times by 4 GPUs  with 3584 cores, 1417 MHz–1531 MHz, 12 GB 384-bit GDDR5X memory). The reconstruction time of the vendor-provided algorithm was completed after data acquisition without delays, making a post-acquisition evaluation impossible, though an in-house developed sampling density compensation algorithm can compute the result within 5 s on a single CPU core.Two board-certified radiologists  separately evaluated the reconstructed MRI DICOM series with viewing software  and DICOM-compliant monitors , allowing readers to adjust the imaging window, level or zoom. The reconstruction algorithms were hidden from the radiologists, and they could not identify any algorithms based on the order of their appearance. To randomize the images, all the DICOM series and the vendor-provided T2 BLADE DICOM series were anonymized and were given random Service-Object Pair (SOP) Instance Unique Identifier (UID) Attributes. Therefore, the radiologists could not have been influenced by the order in which the data appeared. Due to the limited capacity to exhaust all the regularization methods, the L1TV-OLS reconstruction method was excluded from the clinical evaluation because of the obvious stair-casing and oil-painting artifacts noted in the pilot study and the literature. The method of scoring in the literature was modified  but we aAbsolute image scores  were evaThe rationale of the experimental design (the absolute and the relative scores) was that, while the relative image scores gave a detailed evaluation of the subtle differences in image quality, these subtle visual differences could also be reflected in clinical values. Thus, readers were allowed to quantify the visual image quality and the perceived clinical values separately. Finally, two scores were used to represent separately the diagnostic ability and the difference in visual perception. Inter-rater reliability and repeated measure analysis of variance (ANOVA) were used to compare the scores given by the two readers.As shown in Because the approved ethical protocols in the clinical study did not require patients to move their heads, a simulation study was performed to simulate the case of partial k-space coverage with missing blades. In this simulation, a partial k-space  was generated from a T1 weighted sagittal brain MRI. Nearly all five BLADE MRI imaging sets show good clinical diagnostic potential except for subtle differences in the zoom-ins . Howeverp-value < 0.001) in the absolute and relative scores rated by two readers. Cluster analysis using post hoc pairwise ANOVA shows that the scores with the least-squares method were significantly lower than those with the vendor-provided image  in the absolute and relative scores. Insignificant differences between the different regularization strength values of L1TV-LAD  of the receiver operating characteristic (ROC) curve of the disease could not be measured in this study. Although we did not explore the influence of patient demographics , the principle of using BLADE MRI remains unchanged regardless of patient demographics and diseases. It is likely that similar reconstruction parameters could apply to most patient groups. In future studies, it will be essential to collect data on specific diseases with positive MRI findings, from which statistical parameters can be derived.Future study involves evaluating statistical measures, such as sensitivity, specificity, the AUC/ROC curve for brain diseases and other locations. Since PROPELLER MRI has been used in musculoskeletal imaging, liver imaging, lung imaging and body imaging, this algorithm may enhance the image quality of static BLADE MRI. Moreover, subtle pathology may be better identified without being overshadowed by noise. Because the reconstruction apparatus is a separate module outside the pulse sequence, the T2 FLuid Attenuated Inversion Recovery (T2-FLAIR) can be developed further. Beyond the iterative reconstruction method, image reconstruction may be accelerated by leveraging recent machine learning frameworks . NonetheWe demonstrated the image quality of the L1TV-LAD algorithm for BLADE MRI is better than those of the vendor-provided image and the least-squares, but both readers reported that the noisy images were still of diagnostic value.


Young adults’ drinking habits commonly exceed recommendations for low-risk drinking, which may have a negative effect on their mental, social, and physical health. As smartphones are highly accessible to young adults, mobile apps could be used to support young adults to develop low-risk drinking habits and improve their general health.The objective of this study is to evaluate the effectiveness of Boozebuster, a self-guided mobile app based on healthy lifestyle–related components that aim to develop and maintain low-risk drinking habits among young adults.This two-arm, parallel-group randomized controlled trial will investigate whether a 6-week self-guided mobile intervention (Boozebuster) targeting drinking behavior is more effective than a minimal intervention consisting of an educational website on alcohol use and its consequences for young adults. We will recruit 506 young adults (aged 18-30 years) from the Netherlands via an open recruitment strategy by using an open access website. All outcomes will be self-assessed through questionnaires. The primary outcome is the quantity and frequency of alcohol consumption in standard drinks (10 g ethanol per standard drink) per month (timeline follow-back [TLFB]). Secondary outcomes include binge-drinking sessions per month, alcohol-related problem severity (Rutgers Alcohol Problem Index), cannabis use frequency and quantity in grams (TLFB), depressive symptoms , perceived stress , engagement , readiness to change (Readiness to Change Questionnaire), mental well-being , trauma and COVID-19–related trauma , impulsivity , study or work performance , and treatment adherence. Baseline (T0), 6-week postbaseline (T1), and 3-month postbaseline (T2) assessments will be conducted and analyzed on the basis of the intention-to-treat principle using multilevel mixed modeling analyses.Recruitment began in September 2020. We received 933 registrations via our study information website; 506 participants have completed the T0 assessment, 336 participants have completed the T1 assessment, and 308 participants have completed the T2 assessment as of May 2021. The study is still in progress, and results will be reported in 2021 and 2022.Self-guided mobile interventions based on a lifestyle approach might be an attractive approach for young adults due to their preference on self-reliance, healthy living, and increased perceived anonymity. Such interventions are yet understudied, and it is known that interventions addressing solely problem drinking are less appealing to young adults. We hypothesize that the Boozebuster mobile app will effectively reduce drinking levels compared to an alcohol educational website (control condition). If effective, our intervention could be an inexpensive and scalable public health intervention to improve drinking habits in young adults.Netherlands Trial Register NL8828; https://www.trialregister.nl/trial/8828DERR1-10.2196/29750 It is well known that the prevalence of alcohol consumption among young adults is high and that their drinking habits commonly exceed low-risk drinking guidelines . A studyAccording to the literature, face-to-face interventions delivered in individual or group settings targeting young adults’ drinking patterns are effective ,6. HowevAmple digital interventions that are aimed to reduce drinking levels in young adults, mostly in university settings located in the United States or Australia, have been previously evaluated. The majority of these interventions consist of internet-based single-session interventions, based on personalized normative feedback (PNF), that aim to correct erroneous perceptions of peer drinking levels by assessing personal drinking levels and consequently comparing them with drinking levels in similar-aged peer groups ; generalThe majority of the above-described digital interventions are delivered via a computer or web browser. However, it has been claimed that mobile apps are more suitable for young adults given the flexibility, interactivity, and their spontaneous nature . HoweverAlthough single-session interventions can reach a high number of individuals due to their appealing, limited time requirement, they are associated with a few important disadvantages such as the relatively small effect sizes produced and the limited longevity of those effects. For example, meta-analyses assessing single-session interventions show small effect sizes when compared to assessment only, attention-matched, or active controls ranging from g=0.18 to g=0.29 ,14-18. ITherefore, in our intervention, we decided to incorporate relevant lifestyle components that have been shown to be perceived as relevant and important for our target group’s general well-being -27. SuchWe will conduct a two-arm, parallel-group randomized controlled trial (RCT) comparing a 6-week mobile app (Boozebuster) with an active educational website control condition. Participants in the Boozebuster condition will receive access to the self-guided mobile app; participants in the control condition will receive access to an educational website containing information about the effects and consequences of alcohol use on health. The Scientific and Ethical Review Committee of the Faculty of Behavioral and Movement Sciences of the Vrije Universiteit Amsterdam has approved the study protocol, informational letter, and informed consent process. The primary and secondary outcomes in addition to the exact timepoints at which each measure will be applied is presented in Young adults will be eligible for the RCT if they meet the following criteria: (1) aged between 18 and 30 years, (2) willingness to develop healthy lifestyle behaviors and low-risk drinking habits, (3) provision of informed consent digitally, (4) proficiency in reading and writing in Dutch, (5) access to an Android or IOS device with connection to the internet, and (6) possession of an email address. We did not apply a minimum drinking severity inclusion criterion. Our reasoning for not screening drinking severity is based on the fact that the Dutch drinking guidelines recommend an alcohol consumption level of zero or no more than 1 alcoholic drink per day.We will make use of an open recruitment strategy, specifically, by means of advertisements on social media and health-related websites and via posters on educational facilities. These advertisements contain a link to our study information website . InteresFirst, participants will be screened, asked to fill out the baseline assessment (T0), and then randomized into either the mobile intervention or the educational website group. The allocation sequence will be automatically generated via the computerized Castor EDC system by using a 1:1 allocation ratio with random block sizes of 4 and 6, which also ensures allocation sequence concealment. Participants will be informed about the outcome of the randomization via email by a member of our research team. Initial stratification will be into two groups according to gender . Within these two groups, participants will be further stratified into two groups categorized by adherence or nonadherence to the Dutch drinking guidelines. Nonadherence will be defined as the consumption of more than 7 drinks per week or at least one binge-drinking session  in the past 30 days. This will create four strata. Because of the nature of our study design, blinding of group allocation for participants is not possible. Previous meta-analyses assessing drinking reduction in young adults compared to assessment only, attention-matched, or active controls ,18 foundBoozebuster is a mobile app aimed at developing low-risk drinking habits in young adults according to the Dutch drinking guidelines. It has been developed by Vrije Universiteit Amsterdam in collaboration with the Institute for Systems and Computer Engineering, Technology and Science . BoozebuBoozebuster focuses on the importance of the individuals’ motivation, self-efficacy, and environmental constraints with regard to drinking. The behavioral change techniques used include PNF , motivatBoozebuster contains a total of seven modules and was developed with the following rationale in mind: the PNF module serves as the first assessment of young adults’ drinking patterns and compares it with peer norms and the official Dutch drinking guidelines . This prParticipants in the Boozebuster group will be able to monitor their daily alcohol consumption via ecological momentary assessments (EMA)  in termsParticipants are free to use Boozebuster during the 6-week study duration as frequently as desired. The reason we opted for a 6-week period is because it is the recommended timeframe in which alcohol-related changes can occur and be measured . Hence, Participants in the control condition will receive access to an educational website containing information on the effects and consequences of alcohol use on health. Only participants allocated to the control condition will have access to this website.Given that applied outcome measures in the alcohol intervention literature vary considerably we decided to follow the advice and included key outcomes based on the recommendation of the Outcome Reporting in Brief Intervention Trials on Alcohol (ORBITAL) framework .The primary outcome is the quantity and frequency of alcohol consumption in terms of standard drinks (10 g ethanol per standard drink) per month; this will be assessed through the timeline follow-back (TLFB) method , which mIntervention adherence will be calculated descriptively based on the percentage of participants who completed the main PNF and motivational interviewing module.The Readiness to Change Questionnaire (RCQ)  is a 12-Participants will be asked to indicate the number of binge-drinking days defined as 4  or 5  standard drinks on one drinking occasion over the past 30 days.Alcohol-related problem severity will be assessed via the Rutgers Alcohol Problem Index (RAPI) . The RAPCannabis use frequency was assessed using the TLFB method . FurtherDepressive symptoms will be assessed via the Center for Epidemiological Studies Depression (CES-D) scale , a 20-itThe Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS)  is a 14-The Perceived Stress Scale (PSS)  is a 10-The task performance scale is a 5-item self-report scale that measures study or work performance and is part of the Individual Work Performance Questionnaire . The totEngagement will be measured via the Twente Engagement with eHealth Technologies Scale (TWEETS) , a 9-iteThe short-form version of the posttraumatic stress disorder (PTSD) Checklist for Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), or PCL-5 , is a 4-Given the unprecedented situation that emerged due to the global COVID-19 pandemic, we decided to include a modified version of the short-form PCL-5 that measures only stressors tied to the COVID-19 pandemic. The short-form PCL-5 – COVID-19 scale that we used for this study is identical to the previously introduced short-form PCL-5; however, we specifically asked participants to indicate on this scale only stressors that are associated with the COVID-19 pandemic.The reason we decided to include this measurement tool is due to the fact that impulsivity is a significant risk factor for the initiation, continuation, and problematic alcohol consumption . ImpulsiAt baseline, sociodemographic variables such as sex, age, level of education, employment, and marital status will be assessed.d values will be used as the measure of effect size. Effect sizes of 0.8 are assumed to be large, effect sizes of 0.5 are assumed to be moderate, and effect sizes of 0.2 are assumed to be small [After the data collection period, we will clean our data and assess it for accuracy and completeness. Missing data will be handled using either multiple regression imputation techniques or full information maximum likelihood estimation, depending on the amount of missing data and whether it is missing at random or not. All analyses for the primary and secondary outcomes will use linear mixed modeling regression analyses with the three time points nested within participants. For continuous outcomes, linear models will be used for normally distributed data and negative binomial models, for left-skewed data. For dichotomous outcomes, such as whether or not participants had a binge-drinking episode in the past 30 days, binary logistic regression will be used. All primary analyses will be conducted in line with the intention-to-treat (ITT) principle . Sensitivity analyses will be conducted in line with the per-protocol principle  and with study completers only. All statistical tests use a significance level α=.05. Time will be used as predictor and baseline measures will be included as covariates if appropriate. All analyses will be conducted using the R Statistical Package. Cohen be small . In addibe small , readinebe small , engagembe small , and impbe small  will be Our study began recruitment in September 2020. We received 933 registrations on our study information website. Of these registrations, 506 participants have completed the baseline assessment, 336 participants have completed the 6-week postbaseline assessment (T1), and 308 participants have completed the 3-month postbaseline assessment (T2) as of May 2021. The study is still in progress. Results will be reported in 2021 and 2022.The majority of digital interventions aimed at reducing drinking levels in young adults were conducted in university settings in the United States or Australia, were delivered via a computer or web browser, and consisted of single-session interventions showing small effect sizes ranging from g=0.18 to g=0.29 ,14-18. MTo potentially enhance the effectiveness of such interventions, we decided to develop a self-guided mobile app supporting low-risk drinking habits among young adults from the general population, while incorporating relevant healthy lifestyle-related support components that may increase and prolong the effectiveness of our intervention but retain the acceptability and reach of single-session interventions -24.Therefore, the present study aims at improving the existing knowledge on the effectiveness of mobile apps on alcohol reduction in young adults by employing lifestyle-related support components . If our intervention proves to be effective, it could become an inexpensive and scalable public health intervention to improve drinking habits in young adults.Our study has a number of strengths such as the delivery of our intervention via the use of a mobile app, which seems to be a feasible and desirable delivery method for our target group ,75. SecoNevertheless, there are some limitations that are worthwhile mentioning. We are measuring all outcomes via self-report measures, and we have decided against using lengthy and costly diagnostic interviews in order to reach out to a large sample group and due to the preventive nature of our intervention. However, based on the literature, we know that self-report measures are reliable for actual drinking levels in our target population ; furtherProviding young adults with a mobile app that promotes low-risk drinking habits while also providing lifestyle-related support components might be a promising approach to increase the reach, uptake, and effectiveness of behavioral change interventions targeting alcohol reduction in this population. We expect that the use of our mobile app will reduce drinking outcomes significantly compared to the use of the educational website. In addition, findings of the trial will be informative for future researchers developing interventions for young adults with regard to the reduction of alcohol consumption and improvement of associated lifestyle behaviors.


Hepatocellular carcinoma is a highly deadly primary liver cancer. It is usually diagnosed at a late stage, when therapeutic options are scarce, and the lack of predictive biomarkers poses a challenge for early detection. A known hallmark of hepatocellular carcinoma is the accumulation of misfolded proteins in the endoplasmic reticulum (ER), known as ER-stress. Growing experimental evidence suggests that ER-stress is involved in liver cancer initiation and progression. However, it remains unclear if ER-stress markers can be used as therapeutic targets or biomarkers for patients with liver cancer. In this study, we evaluated the prognostic value of proteins involved in managing ER-stress in liver cancer by mining a publicly available patient-derived database, the Human Protein Atlas. We thereby identified 44 ER-stress-associated proteins as prognostic markers in liver cancer. Furthermore, we discussed the expression of these markers in relation to disease stage, age, sex, ethnicity, and tissue localization.Endoplasmic reticulum (ER) stress and actors of unfolded protein response (UPR) have emerged as key hallmarks of hepatocarcinogenesis. Numerous reports have shown that the main actors in the UPR pathways are upregulated in HCC and contribute to the different facets of tumor initiation and disease progression. Furthermore, ER-stress inducers and inhibitors have shown success in preclinical HCC models. Despite the mounting evidence of the UPR’s involvement in HCC pathogenesis, it remains unclear how ER-stress components can be used safely and effectively as therapeutic targets or predictive biomarkers for HCC patients. In an effort to add a clinical context to these findings and explore the translational potential of ER-stress in HCC, we performed a systematic overview of UPR-associated proteins as predictive biomarkers in HCC by mining the Human Protein Atlas database. Aside from evaluating the prognostic value of these markers in HCC, we discussed their expression in relation to patient age, sex, ethnicity, disease stage, and tissue localization. We thereby identified 44 UPR-associated proteins as unfavorable prognostic markers in HCC. The expression of these markers was found to be higher in tumors compared to the stroma of the hepatic HCC patient tissues. Hepatocellular carcinoma (HCC) is a the most common type of primary liver cancer and usually develops in a background of chronic liver disease and inflammation. With global HCC incidence and mortality consistently rising, advanced screening programs, novel predictive biomarkers, and new therapeutic options for advanced HCC are desperately needed . FurtherThe unfolded protein response (UPR) is a conserved cellular survival strategy which is activated in response to the accumulation of unfolded or misfolded proteins in the endoplasmic reticulum (ER) lumen, an event known as ER-stress. Factors such as pathogens, mutations, nutrient deprivation, hypoxia, and an increased metabolic rate lead to an increase in the cell’s protein secretory load. This impairs the ER’s capacity to accurately monitor protein folding and synthesis, which induces UPR signaling through three major ER transducers—IRE1α, PERK, and ATF6—with the goal of reestablishing ER homeostasis. However, sustained ER-stress and prolonged UPR signaling can activate pro-apoptotic signaling pathways ,3.Chronic ER-stress and increased UPR activity have long been reported as the hallmarks of solid tumors, including HCC. Upregulated UPR signaling has been noted in the majority of human HCCs, irrespective of grade or stage . Our groA gene-set of 129 proteins involved in the UPR was generated from online databases: the Harmonizome  and STRIPatient survival was correlated to the expression levels of the selected 129 UPR-associated markers by using publicly available data from the Human Protein Atlas. Patients were classified into two expression groups based on the fragments per kilobase of exon per million reads (FPKM) value of each gene. The expressions of UPR-associated markers in the different tumor stages of HCC were taken from the Human Protein Atlas database using the RNA sequence data from 365 patients derived from the Cancer Genome Project .Images of healthy livers and HCC biopsies stained with antibodies against the UPR-associated unfavorable HCC prognostic markers were derived from the Human Protein Atlas. Five replicates stained with the antibody with the strongest staining intensity were selected for each marker. The staining intensity was then scored on a scale from 0 to 3, with 0 corresponding to no staining detected, 1 to weak staining, 2 to moderate staining, and 3 to the strongest staining intensity on healthy, stromal, and tumoral tissues. The biopsy staining was scored by the authors independently, after which the average scores were generated.p-value. Statistical significance between the groups of the Kaplan–Meier curves was determined with a log-rank test. p-values of <0.05 were considered statistically significant.The dataset derived from the Human Protein Atlas was imported into GraphPad Prism 8. The statistical significance between different experimental groups  was determined through a two-way ANOVA using the Dunn–Šidák correction for multiple comparisons. A volcano plot was generated after performing a multiple t-test on the different stages of HCC in order to visualize the ER-stress genes with the highest fold-change and statistically significant differences. The correlation between the biomarker expression level and patient survival was examined using GraphPad Prism 8. Based on the data from the Human Protein Atlas, the FPKM value of the appropriate gene was used as a cut-off to determine high or low expression. The FPKM/cut-off value corresponded to the best expression cut-off level, meaning that the FPKM value that would yield the maximum difference in regard to survival between the two groups at the lowest log-rank A systematic overview of UPR-associated proteins as prognostic markers for HCC was performed using the publicly available Human Protein Atlas database . This seCancer progression and treatment efficacy are known to be impacted by different host characteristics, including patient sex and age . FurtherMetabolic and oncogenic abnormalities in both tumor cells and the surrounding stromal microenvironment are known inducers of ER-stress . HCC is The actors of the UPR have been widely demonstrated to influence different facets of HCC progression. While numerous preclinical models have proposed ER-stress inhibitors and inducers as a promising therapeutic strategy for patients with liver cancer, the translational potential of targeting UPR signaling is still unclear. In this study, we investigated the prognostic value of UPR-associated proteins in liver cancer through mining the Human Protein Atlas database. Furthermore, in an effort to potentially identify a subpopulation of liver cancer patients that could benefit from ER-stress-targeted therapy, the expression of these markers was evaluated in relation to patient sex, age, ethnicity, and disease stage. The UPR-associated protein expression was also discussed in regard to its hepatic tissue localization.Through mining the Human Protein Atlas, 44 UPR-associated proteins were identified as unfavorable prognostic markers in HCC. While the majority of the UPR markers that were found to be prognostic could not be categorized under one specific UPR branch, several observations could be made regarding their upregulation, correlating with poor HCC prognosis. For instance, unfavorable prognostic markers belonging to the PERK-mediated UPR pathway, such as eIF2α, ATF4, GADD34, and CHOP, have been widely reported as key regulators of HCC chemosensitivity and apoptosis in targeted HCC therapy. Notably, ATF4, CHOP, and eIF2 subunit gamma (EIF2S3) expression were found to significantly increase with the advancement of HCC to later stages, in line with the possible role of these markers in HCC chemoresistance ,42,43,44Receptor of activated protein kinase 1 (RACK1) was shown to play a crucial role in the activation of IRE1α signaling in response to the sorafenib treatment of HCC cells, as RACK1 overexpression led to the increased phosphorylation of IRE1α and enhanced XBP1 splicing, thereby protecting HCC cells from sorafenib-induced apoptosis . MoreoveSignal sequence receptor subunit 2 (SSR2) was found to be overexpressed in patient HCC tissue, with increased expression significantly correlating with disease progression. Hong et al. recently reported that SSR2was involved in the proliferation and invasion of HCC cells, as SSR2 knockdown was shown to suppress the epithelial mesenchymal transition (EMT) of HCC cells .Several homologs of the Hsp40/DnaJ protein family  have been identified as unfavorable prognostic markers in liver cancer. Wang et al. recently demonstrated a novel oncogenic function of DnaJC5 in promoting HCC cell proliferation by regulating the SKP2-mediated degradation of tumor suppressor p27 . FurtherNotably, our analysis identified one UPR-associated protein, phosphoinositide-3-kinase regulatory subunit 1 (PIK3r1), as a favorable prognostic marker in liver cancer. Winnay et al. reviously reported on an ER-stress-dependent interaction between PIK3r1, a key regulator of insulin action, and nuclear XBP1 accumulation, followed by UPR gene induction. This study demonstrated that PIK3r1 deletion in the liver attenuates adaptive UPR signaling and promotes inflammation . We coulOverall, the UPR-associated proteins that were identified as unfavorable prognostic markers in HCC in our study are in line with previous reports that confirm their contribution to ER-stress-mediated hepatocarcinogenesis and their potential as therapeutic targets in HCC. These findings therefore strengthen the evidence of their involvement in HCC progression and their translational potential for patients with liver cancer.When comparing the expression of the UPR-associated unfavorable prognostic markers in relation to patient sex, no notable differences were observed. This finding is in contrast to reports on the sexual dimorphism of ER-stress susceptibility in different in vivo models. Male prepubertal rats were shown to have a lower predisposition to hepatic ER-stress, while an increase in UPR marker expression in males was noted during the transition from a prepubertal to an adult age, which was strongly linked to the notable increase in testosterone levels in this phase . SimilarSimilarly, we did not observe significant differences when we compared the UPR marker expressions between the three different age groups, with the exception of the markers RACK1, ATF4, and CHOP. While an age-related decline in RACK1 expression has been noted in several studies on age-associated immunosenescence ,55, the When we compared the mRNA expression of the UPR markers in HCC between three ethnic categories , differences in expression were observed for the markers RACK1, ATF4, and SSR2. While a common feature of HCC is that it arises in the background of chronic liver disease and cirrhosis, dominant risk factors for HCC development differ between different ethnicities. In East Asia (with the exception of Japan) and Southeast Asia, these risk factors are the hepatitis B virus HBV and aflatoxin exposure, while in Europe and North America they include the hepatitis C virus and the metabolic syndrome . These dFinally, a notable trend of increased UPR marker protein expression in the tumorous hepatic tissue was observed compared to stromal compartments, where the expression was overall downregulated to the baseline levels of the healthy tissue. For instance, key ER-stress markers associated with HCC chemoresistance, such as ATF4, ERO1-α, EIF2S3, and CHOP, were found to have higher expression in tumoral tissue compared to the stromal compartment of the liver biopsies. These ER-stress regulators have also been proposed as therapeutic targets for inducing HCC apoptosis and sensitizing HCC cells to chemotherapy. Chronic UPR activation and ER-stress occurrence in tumor cells is known to be a result of an increased metabolic rate and a mutation-driven protein synthesis need . HoweverThis insight on the tumor/stroma distribution of UPR marker expression in HCC could prove relevant when assessing the use and development of nanotherapies targeted at these markers. Delivering the ER-stress inducers or inhibitors specifically to the cell-of-interest could be of great importance, as systemically targeting UPR pathways could have long-term undesired side-effects for patients.Broad experimental evidence from preclinical and patient studies highlights ER-stress as an important contributor to the hepatocarcinogenic process. While it is important to shed light on the molecular implications of ER-stress markers for the progression of HCC, there is a great need to explore the translational potential and the clinical relevance of these findings. By investigating the predictive value of ER-stress markers in HCC, we identified 44 proteins as unfavorable for patients with liver cancer. Through quantifying the distribution of the expression of these markers in patient HCC tissue as well as discussing the current literature on how these markers are involved in HCC pathogenesis, we provide insight into how the therapeutic value of targeting ER-stress in HCC can be utilized for the advancement of liver cancer prevention and treatment.


COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes  without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77. Early diagnosis and detection of pneumonia can minimize the risk factors of the illness2 and help break the transmission chain. The standard test for diagnosis of COVID-19 is reverse transcriptase polymerase chain reaction (RT-PCR)3. The lack of accessibility and slowness of RT-PCR, along with its high false negative rate (39–61%), drew attention to diagnosis of COVID-19 using chest radiographs5. Automation of COVID-19 diagnosis using chest X-rays can help healthcare systems keep up with demands for patients testing as X-rays are more readily available than RT-PCR and reduce strain from radiologists and healthcare systems. Medical imaging based diagnosis can also help control the high false negative rate of RT-PCR tests by acting as a secondary control. Computer-aided disease diagnosis using medical imaging techniques have accelerated over the past decade due to the breakthroughs in the field of machine learning and the development of detection and classification models that are based on convolutional neural networks (CNNs)8. CNNs, which are mainly used in supervised frameworks, require large amounts of labeled data to learn the task of anomaly detection, such as detecting COVID-19 in chest X-rays. Supervised architectures require training data with complete labels for all image classes . Nevertheless, this requires accurate labeling of the data for all cases and the cumbersome annotation effort, and the diagnosis variation amongst expert radiologists limits the performance of these supervised models on new data. Specially, in pandemics such as COVID-19, at the beginning, there is limited COVID-19 data  available for training a supervised classification model. In contrast, solutions based on semi-supervised learning only require partial labels for the training data9. Semi-supervised learning significantly reduces the cost of creating training data and, thus, opens new opportunities for automated disease detection using training data with only single class labels.COVID-19 spread globally over a short period of time and became a deadly pandemic10. Anomaly detection may be seen as only detecting abnormality in medical images such as a tumour or pneumonia. We extend the definition of anomaly in medical images as the deviation from the image-space manifold of training data. In other words, if the training data only includes COVID-19 negative cases , the anomaly detected in test cases is indeed an abnormality such as COVID-19. On the other hand, if the training data only includes COVID-19 positive cases, the “anomaly” detected in the test cases are the deviation from COVID-19 cases, meaning that the test case does not contain the abnormality in the training class . We show our proposed RANDGAN model is able to differentiate between COVID-19 positive and negative images. To the best of our knowledge, this study is the first of its kind, using semi-supervised learning for detection of COVID-19 in medical images and reporting performance accuracy on the entire cohort of COVID-19 positive images without the need to use any of the COVID-19 positive images to train our model. The code for our RANDGAN along with instructions to create the datasets used in this study can be found here; https://github.com/samxmot/RANDGAN.In this study, we propose a semi-supervised generative model  for detection of COVID-19 positive chest X-ray images. The idea behind anomaly detection using generative adversarial networks (GANs) comes from the great ability of generative models in learning the image-space manifold where training images lie on, and being able to generate never-before-seen images that lie on the learned image-space11 is an effort by Cohen et al. to make a public COVID-19 dataset of chest X-ray images with COVID-19 radiological readings. Wang et al. used covid-chestxray dataset, along with four other publicly available datasets and compiled the COVIDx12 dataset. With the number of images growing, many deep learning models are trained and tested on this public dataset14. Figure Covid-chestxray dataset17. Wang et al.’s CNN based COVID-NET12 achieved a 93.3% test accuracy for multi-class classification on a test cohort of 100 Normal, 100 Pneumonia and 100 COVID-19 images from the COVIDx dataset with the rest of the images of each class being used to train their model. Hemdan et al.’s COVIDX-Net16, comprised of multiple architectures such as VGG19, DenseNet121 and InceptionV3, was tested on a small set of 50 X-ray images from the covid-chestxray dataset; 25 COVID-19 positive and 25 COVID-19 negative. They reported accuracies of anywhere between 50% (InceptionV3) to 90% (VGG19 and DenseNet201) for each investigated architecture. Ozturk et al.’s DarkNet13 experimented with both binary classification (COVID-19 vs. No Findings) and multi-class classification (Pneumonia vs. COVID-19 vs. No Findings). They reported a binary classification accuracy of 98.08% and multi-class classification with accuracy of 87% on 25 COVID-19, 100 Normal and 100 Pneumonia images. Afshar et al. proposed using capsule networks for binary classification of COVID-19 positive and negative cases using COVIDx dataset, pre-trained on non-COVID chest X-ray images from other datasets. They reported an accuracy of Using the covid-chestxray and COVIDx datasets, multiple studies have utilized supervised deep learning models to detect COVID-19 in chest X-raysThe high accuracy achieved in these models, despite the imbalanced dataset with only 18 conducted a few experiments to test the generalizability and robustness of these models trained on the COVIDx dataset. Replicating the same underlying supervised model structure such as COVID-NET12 and training the model on COVIDx dataset, they achieved high test accuracy when tested on COVIDx data. Their predictive performance, however, dropped by 50% when they validated their model on an external COVID and Non-COVID dataset19 where the images were from a single institution. Furthermore, using saliency maps, which highlight the region of each X-ray image that contributed most to the classification decision of the CNNs, they found non-COVID markers such as image edges, diaphragm and cardiac silhouette have contributed to the classification of COVID-19; markers that do not have a predictive value for detection of COVID-1920. This confirms that using the full images from a dataset that comes from different scanners can be problematic where non-disease specific markers could act as a shortcut21 and help CNNs achieve high accuracy on a particular dataset yet fail to generalize to any other dataset. To minimize the effect of shortcuts, we created a segmented COVIDx dataset that includes only the lungs where the true markers of COVID-19 and Pneumonia appear.DeGrave et al.22, which contains 138 frontal chest X-rays from Montgomery County’s Tuberculosis screening program with corresponding masks manually annotated by radiologists. We resized the images to 23 based model, that has been augmented with inception and residual architectures, with these normalized images25. Transfer learning26 has shown promise in adapting tasks from one domain (source) to another (target). For the task of lung segmentation for the COVIDx dataset, the Montgomery dataset was used as the source and COVIDx dataset was used as the target domain. For the task of transfer learning, Sefexa27, an image segmentation tool, was used for manual segmentation of 900 randomly selected images (300 from each class) of the COVIDx dataset. All segmentation masks were corrected by an experienced radiologist and intentionally over-segmented to ensure no region of lung is excluded. Thus, these masks are best to be used for classification algorithms for detection of COVID-19 and pneumonia, and not for precise segmentation of lung boundaries. 850 segmented X-ray images from COVIDx were used for performing transfer learning24 from the Montgomery dataset to COVIDx. We kept 50 manual segmentations to evaluate our model’s accuracy. Since source domain is smaller than our target domain, we fine-tuned 75% of the pre-trained model’s layers (encoder part), and trained on the Montgomery dataset images. We froze the first 25% layers of the pre-trained U-NET and fine-tuned the rest of the encoder and decoder components based on our manual segmentation for COVIDX images. We used open and close operations as a post-processing step to fill any holes in the masks and reduce noise in the predicted masks. We tested the accuracy of our model using Sørensen–Dice coefficient (DSC). We achieved a DSC of 0.83 on our test set of 50 images.To mitigate the issue of deep learning models picking non-disease related markers from the images, we created a new dataset by segmenting lungs in COVIDx dataset. For the training set, we used the Montgomery County chest X-ray setFigure 10 revolutionized the field of deep learning by allowing generation of never-before-seen data that follows the distribution of real data. Applications of GANs have expanded from generating human-like faces, to image style transfer and detection of anomalies in images28. In the following, we describe the components of our proposed random input generative adversarial network (RANDGAN).Generative adversarial networks (GANs)G) . The trained Generator learns the mapping z to realistic, 2D, X-ray images. Our Generator model follows DCGAN’s architecture 28  with three main modifications; the use of randomized 2D image inputs to the generator, inception layers, and residual connections as shown in Fig. The Generator (G) Fig.  learns a30. Real images are encoded into a lower dimensional space before being concatenated with the noise input vector z. To improve generalizability of our generator, specially due to the multi-centric nature of COVIDx data, we randomly select batches of 32 images from the cohort of our training class and encode them to a lower-representation space using inception layers. This helps in adding variability to each iteration of the generator’s training by not only using a random noise vector, but also real, random image representations of the training class. Doing so shows improved results when using the trained GAN to classify images of an unknown class from other known classes. The idea behind the inception and residual architecture31 is being able to increase GAN’s ability to capture more details from training image-space without losing spatial information after each convolution and pooling layer. Although making the Generator deeper is theoretically a valid way to capture more long-range details in the image, deep GANs are unstable and hard to train32.Feeding real training images as an input to the generator has shown improvement in using GANs for augmenting imagesD)  Fig.  is a 4-l10 with the value function V:Optimization of D and G can be thought of as the following game of minimaxDuring training, Generator G is trained to minimize the accuracy of Discriminator D’s ability in distinguishing between real and generated images while the Discriminator is trying to maximize the probability of assigning real training images the “real” and generated images from G, “fake” labels. The Generator improves at generating more realistic images while Discriminator gets better at correctly identifying between real and generated images.16 and Ozturk et al.13 using 25 COVID-19 positive images to test their models, we used 573 images of each class; the entire dataset for COVID-19 was used and for normal and pneumonia classes, 573 images were randomly selected for each class. All images, converted from RGB to grayscale, were resized to We used both full images from COVIDx dataset and our segmentation of the COVIDx data to train separate models and compare the results. One of the advantages of our semi-supervised model compared to supervised models is the ability to test our model on not only a subset, but all of COVID-19 positive images as we do not use any of these images to train our model. While studies such as Wang et. al’s COVID-NET use 100 images of COVID-19, Hedman et al.Table 28 anomaly detection study. One RANDGAN/AnoGAN was trained using Normal images and the other RANDGAN/AnoGAN was trained using Pneumonia images. When the model’s training is done, the generator has learned the mapping z to realistic images. Given a query image x in test, we want to find a point z from the latent space such that, given the Generator’s output on that point (G(z)), that is most similar to the query image x. The expected behaviour after successful training is that the query image x, if affected by pneumonia, will result in finding an image G(z), which is visually closer to image x than if the query image was a normal case (given the GAN was trained on Pneumonia images).We trained two instances of our RANDGAN. For comparison, we repeated the same training using the GAN model (AnoGAN) used in Radford et al.’sz that generates the most similar image G(z) to the query image x, we used back propagation with a predefined number of steps. The loss function defined to find such z through back-propagation is comprised of two components; residual loss and discrimination loss. Residual loss (G(z) and the query image x and enforces visual similarity between the query image and generated image.33 proposed a discrimination loss (34 that enforces generated images f(.), is used to represent the statistical characteristics of the input image.A(x) for the query image x is defined as;33. Both architectures were trained with the same initial conditions for performance comparison.To find latent variable With two trained models, one on Normal and one on Pneumonia images, we calculate two anomaly scores for each test image. One anomaly score from inputting the test image into Normal trained GAN and one from Pneumonia trained GAN. The anomaly score generated from the Normal trained GAN will be lower for Normal test images compared to Pneumonia and COVID-19 images. Respectively, the anomaly score generated from Pneumonia trained GAN will be lower for Pneumonia test images compared to Normal and COVID-19 images. For each test image and the corresponding two anomaly scores, we generate a single anomaly score by summing the two scores together. The idea is that COVID-19 (unknown) images would score high anomalies from both networks while Normal and Pneumonia images score low in one model and high in the other. This should lead to the COVID-19 (unknown) images to score higher overall than the two other (known) classes.We generated a single anomaly score, comprised of two anomaly scores from the two trained models , for the images in our test set. 573 anomaly scores were computed for each class  of our COVIDx and segmented COVIDx dataset. To evaluate the performance of our COVID-19 positive detection model on a balanced test set, we randomly selected 286 Normal labeled and 287 Pneumonia labeled images and combined them into a COVID-19 negative test set with corresponding anomaly scores. We repeated the random selection of images from Normal and Pneumonia test cohorts 5 times in order to achieve an average performance metric of our models. The experiments were performed using AnoGAN trained on full COVIDx images, AnoGAN trained on segmented COVIDx images and RANDGAN trained on segmented COVIDx images. Table 18 to act as shortcuts in wrongfully helping CNNs classify the classes of COVID-19, Normal and Pneumonia images. Our RANDGAN model achieved an AUC of 0.77, a 6% improvement compared to that of the AnoGAN model on the segmented dataset, showing the effectiveness of our down-sampling and feeding randomly selected images to the Generator during the training.Figure 5 varies depending on the time of test in comparison with the time of contracting the COVID-19 virus. It has been shown that at specificity of 90%, on the day of symptom onset, the median FNR for RT-PCR test was 38% with Confidence Interval (CI) 18–65%. On day 8, the median FNR for RT-PCR test is 20% with CI 12–30% and on day 21 from symptom onset, the FNR increases to median of 66% with CI 54–77%. Although we do not have the information for the date of symptom onset and the date of X-ray acquisition from symptom onset in the COVIDx dataset, Table The false negative rate (FNR) of the RT-PCR testFigure In this study, we introduced RANDGAN, a novel generative adversarial network for semi-supervised detection of an unknown (COVID-19) class in chest X-ray images from a pool of known  and unknown classes (COVID-19) by only using the known classes for training. With this model, unknown cases can be screened and flagged for further investigations by radiologists increasing the probability of catching such cases early on. Using semi-supervised approaches for a problem such as detection of COVID-19, specially at the beginning of a pandemic are preferred over supervised approaches for they allow faster training of models without the need for gathering and annotation of data from the spreading disease. The result of semi-supervised models are more reliable where number of images are limited for the unknown (COVID-19) class. Where our semi-supervised model uses all COVID-19 images to test the model’s performance, supervised models have to use majority of the images  making it immediately more reliable although reporting a lower AUC. In contrast, supervised detection models report their results on around 25–100 COVID-19 X-ray images35 (10–20% of the available COVID-19 images). Future directions will focus on improving the performance of our proposed RANDGAN (AUC of 0.77) model by performing data augmentation, and as more data is collected, it is important to validate the model on external data sources .We demonstrated the importance of segmentation of lungs for the COVIDx dataset. DeGrave et al.One limitation of working with data of relatively early stages of a disease such as COVID-19 is dataset size. Even though our semi-supervised model is able to use all COVID-19 images to evaluate the performance of the model, while supervised models have to use majority of the already small COVID-19 cohort to train their images, more images would allow for a better understanding of the true performance of both supervised and semi-supervised models. Segmentation accuracy of the lungs is another limiting factor. Although the performance of the base model greatly improves (AUC of 0.54–0.71), segmentation model fails in some cases Fig. . As more


Nonlinear light sources are central to a myriad of applications, driving a quest for their miniaturisation down to the nanoscale. In this quest, nonlinear metasurfaces hold a great promise, as they enhance nonlinear effects through their resonant photonic environment and high refractive index, such as in high-index dielectric metasurfaces. However, despite the sub-diffractive operation of dielectric metasurfaces at the fundamental wave, this condition is not fulfilled for the nonlinearly generated harmonic waves, thereby all nonlinear metasurfaces to date emit multiple diffractive beams. Here, we demonstrate the enhanced single-beam second- and third-harmonic generation in a metasurface of crystalline transition-metal-dichalcogenide material, offering the highest refractive index. We show that the interplay between the resonances of the metasurface allows for tuning of the unidirectional second-harmonic radiation in forward or backward direction, not possible in any bulk nonlinear crystal. Our results open new opportunities for metasurface-based nonlinear light-sources, including nonlinear mirrors and entangled-photon generation. nd and 3rd harmonic generation into a single beam remains a challenge. The authors addressed this using transition-metal-dichalcogenide metasurfaces with tunable nonlinear emission direction.Though high-index dielectric metasurfaces are attractive due to their nonlinear effects, channelling of 2 In these studies, the enhancement of the nonlinear processes at the nanoscale has been a key enabler. The two main paths in this development have been the use of high-quality factor resonant photonic structures and highly nonlinear materials, including high refractive-index materials following the so-called Miller’s rule. The earlier approaches were based on the use of plasmonic nanoantennas and metasurfaces6 as well as multiple-quantum-well (MQW) metasurfaces9. However, the high dissipative losses of plasmonic materials and the mid-infra-red operation wavelengths of MQWs metasurfaces limit their broad applicability. More recently, high-index dielectric nanostructures have opened a new paradigm for nonlinear meta-optics11. The excitation of Mie-type resonances in such nanostructures plays a vital role in enhancing the light–matter interactions, such as nonlinear harmonic generation14. The use of high-index dielectric materials, such as semiconductors, have simultaneously provided a strong intrinsic nonlinear susceptibility and high-quality factor resonances. Several pioneering works prompted the use of nanostructures from materials with established fabrication to enhance the process of third-harmonic generation (THG). Among them, Si17 and Ge18 show the high refractive index in the near-infra-red spectral range and exhibit strong nonlinear responses. However, Si and Ge are centrosymmetric materials and thereby inhibit the second-order nonlinear effects7.Nonlinear optical phenomena in nanostructured materials have recently attracted much attention due to their wide spectrum of possible applications25. However, zinc-blende III–V materials are not transparent in the visible range and nanostructures are difficult to fabricate on low-index transparent substrates. Further such materials exhibit a peculiar nonlinear susceptibility with only off-diagonal non-zero 20. As a consequence, in (100) AlGaAs metasurfaces, the second-harmonic generation (SHG) is inhibited at the normal direction and the harmonic emission is directed into the diffraction orders27. This nonlinear diffraction phenomenon limits the possible applications of metasurfaces as nonlinear light sources, where a single beam of emission is required. While metasurfaces made of inversion-symmetry-broken nanoresonators29 and individual nanoantennas31 have been proposed to direct the emission at the normal direction, to date they operate in the diffractive regime14, with multiple diffractive orders being emitted at the harmonic waves32. The ability to operate in the zero-order SHG and THG, as well as to be able to tune the directionality of such unidirectional nonlinear emission from forward to backward direction remains elusive.Conversely, zinc-blende III–V semiconductors, such as GaAs and AlGaAs have received a great deal of interest for nonlinear metasurfaces due to their high-refractive indices and strong quadratic susceptibilities2 metasurface in the sub-diffractive, single-beam nonlinear emission in the visible to the near-infra-red regime. Our metasurfaces consist of nanoresonators with the highest refractive index >4, in the visible range, prompting strong material nonlinearity. The high refractive index further allows for optical resonances in smaller-size nanoresonators, thereby for densely packed metasurfaces. In particular, we design our metasurface to exhibit multipolar resonances at the second-harmonic wavelengths and employ the interference of this resonance to enable the tuning of the unidirectional emission of the second-harmonic light in forward or backward direction, controlled either by the excitation wavelength or by the incident polarisation. In all operation regimes, our metasurface remains sub-diffractive for the SHG and THG wavelengths. Finally, our TMDC metasurface can be fabricated on any transparent substrate due to the strong van der Waals (vdW) stiction forces of the layered MoS2. As such, our TMDC metasurfaces provide a versatile tool to tune all harmonic emissions and open new applications of nonlinear metasurfaces for advanced light sources33 or nonlinear mirrors34.Here, we demonstrate the significant enhancement of SHG and THG in a transition-metal-dichalcogenide (TMDC) MoS36 and gratings37. More recently, TMDCs have sparked an interest in novel dielectric metasurfaces with high refractive index44 due to the opportunity to fabricate high purity single-crystalline films on any transparent substrate. The TMDC materials, such as MoS2 further exhibit low optical losses in the visible and near infra-red spectral regions. The refractive index of the MoS2 is over 4.5  and reactive-ion etching (see “Methods”). The design of our nonlinear metasurface is illustrated in Fig. 2O3) substrate (~500 µm thick). A scanning electron microscopy (SEM) image of a typical metasurface is shown in Fig. Here, we fabricate a TMDC-based MoS2 metasurfaces  we can control the spectral position of the resonances from visible to infra-red wavelengths46. All metasurfaces are fully transparent at a longer wavelength (>900 nm)  and the corresponding transmittance through the metasurface. A qualitative agreement is observed between the measured and simulated results  problem with finite-element numerical simulations (COMSOL). The eigenvalues of the problem are the QNMs complex eigenfrequencies 48. We further adopt the following formulation 2 metasurface supports three QNMs with strong field confinement in the range of wavelengths between 700 and 900 nm. The eigenfields distributions associated with these eigenmodes are reminiscent of the field distributions of MD, ED and MQ-type Mie modes, as shown in Fig. However, all resonant modes are generally of mixed nature and to better identify their character we further performed quasi-normal mode (QNM) theory simulations2). The sample is embedded in a confocal scanning microscope (WITec) that allows for performing nonlinear imaging microscopy by scanning the sample stage. The THG and SHG nonlinear images of the MoS2 metasurface C are shown in Fig. 2 depends upon the number of layers in the MoS2 flake. A flake with an odd number of layers is non-centrosymmetric and it belongs to 50. A MoS2 flake with an even number of layers is centrosymmetric and belongs to 49 thereby it does not exhibit second-order nonlinaerity51. However, the metasurface appears to result in a moderate amount of SHG, again well above the background of the substrate. While recent works41 suggest that the SHG from TMDC nanoresonators has a bulk response, such possibility is unlikely and requires mode detailed analyses, as shown below.Next, we test the nonlinear frequency conversion from the TMDC metasurface. The optical microscope image of metasurface C is shown in Fig. n is the refractive index of the surrounding environment, being either air or sapphire substrate in our case, and It is worth noting that to provide a sub-diffractive nonlinear response, the periodicity of the metasurface should be, 52 marked with grey shading in Fig. First, we examine the enhancement of the THG process in the TMDC metasurface. For this purpose, we use a tunable femtosecond laser  in a home-build microscopy setup. We performed wavelength-dependent nonlinear measurements by focusing the laser beam onto the sample using a 20× microscope objective (NA = 0.4). We then collect the third harmonic in the forward direction by a second objective . To filter out the transmitted and reflected pump wave, we employed a short-pass filter at 800 nm. Two waveplates are used to control the polarisation of the pump beam. Figure 2 has a very high refractive index in the spectral region 450–600 nm (n = 4.8–5.6), it also has a large absorption coefficient (k = 3.1–1.1).To gain a better insight on the physics of the third harmonic emission, we further perform full-wave numerical simulations of the THG in our metasurface. Figure 2 metasurface belongs to the D3h symmetry group. The slight asymmetry in the experimental polarisation measurement is likely caused by a slight misalignment in the experimental setup and slight asymmetry in the fabricated samples.Figure Furthermore, full-wave nonlinear simulations have been performed in order to investigate the tapering angle effect of the following three meta-atom shapes: (i) cone, (ii) truncated cone, (iii) vertical cylinder, on the THG conversion efficiency (in either direction forward and backward) and the results are presented in Supplementary Fig. 2 metasurface supports several resonant QNMs at the SH wavelengths, as indicated in Fig. 53.Next, we turn our attention to the second-order nonlinear response of the TMDC metasurfaces and study the resonant enhancement and the directionality of the SHG. The MoSTo test the influence of the resonant modes on the SH enhancement, we examined the wavelength dependence of the SHG from metasurface C. For this purpose, we scan the wavelength of the infra-red pump beam in the range of 1200–1600 nm. The measured SH spectra for different pump wavelengths are shown in Fig. 41 treats the nanoresonators as bulk nonlinear media, the bulk MoS2 has D6h symmetry and does not exhibit bulk second-order nonlinear effects. However, for a few-layer-thick material, the actual second-order nonlinear response will depend on the exact number of layers50. In the case of TMDC metasurfaces, the situation is even more complex due to the inhomogeneous near fields and the absorption of the MoS2. Therefore, it is important to quantify the position and origin of the nonlinear sources for SHG in TMDC metasurfaces.While ref. top and bottom layers of the MoS2, as depicted in Fig. 54. In such nanostructures, the second harmonic emission originates mainly from their sidewalls, where the symmetry is broken. In our TMDC metasurface, the dominant contribution is from the top and bottom MoS2 layers, which are approximately four orders of magnitude more efficient.To determine the location of the SH current sources, we assumed four extreme scenarios on how the second-order nonlinearity  is non-resonant, and we can therefore retain the full-wave expression of The expression in Eq.  is only x-axis and the co-polarised component of the SHG. We can clearly observe that the SHG displays peaks of efficiency that are associated only with the two magnetic modes, while SH light coupling with ED-like mode is significantly inhibited. This result is in qualitative agreement with the full-wave nonlinear numerical simulations  GaAs nanoantenna. However, no metasurfaces of such nanoantennas have yet been realised to date. Furthermore, all metasurfaces to date that are resonant at the fundamental wave have multiple diffraction orders at the SH wave, which would not allow for pure forward to backward switching of the SH emissions. Here, we demonstrate a dielectric metasurface with forward to backward continuous tunability of the SH emissions. This property is a direct consequence of the position of the nonlinear sources and the interference of the QNMs in the far-field. Importantly, to tune the SH directionality, no physical change in the structure or complicated geometry is required. The tuning can be achieved by varying the input wavelength or polarisation.Finally, the most intriguing feature of the presented MoSDF < 0.5 and DB > 0.5. In Fig. x-axis scaling (representing SH wavelengths) corresponds to the selected fundamental wavelengths, where our OPO shows stable performance in the spectral range from 1400 to 1600 nm. Figure 2 meta-atoms, such as the tapering angle and the surface roughness.To quantify such tuning of the SH emission, we define the SH directionality in terms of the relative power of the SH emitted in forward (F) direction as 2 meta-atom has the shape of a straight vertical cylinder . The data in Fig. 34. In addition, in the case of the vertical straight cylindrical metasurface, the SH directionality as function of To illustrate, the effect of the tapering on the switching of the SH directionality in Fig. Furthermore, in Fig. 2. The vdW interactions between the TMDC layers offer a unique opportunity to realise such metasurfaces on any smooth and transparent substrate, thus one can excite and detect the SH and TH signals from each side of the metasurface. We have experimentally and theoretically demonstrated that by changing the aspect ratio of the truncated-cone meta-atoms, one can tune the induced resonances of the metasurface and provide direct control over the second- and third-order nonlinear generation. As compared to other dielectric and semiconductor metasurfaces the use of the high-index TMDCs allows to design of highly subwavelength resonators and therefore to construct nonlinear metasurfaces with single-beam emission at the harmonic waves. In this sub-diffractive regime, only a zero diffraction order is emitted from the metasurface, which makes it act as a homogeneous ultra-thin nonlinear crystal but with enhanced conversion efficiency. Most importantly, the second-harmonic emission can be tuned from forward to backward direction, controlled by wavelength, incident polarisation and metasurface geometry. This tunable unidirectional nonlinear emission facilitates novel applications in nonlinear light sources.In summary, we have demonstrated the directional nonlinear harmonic generation in a high-refractive-index single-crystal vdW MoS57.In addition, we have employed Mie-resonances at the harmonic wavelengths as a versatile tool to control both SHG and THG at the nanoscale. The choice of resonances at the harmonic wavelengths further makes our metasurfaces deeply subwavelength for both the second and third harmonic waves. Our work aims to demonstrate the fundamental physical phenomena on the nonlinear emission in TMDC metasurfaces and as such is able to unveil the origin of the quadratic nonlinearities in TMDC metasurfaces. These stem from the broken crystalline symmetry at the top and bottom layer of the meta-atoms. This feature is distinct to the harmonic generation by surface nonlinearities in other bulk non-centrosymmetric material, such as silicon and results in more than two orders of magnitude higher SHG efficiency with respect to silicon metasurfaces. We note that while our metasurface design does not aim at reaching record-breaking conversion efficiencies, it is possible to applied multiple strategies for increasing the efficiency of both SHG and THG. These include designs with higher quality resonances, such as in the case of bound-state in the continuum59 or dielectric metasurfaces60 have also been recently studied, our TMDC metasurface offers several fundamental advantages. These include the ability to simultaneously enhance the SHG and THG processes, offering a significantly higher interaction volume for the THG. For the case of SHG, it further offers the ability to switch the directionality of SH emission between forward and backward direction, which so far has not been demonstrated in other metasurface systems. Importantly, this intriguing feature has been achieved all-optically: there is no need to apply physical changes to the metasurface and can be dynamically controlled by varying the pump wavelength or polarisation. We have experimentally proven this SH directionality switching and have theoretically shown that the contrast of this switching can be further enhanced for the case of vertical cylinders, thereby virtually acting as a mirror for the SH light.The comparison of the performance of our TMDC metasurface with other existing works, in terms of its high index and SH directionality switching, has been presented in Table 2 nanoresonators (MoS2 metasurfaces) on a sapphire (Al2O3) substrate. To achieve the pristine quality of the sample, we mechanically exfoliated vdW material MoS2 onto the substrate. The thickness of the transferred flakes was then measured by a surface profilometer. After cleaning, we spin-coated a single layer of the ZEP positive resist followed by a thin layer of conductive polymer (to dissipate the charges during the lithography process) on the selected flake. The flake was subsequently patterned into nanoresonators using EBL. After developing the sample, we deposit an 80 nm thick layer of the Al (for dry etching mask), using an e-beam evaporator. The truncated cone nano-pillars were then dry-etched by employing inductive-coupled plasma of SF6 + CHF3 + Ar. and then Al caps from the top of the truncated-cone pillars removed by a lift-off process. Finally, we employed SEM to examine the quality and dimensions of the MoS2 metasurfaces.We fabricated multiple arrays of MoS2 bulk from a work presented in ref. 61  tensor value of 62. To gain a deeper insight into the physics of SH peaks, we employed QNMs theory and solved the complex eigenvalue problem with a finite-element method (COMSOL).To theoretically describe our experimental results, first, we performed FDTD simulations using the Lumerical FDTD tool for the manifestation of mode properties in the SH spectrum (700–900 nm). We used periodic boundary conditions along with normal-incidence plane-wave illumination from either side (the air or substrate side) of the metasurface and calculated the linear spectra of the metasurface from both sides. The data presented in the paper corresponds to the plane-wave illumination from the airside to match our experimental arrangement. We used the refractive index and extinction coefficient of the MoSSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1


Hyperprolactinemia is one of the most common causes of infertility in women. The prevalence of pituitary tumors is 25-30% among infertile participants with hyperprolactinemia. The aim of this study was to describe the causes of hyperprolactinemia in infertile women referred to Yazd Infertility Center.This cross-sectional study was conducted with 182 infertile women with hyperprolactinemia who were referred to Yazd Infertility Center from February 2018 to October 2019. Serum prolactin was assessed by the human prolactin ELISA kit according to the Padtan Gostar Isar protocol. The age, duration of infertility, causes of hyperprolactinemia, and type of infertility treatment were noted. The MRI findings were added. The mean age of participants was 28.9 Idiopathic hyperprolactinemia and polycystic ovary syndrome are the most common reasons for hyperprolactinemia. Hyperprolactinemia (Hyper-PRL) is a common endocrine disorder (1). Hyper-PRL occurs in both genders at any age (2). The annual incidence of Hyper-PRL in men and women is 1.4 and 8.7 per 100,000 persons, respectively. The most common causes of Hyper-PRL are pregnancy, pituitary adenoma, intracranial tumors, prolactin stimulants (3), and pharmacological conditions such as the administration of estrogen or heavy metals (4). Hyper-PRL has also been observed in primary hypothyroidism, chronic renal failure, and hemodialysis (5). Moreover, some participants with Hyper-PRL are diagnosed as having idiopathic Hyper-PRL (3). If participants have a continuously raised prolactin level and other causes have been excluded, a pituitary tumor is assumed. Magnetic resonance imaging (MRI) is the most sensitive imaging technique for recognizing pituitary tumors .Hyper-PRL is associated with short- and long-term consequences including menstrual disorders, decreased quality of life, decreased bone mineral density, and sexual dysfunction. Hyper-PRL affects fertility by impairing gonadotropin-releasing hormone (GnRH) secretion and interfering with ovulation . Common manifestations of Hyper-PRL are anovulation, oligomenorrhea, amenorrhea, and galactorrhea . Morphological changes seen in the follicles in hypothyroidism can be due to the production of higher levels of prolactin which may block the secretion and function of gonadotropins (11).Dopamine agonists are considered the first-line therapy for Hyper-PRL. Dopamine agonist therapy can restore the normal level of prolactin and gonadal action (12). Hyper-PRL is one of the most frequent etiologies of infertility in women. Almost all Hyper-PRL participants need MRI imaging during diagnostic approaches, but MRI is not easily accessible or affordable, and it is time consuming. About 25-30% of these participants have pituitary tumors (1).The aim of the current study was to evaluate the causes of Hyper-PRL in infertile women referred to the Yazd Infertility Center.This cross-sectional study was conducted with 182 Hyper-PRL infertile women who were referred to Yazd Infertility Center from February 2018 to October 2019.The inclusion criteria were: married women with prolactin higher than 20 ng/ml; and normal range of thyrotropin stimulating hormone (0.4 to 4.0 mIu/lit). The exclusion criteria were: no cirrhosis or kidney failure; and no history of medication which increases prolactin levels such as hormonal or antipsychotic drugs. Information such as age, duration of infertility, reasons for Hyper-PRL, MRI findings and type of infertility treatment were extracted from participants' medical records. After 8-12 hr of night fasting, a blood sample was collected from all participants using a 5 ml disposable syringe and their serum was separated by centrifugation (Eppendorf). Serum prolactin was assessed using the human prolactin ELISA kit according to the Padtan Gostar Isar protocol.This study was approved by the Ethical Committee of Shahid Sadoughi University (Code: IR.SSU.MEDICINE.REC.1397.227).t test, and ANOVA. All analyses were performed in the Statistical Package for the Social Sciences (SPSS) software version 22.0 . P-values less than 0.05 were considered significant.Data were demonstrated by the mean, standard deviation (SD), frequency, and percentages. Data were analyzed using Chi-square test, This study was conducted with 182 infertile women with Hyper-PRL. The mean There was no significant difference in the mean duration of infertility in participants with different causes of Hyper-PRL (p = 0.15). In addition, no significant difference was observed between the mean age of participants across the different causes of Hyper-PRL (p = 0.47); however, the mean age of participants in the pituitary adenoma group (29.36 The mean duration of infertility in the present study was 5.04 Hyper-PRL may lead to defective ovulation and decreased fertility (15). Several possible reasons for Hyper-PRL have been reported in studies. Stimulants such as thyrotropin-releasing hormone, vasoactive intestinal peptide, estrogen, and dopamine receptor antagonists induce prolactin secretion (16). Also, studies have shown that excessive secretion of prolactin reduces the pulsatile release of GnRH and impairs normal gonadal steroid secretion, which leads to positive feedback effects at the pituitary and hypothalamic levels, and ultimately infertility (15).In the present study, idiopathic Hyper-PRL was the most common reason for Hyper-PRL with a frequency of 47.3% in infertile participants. The results of a number of studies are in line with these findings. One study demonstrated that idiopathic Hyper-PRL was seen in 8.5%-40% of participants with Hyper-PRL (16). In another study of 183 women with hyper-PRL, most participants had idiopathic Hyper-PRL. The high frequency of idiopathic Hyper-PRL found in our and these studies could be due to not re-examining the prolactin test or testing after the menstrual phase, or long-term use of drugs that interfere with dopamine (a prolactin secretion inhibitor), even if some time has passed since the drug was stopped. Another possibility may be the consumption of drugs that the patient did not mention in the history or that were not on the list of drugs that cause Hyper-PRL but increased prolactin (15). Other studies have reported that idiopathic Hyper-PRL may be due to genetics, macroprolactinemia, or prolonged use of drugs that interfere with dopamine .In these participants, fertility may increase via long-term use of dopaminergic drugs. This treatment prompts ovulatory cycles and normalizes the defective luteal phase. Dopaminergic therapy should be continued for at least one yr since more than 50% of pregnancies occur six months after medication. Stimulation of the ovary with pulsatile administration of GnRH and gonadotropin may lead to ovulatory cycles and fertility in the infertile participants (18).In addition, while PCOS is often considered the most common cause of Hyper-PRL in infertile women, in the present study it was ranked as second. In a study aimed at assessing PRL levels in women with PCOS, Hyper-PRL was observed (19). Hyper-PRL in participants with PCOS may reflect a deficiency of dopamine (20). One of the possible mechanisms for hyperprolactinemia in women with PCOS-induced secretion of luteinizing hormone is accelerating GnRH pulsatility and decreasing dopaminergic tone (21). PCOS and Hyper-PRL have been reported to be the most common etiologies of anovulation in females (21). Various hypotheses have been proposed for this. Several studies have shown that in women with PCOS, the secretion of prolactin is increased under the action of estrogen. However, a number of studies have shown that the use of oral contraceptive medications such as estrogen does not lead to an increase in prolactinoma size (22). Hyper-PRL may be associated with insulin resistance and glucose intolerance in participants with PCOS. It has been shown that prolactin can be associated with enhanced concentration of fatty acids, downregulation of insulin receptors, and defects in insulin function (23). One study confirmed insulin resistance in Hyper-PRL subjects and showed the role of prolactin in mediating insulin resistance (24). Thus, if a high percentage of Hyper-PRL cases are due to PCOS, a course of treatment for this disease and prolactin re-assessment is necessary.Pituitary adenoma accounted for 19.2% of Hyper-PRL cases. Some studies have shown that Hyper-PRL may be mainly due to pituitary tumors (25). Other studies have reported that the cause of Hyper-PRL is mostly due to pituitary adenomas (26-28). A study on 87 infertile women aged 31-40 yr with Hyper-PRL showed that 75.8% of participants suffered from microadenoma. Moreover, participants with pituitary adenoma had a significantly higher level of prolactin than others, which is not consistent with our study. The reason for this difference may be the time of PRL check. In that study, participants with PCOS and pituitary adenoma were younger than participants with pituitary adenoma, which is consistent with our study (25). Differences in findings may be due to the MRI being carried out after repeated measurement of serum prolactin.The high percentage of idiopathic Hyper-PRL may be due to macroprolactinemia, which is not routinely checked for in Iranian laboratories. Prolactin should be measured in the follicular phase and re-assessed, and if it is elevated then MRI study is recommended.Idiopathic Hyper-PRL and PCOS are the most common reasons for Hyper-PRL. Pituitary adenoma appears to be more common in younger PCOS participants, but further studies are needed to confirm this.The authors declare that there is no conflict of interest.