N = 832) twice, immediately before and after the intervention. Based on their level of exposure to the intervention messages, the women were retrospectively classified into two groups, resulting in a two‐group (no–low exposure vs. medium–high exposure) quasi‐experiment. Women in the medium–high exposure group, in comparison with women in the no–low exposure group, exhibited higher campaign awareness (p < .001), visits to the campaign website (p < .001), and engagement with the website content (p < .001). They also reported more positive breastfeeding attitudes , self‐efficacy , and social support . But they did not differ from women in the no–low exposure group in breastfeeding initiation (p > .05) and duration (p > .05). In conclusion, a social media‐based intervention resulted in more positive breastfeeding attitudes, higher self‐efficacy, and higher perceived social support. Future studies need to investigate the optimal level of intervention message dosage that prompts significant behavioral changes.Social media have emerged as a promising communication channel for promoting breastfeeding among a new generation of mothers. Yet, there is no published study reporting the effects of a large‐scale social media intervention on key breastfeeding‐related perceptions, attitudes, and behaviors. As a component of its breastfeeding promotion campaign, the Women, Infants, and Children (WIC) program implemented a 12‐month intervention using Facebook and Instagram and subsequently evaluated the outcomes by surveying WIC‐participating women ( A social media‐based intervention promoting breastfeeding among WIC participants was evaluated for its impact on campaign awareness, use of the WIC Breastfeeding Support website use, breastfeeding attitude, self‐efficacy, perceived social support, and breastfeeding behaviors. The intervention met its objectives by generating higher campaign awareness, more visits to the WIC website, more engagement with various elements of the website content, more positive breastfeeding attitudes, higher self‐efficacy, and higher perceived social support. On the other hand, the direct impacts on breastfeeding initiation and duration were not statistically significant. Instead, the intervention has shown some promise of effecting behavioral changes by improving attitude and self‐efficacy. The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is one of the largest federal nutrition programs, and it serves more than 42% of all infants in the U.S.  directly addressed the campaign‐specific objectives:Exposure to the WIC Breastfeeding Support social media intervention will generate a more positive attitude toward breastfeeding.Exposure to the WIC Breastfeeding Support social media intervention will generate higher breastfeeding self‐efficacy.Exposure to the WIC Breastfeeding Support social media intervention will generate higher perceived social support.In addition, the social media intervention had objectives regarding breastfeeding‐related perceptions and attitudes. These hypotheses directly addressed the objectives:Will exposure to the WIC Breastfeeding Support social media intervention generate a higher breastfeeding initiation rate?.Will exposure to the WIC Breastfeeding Support social media intervention generate a longer breastfeeding duration?Will exposure to the WIC Breastfeeding Support social media intervention generate a higher breastfeeding initiation rate via a more positive breastfeeding attitude, higher self‐efficacy, or higher perceived social support?Will exposure to the WIC Breastfeeding Support social media intervention generate a longer breastfeeding duration via a more positive breastfeeding attitude, higher self‐efficacy, or higher perceived social support?.Lastly, the effects of social media intervention on breastfeeding behaviors were examined. The National Breastfeeding Awareness Campaign (NBAC), conducted by the U.S. Department of Health and Human Services Office on Women's Health between 2004 and 2006, was a multimedia national campaign that deployed television, radio, newspapers, magazines, mass transit shelters, and a website. Although the NBAC demonstrated the potential of using online media as part of a larger effort by garnering a large number of visitors to the website, it did not perform outreach through social media channels. Hence, two research questions rather than hypotheses were generated here to examine the effects of social media intervention on breastfeeding behaviors. Additionally, we explored the indirect effects of social media intervention on breastfeeding initiation and duration via breastfeeding‐related perceptions and attitudes.22.1The social media intervention involved the two most popular social media platforms, Facebook and Instagram. The SA/ITOs either used their existing Facebook and Instagram pages or set up new pages for the intervention. Leading up to the launch of the intervention in August 2020, SA/ITOs promoted the social media pages through their regular communication channels with clients  Five standard social media posts per week, a total of 260 different posts, were distributed via the two platforms over the 12‐month intervention period, and the same posts were made on Facebook and Instagram on the same schedule. Many of the posts explicitly encouraged their viewers to interact with the posts by liking, commenting, and sharing with others. SA/ITOs also had designated personnel who responded to inquiries and moderated conversations when warranted.2.2A two‐group pretest–posttest design was employed to examine the effects of the social media intervention. Baseline pretest surveys were collected immediately before the beginning of the intervention program. Those who participated in the pretest surveys were contacted again by the end of the program to complete the posttest survey. The two groups (high vs. low exposure) were constructed retrospectively based on their level of exposure to social media messages.2.3www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.htmlBaseline pretest surveys were administered to prenatal or breastfeeding, active WIC participants by SA/ITO site staff between June 15 and July 31, 2020. WIC staff used a combination of text messages and mailouts/postcards to share the online pretest survey link. Contact information of pretest participants was retained by SA/ITO site staff, who used the list to contact them again for posttest surveys between June 14 and July 31, 2021. This study was deemed exempt by the University of Nevada, Reno Institutional Review Board under federal regulation 45 46.101 (b) CFR. Reference: 2.4n = 832, 31%) completed the posttest, resulting in the matched pretest–posttest dataset containing 832 cases. The respondents' ages ranged from 17 to 44 years, with a mean age of 29.7 years. The largest percentage of respondents were 30–34 years old (30%) and were high school graduates or had a GED (44%). Homemakers/stay‐at‐home moms made up 41% of the sample, 27% were employed full‐time, and 19% were unemployed. More participants identified themselves as an ethnic or racial minority (61%) than as a non‐Hispanic Caucasian (39%). Forty‐three percent were married, 31% were single or never married, and 20% lived together with a partner.The pretest was taken by 2705 individuals, but only about one‐third of them  read articles, (2) watched videos, (3) clicked links to other webpages from WIC or other government programs, and (4) shared links with friends or family. They were allowed to check all that applied.2.5.5αpretest = 0.65; αposttest = 0.67).Four items tapping into general attitudes were culled from the Iowa Infant Feeding Attitude Scale .The Breastfeeding Self‐Efficacy Scale‐Short Form (BSES‐SF) was used to assess breastfeeding confidence Dennis,  which wa2.5.7αpretest = 0.82; αposttest = 0.81).A breastfeeding social support scale Wilson,  was adap2.5.8One question asked whether the respondent was currently breastfeeding their baby. The following question asked whether the respondent ever breastfed the baby. Those who answered no to both questions were coded as “no initiation.” All others were coded as “initiation.”2.5.9For the respondents who answered that they were currently breastfeeding their baby, their baby's age at the time of the survey became breastfeeding duration. The baby's age had been captured in weeks or months by an earlier question. Those who ever breastfed but were not currently breastfeeding their baby were presented with one more question asking how many weeks or months they had breastfed or pumped milk for their baby. Respondents who did not initiate breastfeeding were assigned “0.” Subsequently, the duration was recoded into two categories, “less than three months” and “three months or longer.”2.6To answer 33.1X2  = 13.74, p < .001. Similarly, a higher percentage of WIC participants with medium–high exposure to the campaign messages (31%) recognized the campaign logo than those with no–low exposure (18%). The group difference was statistically significant, X2  = 15.16, p < .001. A higher percentage of WIC participants with medium–high exposure to the campaign messages (34%) identified the campaign tagline accurately than those with no–low exposure (20%). The difference between the two groups was statistically significant, 3.2%) visited the WIC Breastfeeding Support website than did their counterparts with no–low exposure (25%). The difference was statistically significant, X2  = 53.05, p < .001. A higher percentage of WIC participants with medium–high exposure to the campaign messages (543.3%) read articles on the WIC Breastfeeding Support website than their counterparts with no–low exposure (14%). The difference was statistically significant, X2  = 57.65, p < .001. Similarly, a higher percentage of WIC participants with medium–high exposure (32%) watched videos on the WIC Breastfeeding Support website than those with no–low exposure (16%). The difference was statistically significant, X2  = 24.33, p < .001. Moreover, a higher percentage of WIC participants with medium–high exposure to the campaign messages clicked (18%) and shared links (9%) on the WIC Breastfeeding Support website than the participants with no–low exposure (6% clicked and 1% shared). The difference was statistically significant for those who clicked on links, X2  = 29.54, p < .001, and shared links, X2  = 31.22, p < .001. A higher percentage of WIC participants with medium–high exposure to the campaign messages  than those with no–low exposure . The difference was statistically significant, F  = 4.5, p = .034. WIC participants with medium–high exposure to the campaign message exhibited more positive attitudes  was associated with higher self‐efficacy than no‐low exposure . The difference was statistically significant, F  = 6.88, p = .009. Medium‐high exposure to the campaign messages  was associated with higher perceived social support than no–low exposure . There was a significant difference in the mean postcampaign support for breastfeeding, F  = 19.21, p < .001. Medium–high exposure to the campaign messages  = 0.811, p = .37. The social media intervention did not increase breastfeeding initiation.The percentage of WIC participants who initiated breastfeeding was 91% in the medium–high exposure group and 89% in the no–low exposure group. There was no statistically significant difference in breastfeeding initiation between the two groups, 3.8X2  = 0.677, p = .41. The social media intervention did not affect breastfeeding duration.The percentage of WIC participants who breastfed for 3 months or longer was 76% in the medium–high exposure group and 73% in the no–low exposure group. The two groups were not statistically different, 3.9X2  = 80.79, p < .001. As suggested by the RQ1 analysis, the intervention was not a significant predictor of breastfeeding initiation, OR = 0.89, 95% CI , p = .705. Neither was social support, OR = 0.99, 95% CI , p = .646. On the other hand, breastfeeding attitude, OR = 1.26, 95% CI , p < .001, and self‐efficacy, OR = 1.05, 95% CI , p < .001, were significant predictors. Considered together with the Breastfeeding initiation was predicted by social media intervention, breastfeeding attitude, self‐efficacy, and social support. The logistic regression model was statistically significant, 3.10X2  = 202.98, p < .001. As suggested by the RQ2 analysis, the intervention was not a significant predictor of breastfeeding duration OR = 0.81, 95% CI , p = .359. Neither was social support, OR = 0.98, 95% CI , p = .322. On the other hand, breastfeeding attitude, OR = 1.16, 95% CI , p < .001, and self‐efficacy, OR = 1.08, 95% CI , p < .001, were significant predictors. Considered together with the Breastfeeding duration was predicted by social media intervention, breastfeeding attitude, self‐efficacy, and social support. The logistic regression model was statistically significant, 4A social media intervention was evaluated for its impact on campaign awareness, WIC Breastfeeding Support website use, breastfeeding attitude, self‐efficacy, perceived social support, and breastfeeding behaviors. The intervention met its objectives by generating higher campaign awareness, more visits to the WIC website, more engagement with various elements of the website content, more positive breastfeeding attitudes, higher self‐efficacy, and higher perceived social support. On the other hand, the direct impact on breastfeeding initiation and duration was not statistically significant. Instead, the intervention has shown some promise of effecting behavioral changes by improving attitude and self‐efficacy.These findings are encouraging for breastfeeding promotion advocates. While a few other programs showed the feasibility and potential benefits of social media‐based interventions in low‐income countries or for underserved women in the U.S., they did not include rigorous testing of the intervention efficacy among their target populations. This social media intervention was the first large‐scale trial since 2011 when the Institute of Medicine Food and Nutrition Board prioritized the adoption of social media in future breastfeeding promotion campaigns. In the absence of established intervention protocols and a robust evaluation framework, the current program advanced the research and practice by filling the gaps and demonstrating the positive outcomes specific to the Campaign as well as broadly related to breastfeeding in general.At the same time, the lack of direct impacts on breastfeeding initiation and duration reminds us how challenging it is to change breastfeeding behaviors through social marketing campaigns. After completing the 2004–2006 NBAC, the program lead reflected that, in retrospect, increasing breastfeeding was unrealistic for a 2‐year campaign. Given that the current intervention lasted for only half as long, the focus should be on what was improved during the 1 year and how the gains could be leveraged to facilitate the ultimate behavioral changes. The positive relationships between breastfeeding attitude/self‐efficacy and initiation/duration suggest that future interventions should monitor changes in the key psychological constructs and changes in the behavioral outcomes. Future studies could also focus on identifying the threshold level of intervention dosage that prompts significant behaviors changes.Of the three key psychological constructs, our data showed that self‐efficacy was as strong a predictor of breastfeeding initiation and duration as attitude. The need to emphasize self‐efficacy was another major lesson from the NBAC that the current intervention adopted. On the other hand, the lack of association between perceived social support and breastfeeding initiation/duration illustrates that communication‐based interventions—regardless of the platforms deployed—could become more impactful when supplemented by community‐based support.Despite these contributions to breastfeeding promotion research and practice, this intervention and its evaluation have limitations. First, the level of intervention message exposure has room for improvement. In this age of audience fragmentation and media saturation, the 4:1 ratio between no–low exposure and medium–high exposure participants was encouraging for a social media intervention. Still, it could become more impactful if the exposure level is increased. Furthermore, the medium–high exposure group was more favorably predisposed to breastfeeding than the no–low exposure group in terms of attitude, self‐efficacy, and social support at the pretest. Selective exposure—people's tendency to seek out, whether consciously or unconsciously, messages that support their pre‐existing views while avoiding the opposite messages—is a well‐documented phenomenon in media research and has been observed in health communication campaign contexts as well ; formal analysis ; writing – original draft (lead). Sung‐Yeon Park: Conceptualization (lead); formal analysis ; funding acquisition (supporting); investigation ; methodology ; writing – original draft (supporting); writing – review and editing (lead). Taya Kohnen: Data curation ; investigation ; project administration ; validation . Bret Sarnquist: Data curation ; investigation ; project administration (supporting); validation . Hyo Jin Jeon: Conceptualization ; funding acquisition (supporting); investigation ; methodology . Michelle Granner: Conceptualization ; funding acquisition (supporting); investigation ; methodology . Kelly Morning: Conceptualization ; funding acquisition (supporting); investigation ; methodology . Patricia MacNeil: Conceptualization ; resources ; supervision (lead); writing – review and editing (supporting). Olivia Deavers: Conceptualization ; resources ; supervision (supporting); writing – review and editing (supporting). Valery Soto: Conceptualization ; resources ; supervision (supporting); writing – review and editing (supporting). Elizabeth Christiansen: Conceptualization ; formal analysis ; funding acquisition (lead); investigation ; methodology ; project administration .This manuscript reports findings from a cooperative agreement between USDA FNS and the University of Nevada, Reno (UNR) to implement WIC Breastfeeding Support Campaign (Award # WIC‐BFC‐2019‐NV).There is no conflict of interest to report.The Office of Research Integrity at the University of Nevada, Reno waivered an ethics review for the data collection.


Bark-feeding and wood-boring insect pests can have significant negative impacts on conifers and wood production. The damage they cause is expected to increase in the future due to climate change and the growth of international trade. This study employed DNA barcoding of beetle juveniles (Coleoptera) sampled from standing trap trees and cut log piles at regular intervals over a 2-yr period to monitor the beetle community dynamics and associated environmental factors. Tree species was found to have a major influence on beetle communities, most strikingly at the start of early decay stages. Lower species diversity was reported from standing trap tree samples compared to log pile samples, likely due to higher residual defences in dying and recently dead trees. While the species identified from standing trap trees are more likely to be a threat to the forestry sector, the species found in the log piles are more likely to be beneficial due to their high abundance and their ability to compete with pests for breeding substrate. The analysis of beetles collected inside trees revealed additional information on ontogenetic niches and host preferences beyond that acquired solely from flight interception trap data. Our results offer insights on community composition and dynamics of bark-feeding and wood-boring insect species in Welsh conifer forests and provide resources for monitoring and management of potential pest species. Insects promote forest ecological succession by aiding in the elimination of dead, old, weak, stressed or diseased trees which benefits the overall health and resilience of the forest . Among iThe impact of pests on conifer forests can be economically significant because conifers are preferred over broadleaves in plantations and supply well over 50% of the world timber harvest . MoreoveThere is a growing need to study tree and pest insect systems in order to predict the effects of climate change and the economic and environmental impacts of pests . In the The present study aimed to depict beetle community structure, and its change in time over a 2-yr period, in Welsh coniferous forests through temporal sampling and DNA barcoding of larvae and pupae. A second objective was to investigate regional patterns and/or environmental variables driving community structure. The effects of tree species and early succession stages on beetle species composition (log pile vs. standing trap trees) were studied alongside the effects of log characteristics  and sun exposure for the log pile. To our knowledge, this is the first study in Wales using larvae to determine links between beetle species, tree host, and environmental variables in fresh decaying wood communities of coniferous trees.Picea sitchensis), Scots pine (Pinus sylvestris) and Noble fir (Abies procera). In November 2018, 5 Natural Resources Wales (NRW)-managed commercial forests were selected due to their geographical spread across Wales and the presence of the studied tree species with suitable planting dates. Within these forests and for each tree species, a monoculture stand was selected based on environmental similarities between stands  and to investigate early colonizers of dead wood insect communities, as bark-feeding and wood-boring insect communities differ between dying and dead trees . Three b2O. PCR cycling conditions were: 95oC for 5 min, followed by 35 cycles of 95°C for 30 s, 1 min at 48°C annealing temperature, and 1 min 30 s at 72 °C, followed by a final extension stage at 72°C for 5 min. PCR products were then processed by Sanger Sequencing with the forward primer only. Specimen species identification was determined using the NCBI online Basic Local Alignment Search Tool (BLAST). Identifications were determined based on set thresholds for e-value (<0.001), query coverage (>80%), and sequence similarity (>95%). When a specimen sequence returned the same score for 2 species, the sequence was included in a Maximum Likelihood phylogenetic tree , using the model with the lowest BIC (Bayesian Information Criterion) score, drawn using the software MEGA  were found in breeding galleries alongside larvae and were identified based on morphological characteristics using the key from are MEGA  using sep equal to 100) transformation was used on species proportions and generalized logarithmic transformation (log(y + p); p = 1) was used on Shannon–Wiener and 1-Simpson’s indices. The result of these transformations was then used as an environment in which linear mixed models (estimated using REML and nloptwrap optimizer) were fitted using the lmer function in the lme4 package for R  and polygons connecting the outermost site scores of the different factors . The function envfit was used to fit environmental variables  onto the ordination plot to represent how they were correlated with the ordination axes generated from the community data . On average, the number of galleries found on each log was 6.025. It was 8.451 for the log piles and 3.600 for the standing trap trees. The species accumulation curve for the standing trap tree experiment  showed fHylurgops palliatus  and Dryocoetes autographus (Ratzburg) were the 2 most abundant species  and were the only species found in logs from all 3 tree species  and Pissodes pini (Linnaeus)) which accounted for 9 and 5% respectively of the beetles identified and were collected only from Scots pine logs  exhibited an association with a particular tree species and type of susbtrate: 5 species in Scots pine, 4 species in Noble fir and 2 species on Sitka spruce; 4 species were found exclusively in standing trap trees and 10 species only in log piles. Noble fir supported 10% of the number of individual beetles identified, Scots pine 42%, and Sitka spruce 48%. Geographically, 15% of beetle individuals were identified from St Gwynno, 20% from Breidden, 20% from Hafren, 21% from Radnor and 24% from Ystwyth. The most abundant species  were found on all 5 forest sites, and a similar pattern can be seen for P. pini and Hylastes opacus (Erichson) which were found on 4 out of 5 forest sites. The other species were found on half or less of the forest sites.The number of beetles identified from log piles decreased over successive sampling: 254 in Summer 2019; 198 in Winter 2019; 73 in Summer 2020. From 651 individuals sampled, 16 beetle species were found . Hylurgoine logs . Of the P = 0.024; χ²(2) = 7.112, P = 0.029; χ²(2) = 17.242, P < 0.001). The type of experiment had significant effect on the 3 diversity measures, with Log piles showing higher values than standing trap trees  = 6.732, P = 0.009; β = 0.065, SE = 0.026, χ²(1) = 6.066, P = 0.014; β = 0.004, SE = 0.004, χ²(1) = 9.410, P = 0.002). Similarly, Tree species also had a significant effect (respectively χ²(2) = 26.685, P < 0.001; χ²(2) = 26.257, P < 0.001; χ²(2) = 37.461, P < 0.001). All diversity measures were significantly higher in Sitka spruce  = 4.719, P < 0.001; β = 0.063, SE = 0.031, t(164) = 4.623, P < 0.001; β = 0.008, SE = 0.003, t(164) = 6.762, P < 0.001) and Scots pine  = 4.564, P < 0.001; β = 0.134, SE = 0.032, t(164) = 4.682, P < 0.001; β = 0.014, SE = 0.003, t(164) = 5.901, P < 0.001) than Noble fir. On the other hand, diversity measures were not significantly different in Scots pine and Sitka spruce .For Shannon–Wiener diversity index, 1-Simpson’s index of diversity and Species Proportion, the interaction between Tree Species and Experiment Type was significant (respectively χ²(2) = 7.463, t(45) = −−3.023, P = 0.009; β = −−0.123, SE = 0.040, t(45) = −3.087, P = 0.009; β = −0.022, SE = 0.008, t(45) = −2.749, P = 0.018). On the other hand, diversity measures were not significantly different between Noble fir and Sitka Spruce  = −0.620, P = 0.539; β = −0.019, SE = 0.040, t(45) = −0.476, P = 0.636; β = −0.008, SE = 0.008, t(45) = −0.963, P = 0.341). Finally, Shannon–Wiener diversity index and Simpson’s index of diversity were significantly lower in Noble fir than Scots pine standing trap trees  = −2.404, P = 0.042; β = −0.104, SE = 0.040, t(45) = −2.611, P = 0.025) but not Species proportion  = −1.786, P = 0.164).Tree species had a significant impact on the beetle diversity in standing trap trees. Shannon–Wiener diversity index, 1-Simpson’s index of diversity and Species Proportion were significantly lower in Sitka spruce than in Scots pine  and Cryphalus asperatus  with small log diameter, small bark thickness, and high distance from the ground). Differences in beetle species composition between standing trap tree samples are represented in F = 1.025, P = 0.387; F = 0.390, P = 0.813; and F = 0.163, P = 0.939). However, tree species was significantly correlated to the differences in communities between samples  = 13.068, P = 0.001). Numerical classification separated the standing trap tree beetle communities based on tree species but not forest sites  were significantly correlated to the communities found in the samples  and Sitka spruce . At the second sampling point (W20), after 12 months, all 3 indices were significantly higher for Sitka Spruce than for Noble fir . Shannon–Wiener Index and Simpson’s index of diversity were not higher in Sitka spruce than in Scots pine (P = 0.212 and P = 0.184) but species proportion was (P = 0.017). Finally, Scots pine had higher species proportion than Noble fir (P = 0.025) but not Shannon–Wiener Index and Simpson’s index values (P = 0.208 and P = 0.242). At the third sampling point (S20), tree species did not significantly impact Shannon–Wiener Index and Simpson’s index though the difference between Noble fir and Sitka spruce was almost significant (P = 0.088 and P = 0.095). On the other hand, species proportion was significantly higher in Sitka spruce than in Noble fir (P = 0.026).Proportion of species, Shannon–Wiener Index, and Simpson’s index of diversity of beetle communities in the log piles from each of the 3 tree species followed the same pattern over the sampling period . At the The 2-D ordination plots  indicateF = 12.994, P = 0.001), Ground level  = 11.755, P = 0.001) and sampling time  = 3.078, P = 0.013) all had a significant impact on the beetle community. Additionally, log diameter and bark thickness had a significant impact on the beetle community  = 7.380, P = 0.001; F = 7.472, P = 0.002).On the contrary, sun exposure and forest site did not have a significant impact on the beetle community present  = 1.587, P = 0.206; F = 0.246, P = 0.973).Tree species  in Scots pine, Sitka spruce, and Noble fir stands. The type of substrate was found to affect the beetle communities with those sampled from standing trap trees had lower diversity than those found in log piles. Tree species was found to be significant in structuring beetle communities at all stages of early succession, with Scots pine and Sitka spruce generally hosting more diversity than Noble fir. This sampling and DNA barcoding ID of larvae from standing trap trees or log piles provides precise knowledge on breeding substrate and nutrition requirements of individual species. While most of the beetle species found in the present study are not a source of concern for forestry, a few can be considered pests of conifers and were mainly found in trees in the initial stages of decay and early beetle community succession.Kyklioacalles roboris (Curtis) and Rhagium mordax (De Geer)) usually associated with broadleaf trees were recorded here in the sampled conifer species. Nevertheless, larvae are not usually sampled in monitoring projects due to the lack of taxonomic expertise and the difficulty of their identification  and no ambrosia beetles or wood wasps were found . Even though screening the xylem for beetles was not part of our sampling protocol, many species of wood-feeding larvae begin their lives in the phloem before moving into the sapwood (Trypodendron lineatum (Olivier); Because of their small size and cryptic lifestyle, collecting and studying bark and wood-boring larvae can be challenging. Uncovering the entire diversity of species feeding on the bark and the wood of dying and dead tree requires exhaustive sampling protocols. Because only the bark was sampled in our experiments, communities in branches, xylems, and stumps were not represented in our results. For instance, the number of Cerambycids found here was low . Our results support previous studies suggesting that host tree plays a fundamental role in structuring xylophagous communities  and thataf trees , but wer, Cryphalus asperatus is sometimes abundant in the UK while C. piceae is recorded as a nonestablished introduced species in the UK  is of similar size to P. chalcographus (Linnaeus), a major pest in Europe where it attacks Norway spruce and other European Pinaceae (Molorchus minor and Pissodes pini are known to damage trees of the genus Pinus and Picea that are severely stressed or dying (Fewer beetle species were caught and identified in the standing trap trees than in the log piles (respectively 6 and 12). This could be due to the lower number of logs sampled from standing trap trees versus piles . However, logs from the standing trap trees experiment had a significantly lower proportion of species, lower Shannon–Wiener index, and lower 1-Simpson’s index than logs from the log pile experiment. Moreover, the accumulation curves suggest that this pattern would likely be confirmed by a more intensive sampling . Despite the low species diversity, a high proportion of species found in standing trap trees (when compared to species in the log piles) have the potential to become significant pests or are closely related to beetle species with significant economic impact. For instancen the UK . Neithern the UK . As anotots pine . PityogePinaceae . Molorchor dying  and are or dying .H. palliatus is a very competitive species, good at building up in numbers, which only uses dead material as it cannot deal with resin defenses. In contrast, Hylastes spp. (black pine beetles) and H. abietis were found in the log pile experiment and are pests of new coniferous plantings . Both experiments (standing trap trees and log piles) were useful to depict different aspects of the beetle community succession, and to identify pest species present on the different sites studied. The results reported here provide informative material for decisions on monitoring and management of potential pest species and on conservation and promotion of beneficial species. By better understanding the factors influencing the abundance and diversity of species, management techniques can be developed and tailored to limit the abundance of pest species while simultaneously increasing the abundance of secondary beetle species. The latter are very important for the dead wood decomposition process and prevent potentially damaging beetle species from building up in large numbers by competing with them for breeding substrate.Generally, although lower species diversity was found in the standing trap trees, species identified from this experiment were more likely to cause damage to forestry trees in the future whereas species found in the log piles were more likely to be beneficial species, unless they could damage trees at other stages of their life cycle (e.g., nvad080_suppl_Supplementary_Figure_S1Click here for additional data file.nvad080_suppl_Supplementary_MaterialClick here for additional data file.


Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1‐year mortality among hypertensive patients who underwent CABG.ML algorithms can significantly improve mortality prediction after CABG.Tehran Heart Center's CABG data registry was used to extract several baseline and peri‐procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1‐year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models.Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50–59 and 80–89 years), overweight, diabetic, and smoker subgroups of hypertensive patients.All ML models had excellent performance in predicting 1‐year mortality among CABG hypertension patients, while LR was the best regarding AUC. These models can help clinicians assess the risk of mortality in specific subgroups at higher risk (such as hypertensive ones). Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg following two separate examinations in patients' history and/or taking antihypertensive medications. All the perioperative data of patients were collected and managed by expert nurses in our center. The ethics committee of Tehran Heart Center approved this study (IR.TUMS.THC.1401.023).2.2Baseline characteristics including demographic, preoperative, and intraoperative variables were used as potential predictors. Age, gender, weight, height, and body mass index (BMI) were demographics. Serum hemoglobin (Hb), high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), total cholesterol, triglycerides (TG), and creatinine, in addition to left ventricular ejection fraction (EF) measured by echocardiogram, diabetes, opium consumption, smoking status, prior myocardial infarction (MI), preoperative heart failure (HF), and chronic obstructive pulmonary disease (COPD) were preoperative variables. Finally, hospitalization parameters and intraoperative variables were total ICU hours, total ventilation hours, and cardiopulmonary pump utilization (on‐pump or off‐pump). All these data were obtained from either past medical records or blood sample measurements during hospitalization episodes and before surgery.2.3The study's main outcome was 1‐year mortality post‐CABG, for which we compared different ML‐based prediction models. This outcome included both in‐hospital and after‐discharge mortality events.2.4Exclusion criteria were missing data in addition to out‐of‐range values such as: (1) Hb > 25 g/dl or Hb < 5 g/dl, (2) LDL‐C > 400 mg/dl, (3) TG > 1200 mg/dl or TG < 20 mg/dl, (4) HDL‐C < 5 mg/dl or HDL‐C > 100 mg/dl, and (5) creatinine > 15 mg/dl or creatinine < 0.2 mg/dl were excluded. As we had sufficient data to develop and test models, excluding missing data were possible.2.5In a random assignment process, the total hypertensive population was divided into train and test cohorts . The test cohort sample was used to evaluate and validate the ML models.To select the best predictors for mortality in the total population and each of the subgroups, a feature selection process based on the random forest (RF) model was designed using 10‐fold cross‐validation. This technique investigates the effect of each predictor alone and in combination with other predictive variables. RF feature selection works based on mean decrease accuracy (MDA) and the mean decrease gini (MDG). The former shows how much accuracy is lost if a variable is excluded, while MDG represents the contribution of each variable to the homogeneity of the nodes and leaves in the resulting RF. The higher these scores, the higher the importance of variable.k‐nearest neighbors, and it selects a set of neighbors which then generates new data using them.Our study population was completely imbalanced in terms of mortality, where its rate was only 3.39%. To tackle this common challenge in ML models, we performed the synthetic minority oversampling technique (SMOTE) to balance our data in the training sample. SMOTE works by identifying the minority group's As the last step of preparation for the model development, the “standard scaler” (from the scikit‐learn package2.6Predictive ML models used in this study were 1) logistic regression (LR), (2) extreme gradient boosting (XGB), (3) naïve Bayes (NB), (4) RF, and (5) Artificial Neural Network (ANN). The diagram for ANN and the number of layers are shown in Supporting Information: Figure  logistic2.7Performance evaluation was done using the following metrics: A) sensitivity and specificity; B) accuracy of prediction using 10‐fold cross‐validation; C) AUC score by plotting true positive against false positive rate. The threshold is the cut‐off to allocate a probability into a class label and is normally set at 0.5 (50%). Due to the highly imbalanced outcome in our study, this rate of 0.5 was tuned by utilizing 10‐fold cross‐validation in the train data to adjust the sensitivity and specificity of models.The primary metric for evaluating models was chosen as AUC  since it is independent of the threshold. To validate the findings, the best model in terms of AUC was implemented to measure the metrics for the most recent 30% of cases in terms of admission time (2013–2015). This method assesses the temporal validity of findings over time.2.8χ2 test and Fisher's exact test for categorical variables, in addition to an independent sample t‐test for continuous variables. A two‐sided p value of less than .05 was considered statistically significant. Prediction models were designed and evaluated for 1‐year mortality for the whole hypertensive cohort of patients and subgroups based on gender, age group, BMI, diabetes, and smoking status. All statistical analyses and model development were performed using Python (version 3.10). LR, NB, and RF models were implemented using scikit‐learn (1.0.2) library,Baseline characteristics are reported as mean ± standard deviation (SD) or proportion (percentage). The comparison was made using Pearson 33.1p < .001). Hb and EF were significantly lower in dead patients compared to alive ones. In addition, the prevalence of diabetes was higher in patients who died . Figure Totally, 8,493 hypertensive patients with a mean age of 68.27 ± 9.27 years (mean ± SD) were assessed for model development and evaluation. Of the mentioned population, 63.86% were male, 46.84% had diabetes, and 38.61% had a family history of CAD. Details of baseline characteristics of hypertensive patients in the CABG cohort are shown in Supporting Information: Table 3.2r, we determined correlations between the features. Total ventilation hours and BMI were used instead of total ICU hours and weight due to statistical correlation and more clinical acceptance. Figure The RF feature selector was used using a 10‐fold cross‐validation method to select top features given their AUC. Using Pearson correlation 3.3We designed five ML algorithms for the prediction of 1‐year mortality among hypertensive patients undergoing CABG. Table 3.4Models ran for each of the subgroups of hypertensive patients. Evaluation metrics for the LR model as the top prediction model in the whole population are illustrated in Figure 4In the present study, we attempted to apply and compare five alternative ML algorithms  concerning the prediction of 1‐year mortality among hypertensive patients undergoing CABG. The findings of this study clearly illustrated the power of ML in improving the prediction of 1‐year mortality, with all five ML models found to be able to predict 1‐year mortality with most of them showing AUC > 0.8 which demonstrates excellent predictive ability, according to AUC interpretation.Accordingly, accurate mortality risk prediction systems can play an essential role in improving the continuity of care and management after cardiac surgery, leading to an increase in the survival of patients. Several risk scoring tools, such as Society of Thoracic Surgeons (STS), EURO‐Score I and II, have been developed to predict mortality and detect the involved factors. However, several limitations of these scores in some surgeries or patient subgroups have been reported. Therefore, it seems that these scoring tools need to be modified and improved.It has been illustrated that ML could improve the forecasting quality of the traditional epidemiologic standard mortality models.As LR is sometimes considered a traditional model and not an ML algorithm, a meta‐analysis concluded that ML was superior to the LR model in terms of mortality prediction after cardiac surgery, with a nonsignificant trend toward the better predictive ability of each ML algorithm. Nevertheless, the clinical importance of such an enhancement remains challenging.The frequency of hypertension among patients who require CABG is notable, as a recent study reported that hypertension was present in 54.6% of patients who underwent CABG.Similar to our study, several studies aimed to investigate implementing ML models in certain groups of patients after cardiac surgery. For instance, Zhong et al.Feature selection is widely applied to removing irrelevant and unnecessary data, thereby could improve the accuracy and understanding of the ML models.p = .03).Consistently, in another study, LR, RF, and XGB models selected the mechanical ventilation time as an important perioperative factor for predicting mortality after CABG.Similarly, in this study, TG, age, creatinine, Hb, LDL‐C, total cholesterol, FBS, HDL‐C, and BMI were detected in order as other important features by RF. The importance of these factors has been shown in several studies using various ML methods or otherwise. Like our study, a recent survey reported the 25 important predictors selected by the RF algorithm for mortality after cardiac surgery, which include chronic HF, mechanical ventilation, sodium, blood pressure, Hb, age, creatinine, renal failure, dyslipidemia, and glucose.We applied each ML model for each of the subgroups of hypertensive patients, with the results implying that the LR model had the highest predictive ability for females, the age range of 50–59 and 80–89, overweight patients and diabetic cases, and smokers. This can elucidate the beneficiary effects of ML prediction models for each subgroup of patients and result in better utilization of such models in clinical settings. Although there are not many studies in this regard, the study predicting mortality in the elderly population undergoing CABG compared five ML models and found that XGB had the best predictive ability.Our study proposed a prediction model for the hypertensive population undergoing CABG. As it has been reported that hypertensive patients can contribute up to 80% of patients scheduled for coronary revascularization surgery,While our study was based on the databank, which provides a large population's various demographic, preoperative, intra‐operative, and postoperative information, there were several limitations. First, we used data from a single heart center in Iran. Although we tried to address this issue by performing a separate analysis on the most recent 30% of data to assess the model's validity over time, multicenter registry studies and assessment of the prediction models on other centers in the country are required to strengthen the generalizability of findings. Second, since the missing data of some relevant input features were high, we discarded them. Moreover, the potential effect of not including confounding variables should be considered. It should also be noted that there were no ECG data and follow‐up laboratory data available in this databank. Like usual real‐world clinical data sets, our data set is also composed of imbalanced data, which is a main methodological challenge in ML models. Several approaches have been suggested to resolve this issue, including oversampling the minority group, undersampling the majority group, and lowering the prediction threshold.5Five different predictions using ML models for 1‐year mortality after CABG in hypertensive patients were developed. After applying RF for feature selection, the 11 most important features for the mortality prediction were detected. Among them, the mechanical ventilation time and baseline EF were by far the most influential determinants. All ML models, including LR, XGB, ANN, RF, and NB, demonstrated acceptable predictive performance, with LR providing the greatest AUC. ML algorithms may pave the innovative way for early and accurate prediction of post‐CABG mortality in high‐risk groups, especially hypertensive patients. It could offer individualized tools for clinical decision‐making and management. However, the current study's findings warrant further validation by more studies.Amir Hossein Behnoush and Amirmohammad Khalaji: Design, manuscript drafting, data analysis, and revision. Malihe Rezaee, Shahram Momtahen, Soheil Mansourian, Jamshid Bagheri, and Farzad Masoudkabir: Data gathering and manuscript drafting. Kaveh Hosseini: Supervision, design, manuscript drafting, and critical revision.The authors declare no conflict of interest.Supplementary information.Click here for additional data file.


GABRB3 variant, we here describe another patient with a loss‐of‐function GABRA1 variant (p.(Arg112Gln)) who benefited from vinpocetine treatment. This patient was diagnosed with autism spectrum disorder, psychiatric complications, and therapy‐resistant focal epilepsy. Upon add‐on treatment with 40 mg vinpocetine daily for 16 months, the patient experienced an overall improved quality of life as well as seizure freedom. Our findings corroborate that vinpocetine can attenuate epilepsy‐associated behavioral issues in patients with loss‐of‐function GABAA receptor gene variants.Vinpocetine is a synthetic derivative of the alkaloid vincamine and has been used as a dietary supplement for decades. Following a positive report of the use of vinpocetine in a patient with a loss‐of‐function  A receptor, GABRA1, GABRB3, and GABRG2, have been identified to cause different types of epilepsy ranging from mild genetic generalized epilepsy to severe developmental and epileptic encephalopathies.Variants in genes encoding subunits that form the major α1β3γ2 GABAVinca minor, improved clinical outcome of a 29‐year‐old woman with Lennox–Gastaut syndrome caused by a loss‐of‐function (LOF) variant in GABRB3. Upon add‐on treatment with 20 mg vinpocetine three times daily over a period of 9 months, this patient displayed improved language abilities, behavior, and reduced frequency of spike–wave discharge on electroencephalogram (EEG).Recently, Billakota et al.GABRA1, who has a history of intractable focal epilepsy and social behavioral disorders. Given the previous success in treating the GABRB3 variant patient and the fact that vinpocetine is generally considered safe for human consumption, we trialed add‐on treatment with vinpocetine in our patient. A remarkable positive outcome was observed, including reduced seizure frequency and better cognition and behavior.In our clinic, we have been treating a 17‐year‐old male with de novo LOF variant in The patient is a 17‐year‐old male born at term by healthy non‐consanguineous parents. Early development was unremarkable; however, mild language delay was noticed at the age of 3 years. He suffered from childhood‐onset fluency disorder that required support from a speech therapist. At the age of 11 months, he presented with his first febrile seizure (FS). In the following 22 months, he experienced five additional bilateral tonic–clonic seizures, provoked by fever (38–38.5°C). Since the third year of life, the patient started to suffer from afebrile focal seizures characterized by, (1) impaired awareness, occasionally evolving to bilateral TC seizure, or (2) impaired awareness and myoclonic jerks in the neck, shoulder, and arm, typically on the left side.GABRA1 c.335G>A p.(Arg112Gln), which is a recurrent GABRA1 variant that has previously been reported in 12 individuals with neurodevelopmental disorders and epilepsy.MRI was normal. EEG revealed occipito‐central 2–4 Hz spikes/polyspikes and slow wave complexes accentuated by sleep with anterior spread. Lamotrigine was initiated and he became seizure‐free. At the age of 6 years and 6 months, lamotrigine was replaced with valproic acid (VPA) because of possible adverse effect . In the seventh year of life, attention deficit hyperactivity disorder (ADHD) was diagnosed, and methylphenidate was introduced. He was seizure‐free until the age of 10 years and 11 months, where he started having monthly generalized tonic–clonic (GTC) seizures and daily FS characterized by: (i) hiccups possibly related to myoclonic jerks of the diaphragm, or (ii) eyes flickering and amaurosis, or (iii) visual aura followed by impaired awareness. VPA increase was associated with severe aggressive behavior. Thus, several drugs  were tried without improvement on seizure frequency; in addition, he started to suffer from anxiety and depression. EEG was unchanged, besides the appearance of photosensitivity. At the age of 13 years, he was diagnosed with obsessive compulsive disorder (OCD), autism spectrum disorder, and mild intellectual disability (IQ score at 53). At 15 years old, implantation of vagus nerve stimulator (VNS) in combination with brivaracetam and VPA lead to a reduction of GTC seizures from monthly to only two GTC seizures a year  variant. Patients with de novo GABRA1 variants are heterozygous for the variant. As pentameric GABAA receptors contain two α1 subunits, patients have mixed receptor populations where each receptor contains either zero (wild type), one (heterozygous), or two (homozygous) variant α1R112Q subunits. As previously shown, receptors with two variant subunits can be expected to be the more severely affected, while receptors with a single variant subunit display less impairment.R112Q subunit was accomplished using concatenated receptor constructs as previously described .Vinpocetine has been shown to have extensive pharmacological actions. It has primarily been thought to inhibit calcium/calmodulin‐dependent cGMP‐PDEGABRB3 and GABRA1 LOF variants. However, if enhancing the GABAergic system is responsible for the effects of vinpocetine, then caution is required when administering vinpocetine to other patients with GABR variants as these can also result in gain‐of‐function variants. Absalom et al.GABRB3 GOF variants and a similar scenario could occur with vinpocetine as add‐on treatment in these and other patients that have a GABAA GOF variant.Overall, we believe that it is reasonable to speculate that the potentiation of GABAergic activity constitutes an important component for the effects observed in patients with the AR variants and most importantly improve the patients' quality of life. Further studies in larger cohorts including younger patients and with a longer follow‐up are needed to confirm these results and to determine the best dosing practice, long‐term effects, and safety profile of vinpocetine.In conclusion, our findings suggest that vinpocetine may be effective in treating seizure and cognitive behavioral disturbances in patients with LOF GABAC.E G., P K A, R.S.M were involved in the conception and design of the study. CEG, TSM, KMJ, VWYL, MC, HAJN, EG, GR, PKA, and RSM were involved in acquisition and analysis of data. C.E G., P K A and R.S.M were involved in drafting a significant portion of the manuscript.The authors have no conflict of interest to disclose.The Australian National Health & Medical Research Council grant APP1185122  and the Novo Nordisk foundation grant NNF 0058749 (RSM).


We investigated the association of serum uric acid to high‐density lipoprotein ratio (UHR) with the presence and severity of metabolic syndrome (MetS) among MASHAD cohort participants.In this cross‐sectional study, according to International Diabetes Federation criteria, the cohort participants were divided into MetS (+) and MetS (−) groups. MetS (+) were classified into Group 1 (those with 3 MetS criteria), Group 2 (those with 4 MetS criteria) and Group 3 (those with 5 MetS criteria). UHR was compared among the groups.p < .001) in the MetS (+) group compared with the MetS (−) group. UHR increased as the MetS severity increased (p < .001). ROC analysis revealed that UHR greater than 9.5% has 89.07% sensitivity and 77.03% specificity in differentiating MetS (−) from MetS (+) subjects.Data related to 9637 subjects including 3824 MetS (+) and 5813 MetS (−) were analysed. The mean UHR was significantly higher  UHR is (significantly) higher in patients with MetS compared to those without MetS. (2) UHR increased as the MetS severity increases. (3) Serum UA is (significantly) higher in patients with MetS compared to those without MetS. (4) Serum UA increased as the MetS severity increases. Studies showed that increased insulin secretion increases serum UA levels due to a decline in renal function and serum UA concentration is independently correlated with MetS risk factors, HTN, kidney disease and cardiovascular events.prevalence of hyperuricaemia and MetS has been increasing not only in advanced countries but also in developing countries,the early diagnosis is a crucial issue. Also, due to both anti‐inflammatory and pro‐inflammatory properties of total HDL and since few studies assessed the relationship between UHR as a novel and easy‐to‐assess metabolic marker and MetS in a large‐scale population so far, the present study intended to compare the UHR between MetS patients and non‐MetS ones enrolled in MASHAD cohort studySince the 22.1The target population was recruited from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, a 10‐year prospective cohort from Mashhad city, northeast of Iran. The methodology and sampling details have been published elsewhere.Raised concentration of triglycerides (TGs): ≥150 mg/dL (1.7 mmol/L) or receives specific treatment for this lipid abnormality;Reduced concentration of HDL cholesterol: <40 mg/dL (1.03 mmol/L) in men and <50 mg/dL (1.29 mmol/L) in women or receives specific treatment for this lipid abnormality;Raised fasting plasma glucose concentration: ≥100 mg/dL (5.6 mmol/L) or previously diagnosed with DMII;Raised blood pressure: systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg or receives treatment for previously diagnosed HTN.In our study, MetS is defined based on the International Diabetes Federation (IDF).Then, subjects classified as MetS (+) group were divided into three subgroups based on the number of MetS criteria they had as follows: group 1 (patients with three MetS criteria), group 2 (patients with four MetS criteria) and group 3 (patients with five MetS criteria). Rest of the population was selected as MetS (−) group. The flowchart of current study design outlined in Figure 2.2All participants included in the study were informed about the study, and signed a written informed consent form before inclusion. The study was approved by the Ethic Committee of Mashhad University of Medical Sciences.2.3Anthropometric measurements including height, weight and waist circumference (WC) were obtained. Height was measured by a standard wall height meter. Weight was measured by an analogue scale, which was kept on a firm horizontal surface. WC was measured with a non‐stretchable fibre measuring tape. The body mass index (BMI) was calculated as the weight (kg) divided by the square of the height (m).2.4Participants were requested to relax for at least 15 min. Then, blood pressure of the right arm was measured and repeated after 10 and 20 min . If taking the blood pressure from the right arm was not successful, the left arm was considered. The mean of the three measurements was recorded as the participant's final blood pressure.2.5In our laboratory unit, a 20‐mL blood sample was taken from each participant. Blood samples were taken between 8 and 10 a.m. by venipuncture of an antecubital vein after 14 h of overnight fasting. According to standard protocol, samples were collected in vacuum tubes (20 mL) from subjects in a sitting position and immediately centrifuged at room temperature to separate the plasma and serum into six aliquots (0.5 mL) and then sent to the MASHAD study biobank. BT3000 biochemical analyser was used for determination of biochemical parameters levels including: fasting blood glucose (FBG), UA, high‐sensitivity C‐reactive protein (hs‐CRP), Low‐density lipoprotein (LDL), total cholesterol (TC), TGs and HDL. All devices were daily checked by a laboratory technician. UHR was calculated as the ratio of serum UA (mg/dL) to HDL (mg/dL).2.6t test. In addition, logistic regression (LR) algorithm was used to analyse data. In fact, we applied the algorithm to deduce the association between MetS and related factors. All analysis was performed using SPSS version 22 (IBM Corp.).To describe the quantitative and qualitative variables, mean ± SD and frequency (%) are reported, respectively. The Chi‐squared test and Fisher's exact test were applied to measure the association between categorical variables. Also, the mean of quantitative variables between the MetS (+) and MetS (−) groups were compared by independent 2.7LR is a popular model to evaluate the relationship between various predictor variables  and binary outcomes in medicine, public health, etc.Yi denotes the response variable and takes the values of 0 or 1 depending on whether response occurs or not. Also, X be vectors of covariates associated with response variable, β is the corresponding vectors of regression coefficients. So, the association between the covariates and binary response variable can be investigated as follows:Let logitEYi=logit{PrYi=1|Xβ=βTX3p < .001). In MetS (+) group, 1184 (30.96%) were male and 2640 (69.04%) were female and in the MetS (−) group, 2663 (45.81%) were male and 3150 (54.19%) were female. There was a significant difference between the two groups in terms of gender (p < .001). There was significant difference between the two groups in terms of BMI, WC, SBP, DBP, hs‐CRP, TC, LDL, TGs and FBG as well (p < .001). On the contrary, smoking status was not statistically significantly different between the groups (p = .53). The mean HDL level in the MetS (+) group was significantly lower than the MetS (−) group . The mean UA level in the MetS (+) and MetS (−) group was 4.89 ± 1.41 and 4.49 ± 1.36 mg/dL, respectively (p < .001). Accordingly, MetS (+) group had a significantly higher UHR compared to the MetS (−) group .A total of 9704 subjects were enrolled to the MASHAD cohort study. After applying exclusion criteria, data related to 9637 eligible patients were analysed (3824 with MetS and 5813 without MetS)  as well as hs‐CRP levels . There was no significant difference between the group 2 and 3 in terms of UA (Table p < .001). In addition, UHR was detected to be significantly lower in the MetS (−) group compared to the group 1, 2 and 3 . Group 1 had significantly lower UHR than group 2 and 3 (p < .001), whereas there was no significant difference between the group 2 and 3 (p = .33).Subgroup analysis demonstrated that in the MetS (+) group, there was a positive relationship between increasing number of MetS components (i.e. the severity of MetS) and serum UA levels . So, the main objective was to anticipate MetS using the LR model and to determine MetS associated factors.3.1p < .002) (Table p < .001). Also, after adjusting the effect of other variables for each increasing in UA, the odds of having MetS raised by 31% (p < .001) and for a 10‐unit increase in UHR, the odds of having MetS decreased by 54% (p < .001). Among the analysed variables, UHR , UA  and HTN  had the greatest associations with MetS. In addition, Figure Results from the multiple LR model revealed that variables including sex, TG, WC, HDL, glucose, SBP, DBP, HTN, UA, TC, DM and UHR were significantly associated with of the presence of MetS 2) Table . In othe Table . 4To the best of our knowledge, this is the first study conducted on possible association between UHR and severity of MetS in a large population. Our study showed that UHR was significantly higher in the MetS (+) group compared to the MetS (−) population. Subgroup analysis showed a correlation between the increases in the number of MetS criteria along with the increase in the UHR, serum UA and hs‐CRP.MetS is a tremendously growing public health problem, which is comprised of interconnected and heterogeneous metabolic origin risk factors that appear to be directly promoting the development of type 2 DM, adverse cardiovascular events and all‐cause mortality.The relationship between serum UA levels and MetS has been well demonstrated in the literature. According to numerous studies, there is a positive association between serum UA and the prevalence of MetS. Furthermore, serum UA level was elevated significantly as the number of metabolic components increased.HDL has anti‐inflammatory, antioxidant and atheroprotective properties with mitigates endothelial dysfunction through reverse cholesterol transport.In a study on 4551 patients diagnosed with type 2 DM in Shanghai, a positive correlation between UHR and diabetic macrovascular and microvascular complications such as CVD and chronic kidney disease (CKD) was observed in men and postmenopausal women, which may show the importance of measuring and lowering UHR level in a timely manner to prevent diabetic nephropathy. However, no correlation was observed between the prevalence of diabetic retinopathy and UHR level.1c in men with type2 DM, so the study suggested that UHR could serve as a promising predictor of diabetic control.The relationship between UHR and MetS has been investigated in few studies before. Kocak et al. pointed out that UHR could predict MetS better than all five MetS criteria and could use as a new a inflammatory marker.5The present study showed that among Iranian adult population, there is a significant positive correlation between UHR level as a novel easy‐to‐assess inflammatory and metabolic indicator and MetS. Furthermore, there is a positive association between UHR and MetS severity.Rana Kolahi Ahari: Conceptualization ; writing – original draft . Amin Mansoori: Conceptualization ; formal analysis . Toktam Sahranavard: Writing – review and editing . Monireh Sadat Miri: Writing – original draft . Sara Feizi: Writing – original draft . Majid Ghayour‐Mobarhan: Project administration .This research did not receive any specific grant from funding agencies in the public, commercial or not‐for‐profit sectors.Not applicable.All participants included in the study were informed about the study and signed a written informed consent form before inclusion. The study was approved by the Ethic Committee of Mashhad University of Medical Sciences.


Teratoscincus roborowskii, lives in extreme desert environments and has a flexible dietary shift to fruit‐eating in warm seasons. However, the effect of such shifts on the gut microbiota is poorly understood. In this study, 16S rRNA sequencing and LC–MS metabolomics were used to examine changes in the gut microbiota composition and metabolic patterns of T. roborowskii. The results demonstrated that the gut microbes of T. roborowskii underwent significant seasonal changes, and the abundance of phylum level in autumn was significantly higher than spring, but meanwhile, the diversity was lower. At the family level, the abundance and diversity of the gut microbiota were both higher in autumn. Firmicutes, Bacteroidetes, and Proteobacteria were the dominant gut microbes of T. roborowskii. Verrucomicrobia and Proteobacteria exhibited dynamic ebb and flow patterns between spring and autumn. Metabolomic profiling also revealed differences mainly related to the formation of secondary bile acids. The pantothenate and CoA biosynthesis, and lysine degradation pathways identified by KEGG enrichment symbolize the exuberant metabolic capacity of T. roborowskii. Furthermore, strong correlations were detected between metabolite types and bacteria, and this correlation may be an important adaptation of T. roborowskii to cope with dietary shifts and improve energy acquisition. Our study provides a theoretical basis for exploring the adaptive evolution of the special frugivorous behavior of T. roborowskii, which is an important progress in the study of gut microbes in desert lizards.Seasonal dietary shifts in animals are important strategies for ecological adaptation. An increasing number of studies have shown that seasonal dietary shifts can influence or even determine the composition of gut microbiota. The Turpan wonder gecko,  T. roborowskii. On the one hand, the core flora such as Firmicutes and Bacteroidetes have stable relative abundance, and on the other hand, other flora such as Proteobacteria and Lachnospiriaceae have flexible seasonal variation patterns. The correlation studies with metabolomics also indicate the stable state and dynamic changes of gut microbiota, which may be to adapt to the changes of food and meet the functional requirements of food digestion and metabolism. Our study provides a theoretical basis for exploring the adaptive evolution of T. roborowskii to special frugivorous behaviors.Seasonal dietary shifts led to the alterations of the gut microbiota in  It also maintains the homeostasis of gut microbes  varies seasonally on an ecological scale  and brown bears , plateau pikas (Ochotona curzoniae), brown frogs (Fejervarya limnocharis), black howler monkeys (Alouatta pigra), and giant pandas (Ailuropoda melanoleuca) have shown that the changes in animals microbiota composition that response to seasonal was driven by dietary shift  in captive and wild environments  and western lowland gorillas (Gorilla gorilla gorilla) indicate that dietary restriction is a potential factor driving specific changes in gut microbes. For example, increased consumption and fermentation of plant polysaccharides are associated with an increased abundance of Bacteroidetes and decreased abundance of Firmicutes. Lachnospiraceae and Erysipelotrichaceae were significantly associated with a high proportion of butyrate in feces. Carbohydrates in the diet are composed of soluble butyrogenic substrates , western fence lizards , mongolian racerunners (Eremias sargus), Puerto Rican Anolis lizards, and mesquite lizards (Sceloporus grammicus) under the influence of climate anomalies, temperature, ecomorphs, altitude, and other factors , northern grass lizards , and Puerto Rican Anolis lizards in captivity, captivity was found to significantly alter the gut microbiota of the lizards, and diet was considered the main factor responsible for this difference, which suggests that there is an extremely significant relationship between diet and gut microbiota in lizards , which also live in desert environments, can convert a small amount of carbohydrates into endogenous fructose owing to the stimulation of a high‐salt diet, thereby aggravating the tendency toward obesity and effectively adapting to the lack of food . We shined a flashlight on the ground and determined the location of T. roborowskii by the bright spots generated through the reflection of the light from its eyes, at the peak time of T. roborowskii activity (23:00–1:00). The experiment was divided into two groups: (1) 11 lizards were captured in spring  and 23 complete fresh fecal pellets were collected during the fasting period; (2) 15 lizards were captured in autumn , and 21 complete fresh fecal pellets were collected during the fasting period  ranged from 70.36 to 94.72 mm, with a mean of 82.27 ± 2.91 mm; body masses ranged from 12.44 to 24.71 g, with a mean of 17.72 ± 1.38 g. The autumn group's SVLs ranged from 69.36 to 88.09 mm, with a mean of 78.77 ± 1.46 mm; body masses ranged from 11.0 to 20.00 g, with a mean of 15.04 ± 0.68 g. Subsequently, the lizards were individually maintained in 30 × 21 × 15.5 cm (L × W × H) plastic rearing boxes, cleaned using 75% ethanol. A non‐invasive fecal sampling method was used to check for excretion every 2 h to ensure the timely collection of fresh fecal samples. Complete fecal samples were collected in sterile cryovials using sterilized tweezers. Samples were immediately marked and snap‐frozen in liquid nitrogen, then transferred to a −80°C freezer for subsequent experimental operations. Our previous study showed that the transit time of food through the digestive tract of 2.2In the spring group, feces were randomly selected from 2 to 3 individuals and mixed immediately to represent one sample, whereas in the autumn group, feces were randomly selected from 3 to 4 individuals. Four mixed samples were obtained from each group  using the primers 27F (5′‐AGRGTTTGATYNTGGCTCAG‐3′) and 1492R , and Lima (v1.7.0) software was used to identify the CCS sequences of different samples through barcode sequences and remove chimeras to obtain Effective‐CCS sequences. Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using UPARSE  software  and nonmetric multidimensional scaling (NMDS). This was used to observe differences in the gut microbial community structure  was added, vortexed for 30 s, sonicated for 10 min in an ice water bath, and centrifuged at 13,400 g for 15 min at 4°C. Finally, 120 μL of the supernatant was collected, and 10 μL taken from each sample to be mixed into QC samples for machine testing.The feces used for LC–MS are the same as the samples used for DNA extraction. A fecal sample (50 mg) was weighed, and 1000 μL of the extract containing an internal standard (1000:2)  was added, and the sample vortexed for 30 s. Subsequently, magnetic beads were added, ground at 45 Hz for 10 min, sonicated in an ice‐water bath for 10 min, then let stand for 1 h, and centrifuged at 4°C, 13,400 Metabolites in fecal samples were detected in positive and negative ion modes and separated using a Waters Acquity 1‐Class PLUS ultraperformance liquid chromatography system . Primary and secondary mass spectrometry data were collected in MSe mode under the control of an acquisition software . In each data acquisition cycle, dual‐channel data acquisition was performed simultaneously at both low and high collision energies. The low collision energy was 2 V, the high collision energy range was 10–40 V, and the scI ion source was as follows: capillary voltage, 2000 V (positive ion mode) or –1500 V (negative ion mode); cone voltage, 30 V; ion source temperature, 150°C; desolvent gas temperature, 50°C; backflush gas flow rate, 50 L/h; desolventizing gas flow rate, 800 L/h  were processed using Progenesis QI software for peak extraction, peak alignment, and other data processing operations, based on the Progenesis QI (Waters Corp.) software online METLIN database  was performed on the metabolite data and the OPLS‐DA model was calculated using the R (3.3.2) , and metabolite data were dimensionalized using hierarchical cluster analysis (HCA). The metabolites were divided into different metabolite clusters, and data with a correlation 33.1In total, 468 OTUs were divided into spring (SG) and autumn (AG) groups Figure . Among t3.2p = .023 < .05) was significantly higher than that in SG  was significantly higher than that in AG  and Shannon (p = .029 < .05) indices were significantly higher in AG than in SG . According to the similarity analysis results, it is not difficult to see that the distance between the two groups is large and the distance within the group is small. In other words, the samples from the two treatment groups were well clustered, and the separation between the different groups was clear . The distribution within the group was more discrete in the spring group and more uniform in the autumn group, suggesting that food and environmental influences after hibernation promoted the clustering and stability of the gut microbes in Turpan wonder geckos , Lachnospiraceae , Akkermansiaceae , Erysipelotrichaceae , Tannerellaceae , Lactococcaceae , Pseudomonas , and others were highly abundant in fecal samples Figure .Bacteroides , Akkermansia , Parabacteroides , Roseburia , Pseudomonas , Odoribacter , and Alistipes , among others, were the dominant bacterial genera of T. roborowskii  to screen for microorganisms with significant differences in relative abundance between the two groups. At the phylum level, the relative abundance of Tenericutes  was significantly higher in SG than in AG; however, the abundance of Proteobacteria was higher in AG .Based on the above analysis of community structure and species composition, the gut microbes of Turpan wonder geckos changed to a certain extent in different seasons. We used LEfSe analysis based on the effect pattern of LDA , Tannerellaceae , and the uncultured bacterium Mollicutes RF39  were present at significantly higher levels in SG than in AG. In AG, Enterobacteriaceae , Marinifilaceae , Lachnospiraceae , and Pseudomonadaceae  were significantly more prevalent than in SG.At the family level, Erysipelotrichacea , Parabacteroides , Breznakia , and uncultured bacterium Mollicutes RF39  were significantly higher in SG, while Odoribacter , Rothella , Pseudomonas , and Roseburia  were significantly higher in AG , genetic information processing (8.0%), environmental information processing (6.5%), cellular processes (3.0%), human diseases (2.5%), and organic systems (1.4%).p < .05). Amino acid metabolism, terpenoid and polyketide metabolism, xenobiotic biodegradation and metabolism, cell motility, signal transduction, and lipid metabolism were significantly enhanced in AG . The two‐component system and glycine, serine, and threonine metabolism were significantly enhanced in AG  were sorted to get the top 10 metabolites in each group. The levels of hexaethylene glycol, leucyl‐tyrosine, scillipheosidin 3‐[glucosy‐(1‐>2)‐rhamnoside], 1‐O‐caffeoyl‐(b‐D‐glucose 6‐O‐sulfate), serinyl‐gamma‐glutamate, bradykinin hydroxyproline, phaseolic acid, TR‐saponin B, corepoxylone, and ethylparaben were higher in SG. Methyl dihydrojasmonate, dodecanoic acid, trans‐2‐hexyl‐1‐cyclopropaneacetic acid, cochliophilin A, “PC/24:0),” uridine diphosphate glucose, grevillol, benzyl hexanoate, trans‐cinnamic acid, etc. were higher in AG Figure .p = .05, VIP = 1, and FC = 1. Finally, 1264 differential metabolites were detected in the two sample groups, of which 668 were upregulated (higher in AG) and 596 were downregulated (higher in SG). The relative contents of deoxycholic acid glycine conjugate and 26‐methyl nigranoate were higher in SG. Simulansine, 3‐oxocholic acid, and asparaginyl‐proline were the three metabolites with the highest relative content in AG . Pearson's correlation analysis of significantly different metabolites and OTUs (microbes) was performed to determine the relationships between the metabolites and microbes Figure . The res4Teratoscincus roborowskii has evolved a range of adaptive behaviors to escape hot and arid desert environments. For example, the strict nocturnal activity rhythm of T. roborowskii  possesses a gut microbial composition highly similar to that of T. roborowskii, and remains stable after 28 days of fasting, suggesting that animals living in extreme desert environments have a high degree of control over gut microbes , this may be largely due to the special frugivorous behavior of T. roborowskii in the long‐term evolutionary. Furthermore, it was speculated that the richness of Proteobacteria was in an effort to the digestion of caper fruits and insects in autumn, which improve the utilization rate of food resources. Related studies have shown that some Enterobacteriaceae species possess sucrose‐specific phosphotransferase systems and sugar transporters with different functions and structures  in autumn may be an adaptation to the abundant heteropolysaccharides in caper fruits ; investigation ; methodology ; software ; supervision ; writing – original draft ; writing – review and editing . Wei‐Zhen Gao: Data curation ; investigation ; methodology ; software ; supervision ; writing – original draft . Yi Yang: Data curation ; methodology ; software ; writing – original draft ; writing – review and editing .We gratefully acknowledge funding from the National Natural Science Foundation of China  and the Innovative Team Foundation of Biology of Xinjiang Agricultural University, China (ITB202101).The authors declare that they have no competing interests.Appendix S1.Click here for additional data file.


Validation studies of the 21‐gene recurrence score (RS) previously demonstrated that adjuvant chemotherapy plus endocrine therapy (CET) was associated with a significant survival benefit in women with node negative breast cancer (BC) and RS >31. However, the TAILORx trial, did not quantify the benefit of adjuvant CET in older women with node negative hormone receptor positive (HR+) BC with RS ≥26. We hypothesized that CET would be associated with improved overall survival (OS) compared to endocrine therapy (ET) in women >50 with HR+/HER2‐node negative BC and RS ≥26.The National Cancer Database (NCDB) was queried to identify women >50 with RS ≥26 ER+/HER2‐BC pT1‐2N0M0. Chi‐square and logistic regression analysis determined the difference between ET and CET. OS was analyzed using a multivariable Cox model.p < 0.001) or poorly/undifferentiated tumors , pT2 , or lymph‐vascular invasion . After accounting for demographic and oncologic factors, 5‐year OS rates were significantly superior in women receiving CET vs. ET alone .We included 16,745 women—4740 (28.3%) received ET and 12,005 (71.7%) received CET. Women who received CET had: moderately (OR = 1.853, We observed that CET was associated with a clinically and statistically significant higher OS compared to ET alone in women >50 years of age with RS ≥26 pT1 and pT2 N0M0 HR+/HER2‐breast cancer, and which suggests that cytotoxic chemotherapy has an impact on reducing mortality that is independent of induction of premature ovarian failure. >26 pT1 and pT2 N0M0 HR+/HER2‐breast cancer, suggesting that cytotoxic chemotherapy has an impact on reducing mortality that is independent of induction of premature ovarian failure.Chemotherapy plus endocrine therapy was associated with a clinically and statistically significant higher overall survival compared to endocrine therapy alone in women >50 years of age with RS  We therefore performed a real world analysis utilizing National Cancer Database (NCDB) to estimate the overall impact of adjuvant CET compared to ET alone on OS in women age > 50 with HR+/HER2‐pT1‐2N0M0 BC and RS ≥26. We hypothesized that given the benefit of adjuvant CET regardless of age noted in EBCTCG meta‐analyses, CET would be associated with a statistically significant OS benefit in postmenopausal women >50 compared to ET alone.22.1http://ncdbpuf.facs.org). The hospitals participating in the CoC NCDB are the source of the de‐identified data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.A retrospective cohort study of the NCDB was performed.2.2The NCDB was queried to identify female patients age >50 with HR+/HER2‐pT1‐2N0M0 BC and RS ≥26 from 2004 to 2017. Clinical staging data for the cohort was based on TNM classification in American Joint Committee on Cancer (AJCC) 7th edition.2.3The primary outcome was OS. Analysis included univariable comparison of patient factors associated with CET (vs. ET). To compare the two groups, Wilcoxon rank‐sum test was utilized for continuous variables and chi‐square for categorical data. Difference is OS between the groups was analyzed using Kaplan–Meier survival estimates and compared through log‐rank test, with a subgroup analysis by T stage. To control for confounding effects, multivariable Cox proportional hazard analysis was performed.All statistical analysis was performed using STATA/MP, version 16.0 (Stata Corp LLC). Institutional Review Board (IRB) approval was exempted by the University Hospitals Cleveland Medical Center IRB as all data is de‐identified.3p < 0.001), had lower comorbidity scores , higher tumor grade , had lymphovascular invasion , and were more likely to have pT2 tumors . Moreover, patients who received CET were more likely to have private insurance , receive treatment at an academic/research facility , and have undergone mastectomy instead of breast conservation .A total of 16,745 BC patients met the inclusion criteria. Of these, 4740 28.3%) received ET and 12,005 (71.7%) received CET. We observed that CET use in patients with pT1‐2N0 BC in women 50 and older increased over time, from 62 in 2009 to over 1,800 patients by 2017 , African American race , and high comorbidity score  were associated with a significantly lower likelihood of receiving CET. Conversely, high tumor grade , the presence of lympho‐vascular invasion , and T2 stage  were associated with a significantly higher probability of receiving CET.A multivariable logistic regression was performed to determine whether any clinical factors were independently associated with CET Table . We founp < 0.001, Figure Kaplan–Meier estimates demonstrated that among women over age 50 with pT1‐2N0M0 HR+HER2‐BC and RS ≥26, those who received adjuvant CET had a significantly higher OS compared to patients who received ET alone, independent of T stage , high comorbidity score , higher grade tumor , the presence of lymphovascular invasion , and pT2 stage  were all associated with a significantly higher death risk.Multivariable Cox proportional hazard regression revealed that after controlling for demographic, clinical pathologic, age, comorbidities, and T stage, adjuvant CET was still associated with a significantly higher survival probability , when compared to ET alone in women >50 years of age with RS ≥26 pT1‐ pT2 N0M0 HR+/HER2‐BC.In addition to our primary findings, we also found that the CET utilization rate was 71.7% within the investigated cohort. We found key differences between patients who received only adjuvant ET and those who received CET which included: younger age, lower comorbidity scores, higher tumor grade, lymphovascular invasion, pT2 tumors, a higher prevalence of private insurance coverage, a greater likelihood of receiving care at academic/research institutions, and higher liklihood of mastectomy. Furthermore, through the application of a regression framework, we found that that older age, African American ethnicity, and elevated comorbidity scores were significantly associated with a diminished likelihood of receiving CET. Conversely, higher tumor grade, lymphovascular invasion, and pT2 stage tumors were robustly linked to an higher probability of adjuvant CET.Data from MINDACT, TAILORx, and RxPonder phase III trials—genomically driven adjuvant BC clinical studies—suggest that most of the benefit of adjuvant chemotherapy in reducing HR+/HER2 negative BC mortality in patients with genomically low risk tumors is confined to women under age 50 .n = 89, 6%). Therefore, the benefit of CET over ET reported here involves  women under the age of 50, since 30% of analyzed patients were under 50.The TAILORx study was specifically designed to assess the influence of adjuvant CET versus ET in women diagnosed with node‐negative, HR+ BC and a RS of 11–25.From a clinical standpoint, the outcomes of our study contribute to the growing body of evidence endorsing the utilization of the 21‐gene RS in guiding decisions regarding adjuvant chemotherapy in women with HR+/HER2‐BC.Our study does have several limitations. The NCDB does not include local regional recurrence and disease‐free survival, thus our analysis of long‐term outcomes is limited to OS. Similarly, we do not have access to data regarding to the intensity of ER/PR expression, which is not included in NCDB. Another disadvantage of NCDB is that is does not include specific information on the type of adjuvant chemotherapy administered (anthracycline vs. non‐anthracycline) or ET (aromatase inhibitor vs. tamoxifen), nor the overall duration of ET—granular information, which we know from the published literature significantly influences both recurrence‐free and OS. Another limitation in our study is that age over 50 is an imperfect surrogate for menopausal status, but there were no other alternatives since NCDB does not capture menopausal status. Finally, the NCDB only receives data from Commission on Cancer (CoC) accredited hospitals, and therefore excludes patients treated in many non‐CoC accredited centers in the United States. On the other hand, the strengths of our study lie in the fact that it addresses a crucial clinical question that remains unaddressed by randomized clinical trials. Considering the improbability of any future clinical trial providing an answer to this question, the utilization of real‐world data is a reasonable approach. Additionally, our study benefits from the overall quality and substantial sample size of the real‐world data we have employed.In summary, using age as a surrogate for menopausal status, adjuvant CET does have a clinically and statistically significant impact in improving OS in women over 50 with pT1‐2 HR+/HER2‐, node negative BC and RS ≥26. Based on a real world analysis using NCDB, the overall impact of CET over ET in this patient population was a 32% reduction of death risk, translating into a 5‐year OS difference of approximately 2%. These findings are consistent with data from the EBCTCG, which have demonstrated an OS benefit of adjuvant CET in women over 50 with node negative HR+ BC. Future studies should prioritize prospective designs, delve into diverse patient populations, evaluate the impact of CET in various subgroups, and thoroughly assess the feasibility and effectiveness of integrating CET strategies within different healthcare systems.Nickolas Stabellini: Conceptualization ; methodology ; writing – original draft ; writing – review and editing . Lifen Cao: Conceptualization ; data curation ; formal analysis (lead); methodology ; writing – original draft (lead); writing – review and editing . Christopher W Towe: Conceptualization ; methodology ; writing – original draft ; writing – review and editing . Amanda L Amin: Methodology ; writing – original draft ; writing – review and editing . Alberto J Montero: Conceptualization ; methodology ; project administration (lead); resources ; supervision (lead); validation ; writing – original draft ; writing – review and editing .NS is supported through funding from the Sociedade Beneficente Israelita Brasileira Albert Einstein on the program “Marcos Lottenberg & Marcos Wolosker International Fellowship for Physicians Scientist–Case Western”.This study was provided Institutional Review Board (IRB) exemption due to the de‐identified data source.The abstract of this study was presented as a poster presentation at the American Society of Clinical Oncology Annual Meeting June 3–7, 2022, Chicago, IL.