Females were more likely to report severe problems with anxiety and depression than males (p < 0.006). The EQ visual analog scale (EQ VAS), which reports perceived overall health on a 0–100 scale, showed a mean of 74.7 (±19.6). Patients with comorbidities and/or complications perceived their health to be worse, with VAS scores significantly lower (p < 0.001). Other factors that negatively affected VAS scores were being female, obesity, insulin usage, and lower levels of education. In conclusion, patients with diabetes who have comorbidities and/or complications perceived their health to be worse. Further research is required to evaluate if current diabetes management interventions help improve patients' HRQoL.Diabetes is known to compromise patients' health-related quality of life (HRQoL). It is important to understand the HRQoL of Kuwaiti nationals living with diabetes and identify factors that negatively affect it. This study included 1,182 Kuwaiti nationals with diabetes, aged 18–80 years. Patients' demographic and HRQoL information was collected using the EuroQol EQ-5D-5L instrument. Mean values for each EQ-5D subscale were worst for mobility (1.9) and pain/discomfort (1.8). Most patients reported having no problems in self-care (84.4%). Older adults were most likely to report severe problems with mobility ( Globally, the prevalence of diabetes and its complications is growing, which places a considerable burden on national healthcare systems' resources and expenditures. The International Diabetes Federation (IDF) estimated the 2019 worldwide prevalence of diabetes in adults (aged 20–79 years) to be 9.3% (463 million people)  factors contributing to the clinical aspects of the successful management of diabetes treatment. HRQoL is defined in many ways, but in general, it involves a wide range of health- or treatment-related factors from three broad domains: physical, psychological, and social functioning . In the Understanding how diabetes and its comorbidities and complications affect the HRQoL of people living with diabetes is essential. It has been reported that patients suffering from diabetes complications are more prone to diabetes distress and depression, which will affect their health related quality of life negatively . HoweverFor this cross-sectional study, patients from all six Kuwait governorates, including one hospital and two primary care centers from each governorate, were recruited. Centers were selected if they were in areas with high populations of Kuwaiti nationals and if the center's administration were willing to participate in the study. In addition, two primary care centers with diabetes clinics were selected from a list of clinics provided by the Ministry of Health (MOH). The clinics were selected according to these criteria. Clinics that have a diabetes clinic, high number of Kuwaiti patients, and willing to collaberate. Due to a shortage of resources, we approached all eligible patients that entered the clinics on data collection days, meaning convenient sampling rather than systematic random sampling was used.For the sample to be representative of the Kuwaiti population, we used a multi-stage stratified sampling method. The first stage was the governorates. The second stage was the health facilities . The weight of the strata are accounted for in the sampling; in this case, the strata were the governorates. It was estimated that 741,648 Kuwaiti adults were living in all six governorates, according to the June 2018 census from the public authority of civil information. To determine the weights, we divided the number of Kuwaiti adults in each governorate by the lowest number of Kuwaiti adults found in the smallest governorate (Mubarak Alkabeer). To obtain the number for each governorate, the resulting weights were multiplied by our target total sample size , as shown in Kuwaiti adults (aged 18–80) diagnosed with either diabetes type 1 or type 2, with or without complications, were eligible and recruited for this study. The rationale of the age cut off was that we did not want to have pediatric population, because diabetes representation in pediatrics is different, hence above 18. The 80 upper limit was because patients above 80 years old are more likely to have memory issues and clinically complex cases due to age-related comorbidities not due to diabetes.Only Kuwaiti nationals diagnosed with diabetes during or before January 2018 were included.Non-Kuwaitis were excluded because they do not have the same access to care as Kuwaitis. Patients with dementia or who were not mentally capable of completing the interview were excluded. Because gestational diabetes is primarily a temporary type of diabetes, females with gestational diabetes were excluded.For this study, a data collection form was created through consultation with four diabetologists, a health economist, and five consultants specializing in diabetes complications . The form was used to collect each patient's demographic data and clinical data such as diabetes type, onset and duration of disease, most recent HbA1c level, comorbidities, and complications. The EQ-5D-5L tool was attached to the form.Data collection period started in August 2019 and ended by end of December 2019. The data collector approached patients waiting for their appointments in the seating area of diabetes outpatient clinics in hospitals and primary care centers. To decrease the convenient sampling bias, all patients in the seating area were approached, regardless of friendliness or willingness to participate. The data collector first screened each patient for eligibility by asking if the patient had been diagnosed with diabetes for at least 1 year, were an adult aged 18–80 years, and were a Kuwaiti national. If the patient was eligible and willing to participate, the data collector obtained consent by asking the patient to sign a consent form, then collected the patient's demographic and HRQoL data. Next, the data collector accompanied the patient to the entrance of the clinic and informed the physician that the patient had consented to participate in the study. The data collector then gave the clinical data collection form to the physician to complete for that patient. The physician collected the clinical data either from the patient chart or, if the information was not available in the chart, by asking the patient directly. After completing the form, the physician handed it to the data collector.Comorbidities were defined as patient diseases not caused by diabetes . Dyslipidemia is defined as elevated lipid levels in the blood. Complications were defined as patient diseases caused by diabetes . Physicians confirmed the presence of complications and comorbidities by reviewing the patient's medical record and by asking the patient questions, such as whether they visit a specialized doctor to monitor their complications, if they take medications for complications, or if they are experiencing any symptoms of complications.Data on patient HRQoL was collected using the EuroQol 5-dimension 5-level (EQ-5D-5L) standardized instrument for measuring health-related quality of life. The EQ-5D-5L instrument consists of two parts. The first part is the EQ-5D-5L descriptive system, which uses sub-scales to measure five aspects of quality of life: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Respondents provide a score from 1 to 5 for each sub-scale, where 1 indicates no problems and 5 indicates extreme problems. The second part of the EQ-5D-5L instrument is the visual analog scale (EQ VAS). In this section, every patient was asked to rate their perceived health on a 100-point scale, with 0 being the worst health one can imagine and 100 being the best  and Ministry of Health (MOH) Ethical Committee. Written informed consent was obtained from the patients who participated in the study.p < 0.05. IBM SPSS Statistics version 26 software was used to perform the statistical analysis.We used mean and standard deviation to describe continuous variables, and we used counts and percentages to describe categorical variables. To examine the correlation between the VAS score and the EQ-5D-5L sub-scales, we used the Spearman correlation coefficient. To assess the impact of comorbidity and complication status on the HRQoL evaluation, a set of regression models adjusting for significant covariates was performed. Significance level was set at The assumptions of both the linear regression and the ordinal logistic regression were checked to prove the data fit. For linear regression, a relationship was identified between each of the predictors used in the final model and the VAS score. The distribution of residuals for linear regression was approximately normal, and visual inspection of the plot of standardized residuals vs. standardized predicted values showed homoscedasticity. Testing the predictors proved no multicollinearity. Therefore, this method was determined to be applicable.A major assumption for ordinal logistic regression is the assumption of proportional odds, which means each independent variable has an identical effect at each cumulative split of the ordinal dependent variable. To prove this assumption, a parallel test was performed. The initial test showed the assumption was violated for 4 of 5 sub-scales. However, considering that the share of patients providing the highest score on each of the sub-scales (5 of 5) was relatively low (no more than 2%), the decision was made to merge them with the patients who provided a score of 4. This made the last group more representative and avoided the issue of accommodating many empty cells in regression models due to few patients in that category. The merging of the two sub-scale groups resulted in improvement in parallel test results, with all models fitting the assumption of proportional odds.Table 5) included gender, comorbidity and complication status, education level, and whether the patient was taking insulin. Other predictors including age, duration of disease, obesity, HbA1c score, and diabetes type provided insignificant impact on the VAS score after adjusting for all the covariates and therefore were not included in the final model.To create multivariate models, predictors with a significant impact on the dependent variable were selected. The final model for VAS scores  years . Most paAlmost two thirds of the patients (63.3%) were obese, with a mean body mass index (BMI) for all patients of 32.7 (±7.5). Fewer than half of the patients (45.7%) reported taking insulin. Only 25.0% of the study population had HbA1c levels <7.0% (controlled), almost the same number of patients (24.3%) had HbA1c levels of 7.0%−8.0% (nearly controlled), and over a third (39.3%) of patients had HbA1c levels >8.0% (not controlled).The patients were almost equally distributed between those with secondary and those with higher education (44.1 and 42.7% respectively). About two thirds of the patients were not employed, with 31.8% being retired or resigned and another 34.4% not working.p < 0.001) than patients with both comorbidities and complications, as shown in p < 0.001) compared to patients with both comorbidities and complications. On the other hand, female patients showed a decrease in VAS score , indicating a worse level of perceived health than males.Patients with neither comorbidities nor complications had higher (better) VAS scores  than illiterate patients, and patients with a middle level of education scored higher  than illiterate patients. Moreover, Taking insulin had a negative effect on VAS score. Patients who did not take insulin assessed their overall health as better  than those who use insulin.Education level also showed a difference in VAS scores, with increased education levels associated with higher (better) VAS scores. Patients with a higher level of education scored higher .p < 0.001; OR = 2.1, 95% CI: 1.1–3.8, p < 0.05; and OR = 1.9, 95% CI: 1.1–3.0, p < 0.05, respectively). However, on the anxiety/depression sub-scale, there was an opposite tendency as patients aged 51–65 reported lower degree of problems with anxiety/depression than younger patients .Age also had a negative impact on patients' assessment of most of the sub-scales. Patients ≥66 years of age reported more problems with mobility, self-care, and usual activities than patients <50 years old , the usual activities sub-scale , and the pain/discomfort sub-scale . Patients with only one comorbidity or complication were also more likely score lower (better) on the mobility and anxiety/depression sub-scales, although the impact was slightly lower . Compared to those with both comorbidities and complications, patients without comorbidities and complications were also less likely to score higher (worse) for mobility , usual activities , pain/discomfort , and anxiety/depression  sub-scales.Patients having only one comorbidity, or one complication were more likely to score lower (better) on all sub-scales compared to those who had both comorbidities and complications. This tendency for patients with only one comorbidity or complication to score better than patients with both was especially apparent with the self-care sub-scale , pain/discomfort , and anxiety/depression  sub-scales. Similarly, being obese led to a statistically significant increase in the likelihood of scoring higher (worse) on mobility , self-care , and usual activities  sub-scales.Those who do not take insulin were less likely to score higher (worse) than those who do take insulin on mobility  and usual activities  sub-scales. Patients with an elementary/intermediate/secondary education were also more likely to score lower (better) on the mobility sub-scale . Having a higher education level (diploma/university) also made patients much more likely to score lower (better) on the mobility , self-care , and usual activities sub-scales .The higher a patient's level of education, the lower (better) were the scores on the sub-scales. Compared to illiterate patients, those with an elementary/intermediate/secondary education were more than twice as likely to score lower (better) on the self-care .The duration of the disease did not show a significant impact on four of the five EQ-5D-5L sub-scales. The exception was pain/discomfort; each additional year of diabetes lead to an increase in the likelihood of scoring higher (more pain) in this sub-scale  negative relationship was shown between VAS scores and all EQ-5D subscales. For example, an increase in the depression subscale score always (p < 0.001) led to a decrease (ρ < 0.0) in overall VAS score, but the decrease was not high .A statistically significant  with type 1 diabetes was compared to children and adolescents without diabetes. That study revealed an HRQoL that was lower than the control group in the pediatric population . A studyIn our study of adult Kuwaiti nationals living with diabetes, we sought to identify patient characteristics and variables that affect patient-perceived HRQoL. Our analysis demonstrated that the variables that led to worse VAS and EQ-5D-5L sub scale scores (indicating lower perceived HRQoL) were being female, obesity (BMI ≥30), insulin usage, lower levels of education, and the presence of complications and/or comorbidities. However, a statistical association between two variables (for example insulin usage and HRQoL score) merely implies that knowing the value of one variable  provides information about the value of the other . It does not necessarily imply that one variable causes the other.These results appear to align with many aspects of previous studies in Kuwait and other countries, including studies that used different patient survey instruments , 11–15. Some sub-scale scores in our study aligned with another study done in Norway, which also used the EQ-5D instrument . In thatHowever, a study done in Saudi Arabia that used the EQ-5D instrument found different results than our study , with a Moreover, a study done in Iran looked at the factors that affected HRQoL using the EQ-5D instrument . They foA particular strength of this study was the inclusion of data from a large number of patients from all six governorates of Kuwait and from both hospitals and primary care clinics, which makes the data generalizable to the general Kuwaiti population across all provider settings. In addition, this study did explore the effects of both comorbidites and complications on HRQol in diabetes patients, these factors were not well-explored in other studies.One of the limitations in this study that it used convenience sampling, this may have resulted in a unrepresentative sample of diabetes patients in Kuwait. We only captured patients who sought care; sicker patients who don't go to diabetes centers may have been missed. In addition, patients who are nonadherent to their treatment plan may have been missed because they may be more prone to miss their doctor's appointment. Another limitation is that, for consistency and access, this study evaluated only patient data for Kuwaiti nationals. Because non-Kuwaitis are estimated to make up 65% of the Kuwait population , the resQuality of life is difficult to measure and has a high degree of subjectivity. The instrument we used (the EQ-5D-5L) is widely used and user-friendly, but it still has a high degree of subjectivity, as do other instruments. The results of the EQ-5D-5L from this study would be difficult to compare directly to studies that measure QoL specifically for diabetes, such as the ADDQoL.For Kuwaiti nationals living with diabetes and the healthcare providers who treat them, HRQoL is a concern that merits ongoing attention. The factors that contribute to lower perceived HRQoL in Kuwait are being female, obesity (BMI ≥30), insulin usage, lower levels of education, and the presence of complications and/or comorbidities. Understanding how these factors affect a patient's perceived HRQoL can help healthcare providers and patients work together to identify ways to mitigate them. For example, treatment plans may be modified so that diabetes-related complications are controlled or minimized, the importance of treatment adherence may be emphasized, or options for addressing mental health concerns may be discussed. Better diabetes management leads to better control of diabetes and its complications and comorbidities, which can improve overall quality of life.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Kuwait University (KU) Health Sciences Center (HSC) Ethical Committee and Ministry of Health (MOH) Ethical Committee. This study has been granted ethical approval from KU-HSC Ethical Committee on October 8th 2018, reference number (VDR/EC/3411). Also, it has been granted approval from MOH Ethical Committee on November 4th 2018, reference number (903/2018). The patients/participants provided their written informed consent to participate in this study.Conceptualization, methodology, and validation: MA and SA. Formal analysis, funding acquisition, investigation, project administration, supervision, and writing—original draft: MA. Resources: AAlad, TA, AAlat, and DA. Visualization: MA, SA, AAlad, TA, and AAlat. Writing—review and editing: MA, SA, AAlad, TA, AAlat, and DA. All authors contributed to the article and approved the submitted version.


The Astragali Radix–Cinnamomi Ramulus herb-pair (ACP) has been widely used in the treatment of diabetic peripheral neuropathy (DPN) as part of East Asian herbal medicine (EAHM). Eligible randomized controlled trials (RCTs) were identified by searching 10 databases. The outcomes investigated were response rate, sensory nerve conduction velocity (SNCV), and motor nerve conduction velocity (MNCV) in four regions of the body. The compounds in the ACP and their targets of action, disease targets, common targets, and other relevant information were filtered using network pharmacology. Forty-eight RCTs, with 4308 participants, and 16 different interventions were identified. Significant differences were observed in the response rate, MNCV, and SNCV, as all EAHM interventions were superior to conventional medicine or lifestyle modification. The EAHM formula containing the ACP ranked highest in more than half of the assessed outcomes. Furthermore, major compounds, such as quercetin, kaempferol, isorhamnetin, formononetin, and beta-sitosterol, were found to suppress the symptoms of DPN. The results of this study suggest that EAHM may increase therapeutic efficacy in DPN management, and EAHM formulations containing the ACP may be more suitable for improving treatment response rates to NCV and DPN therapy. Diabetic peripheral neuropathy (DPN) is a common complication, affecting around half of diabetic patients ,2. Both Natural products are considered a promising alternative treatment because they can be applied to the multifaceted lesions of DPN owing to their safety and higher patient compliance ,13,14,15As described above, in EAHM, the synergistic effect of drug combinations is as important as that of a single material. A combination of two or three materials capable of obtaining such a synergistic effect is called an “herb-pair”, which is the smallest unit of an EAHM polyherbal formula and an appropriate unit of analysis for investigating candidate therapeutics ,35,36,37Accordingly, we hypothesized that the ACP might be a combination that can show high efficacy in the treatment of DPN. To verify this hypothesis, this study was conducted as follows: (1) After a systematic search for randomized controlled trials in patients with DPN, a Bayesian network meta-analysis was performed to determine whether the EAHM formula containing the ACP was superior to that without the ACP. (2) Further network pharmacology analyses of the ACP were performed to predict the compounds and gene targets involved in the putative synergistic mechanisms. Therefore, we aimed to explore the efficacy of ACP in the treatment of DPN and its potential as a candidate drug.This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension statement for network meta-analysis . The proA comprehensive electronic search of four databases in English , and EMBASE, four Korean databases (Korean Studies Information Service System (KISS), Research Information Service System (RISS), Oriental Medicine Advanced Searching Integrated System (OASIS), and Korea Citation Index (KCI)), one Chinese database ), and one Japanese database (CiNii) was conducted by two investigators from inception to 20 July 2021. The following Boolean format was used for the search: (mononeuropathy (MeSH) OR nerve compression syndromes (MeSH) OR neuralgia (MeSH) OR polyneuropathies (MeSH)) AND (“neuropathy”(Title/Abstract) OR “peripheral neuropathy”(Title/Abstract) OR “neuropathic pain”(Title/Abstract) OR “neuralgia”(Title/Abstract)) AND  OR “Medicine, Kampo”(MeSH) OR “Medicine, Korean Traditional”(MeSH) OR “Herbal Medicine”(MeSH)). These search terms were appropriately modified to perform a search in the Korean, Chinese, and Japanese databases. The detailed search strategy is shown in Only randomized controlled trials (RCTs) evaluating the efficacy and safety of oral administration of EAHM for DPN were included. There were no restrictions on language, publication date, or type of diabetes. Studies were excluded if they met any of the following criteria: (a) not an RCT or quasi-RCT; (b) a control group was not used or was inappropriate; (c) unrelated to manifestations due to DPN; (d) animal experiments; (e) case reports or reviews; or (f) not published in peer-reviewed scientific journals, including postgraduate theses or dissertations. Trials were considered eligible for inclusion if they were conducted on adults (age > 18 years) diagnosed with DPN with no restrictions on age, sex, or race.RCTs trials comparing EAHM as an active intervention in the treatment group with CM in the control group were included. However, RCTs that used a combination of EAHM and CM as an intervention were beyond the scope of this review and were omitted. All dosage forms of EAHM intervention for symptom management in DPN, such as decoctions, granules, and capsules, were included. Studies in which East Asian medical interventions such as acupuncture, massage, or other nondrug therapies were only combined in the treatment group were excluded. Studies in which the control group included other EAHM were excluded. Even if all other inclusion criteria were satisfied, RCTs in which the exact constituent herbs of the EAHM formulation used as an intervention were not identified were excluded.The remission rate of DPN-related global symptoms observed according to the explicit criteria was selected as the outcome measure. However, most of the included studies reported the remission rates of complete remission (CR), partial remission (PR), mild remission (MR), and no remission (NR) as CR + PR/all patients. Considering that the remission rates reported by individual studies would have led to inconsistencies in the outcomes because different studies used different categorization criteria, the proportion of patients who achieved symptom alleviation in each group was used as the response rate in this review, and various study results were converted into this system.The first set of secondary outcomes was indices evaluating motor nerve conduction velocities associated with neurological abnormalities in patients with DPN. Therefore, to evaluate the neurological improvement of the motor nerves, the median motor nerve conduction velocity (MMNCV), ulnar motor nerve conduction velocity (UMNCV), peroneal motor nerve conduction velocity (PMNCV), and tibial motor nerve conduction velocity (TMNCV) were selected as indices for each upper and lower extremity. The second set of secondary outcomes was indices evaluating sensory nerve conduction velocities associated with neurological abnormalities in patients with DPN. Accordingly, the median sensory nerve conduction velocity (MSNCV), ulnar sensory nerve conduction velocity (USNCV), peroneal sensory nerve conduction velocity (PSNCV), and tibial sensory nerve conduction velocity (TSNCV) were selected as indices for the upper and lower limbs. Finally, the adverse events occurring in each intervention and control group were used as safety evaluation indicators.According to the aforementioned search strategy, titles and abstracts of potentially eligible studies were independently screened by two investigators (H.-G.J. and D.L.). Subsequently, a full-text review was conducted based on the inclusion and exclusion criteria. Information from the included studies was independently extracted by two reviewers (H.-G.J. and D.L.). The following information was collected: title, author name, the country where the clinical trial was conducted, diagnostic criteria, trial design, publication year, sample size, participant age, sex distribution, interventions in the treatment group and comparators, treatment duration, outcome index, reported adverse events, EAHM composition, and dosage. Discrepancies were resolved through discussions between the two investigators.Two investigators (H.-G.J. and D.L.) independently evaluated the methodological quality of each included study using the revised tool for the risk of bias in randomized trials, RoB 2 . RoB 2 ip-value based on the χ2 test was <0.10 or I² was ≥50%. Two-sided p < 0.05 was considered statistically significant. Statistical synthesis of individual research results was performed using the software R  and RStudio version 2022.02.3 build 492  using the default settings of the ”meta” and ”metafor” package [A pairwise meta-analysis (PMA) was performed to directly compare the EAHM with the comparator. Evidence synthesis of the included studies using the available data was performed by calculating the effect size and 95% confidence interval (CI) using a random-effects model. Heterogeneity was considered statistically significant when the  package . The RR  package . To addr package . The GRAA network meta-analysis (NMA) was performed to evaluate the relative efficacy of EAHM formulae containing AC herb-pairs and other interventions. In this review, Bayesian NMAs were performed using R v. 4.1.2, and RStudio V. 2022.02.3 build 492 to evaluate the comparative effectiveness of treatments, using the most commonly used control intervention as a common comparator. The default settings of the R packages ”BUGSnet” and ”GeMTC” were used for the implementation of NMA ,53. For https://tcmsp-e.com/) analysis platform [http://www.uniprot.org/) with the species filter “Homo sapiens”. Using “diabetic peripheral neuropathy” as the keyword, data on DPN-related target genes were obtained from the GeneCards database (http://www.genecards.org). For targets in GeneCards, only those with a score ≥ 10 were screened [https://bioinformatics.psb.ugent.be/webtools/Venn/). Using Cytoscape , a network of the components of the ACP and DPN targets was created to graphically depict the complex interactions between compounds and targets. The degree of each node is measured using a layout tool: the larger the node in the network, the higher the degree. The STRING protein analysis platform , together with the protein categorization “Homo sapiens”, was used to import the interacting gene targets of the ACP and DPN [https://metascape.org/), an online tool for gene enrichment analysis, incorporates more than 40 functional annotation datasets [p < 0.05.All bioactive ingredients in the ACP were screened and retrieved from the Traditional Chinese Medicine Systems Pharmacology  to evaluate the effects on the response rate, motor nerve conduction velocity (MNCV), and sensory nerve conduction velocity (SNCV) compared to the control group.p < 0.0001; I2 = 0%, p = 0.9420; p < 0.0001; I2= 22%, p = 0.2341; p < 0.0001; I2 = 0%, p = 0.9957; p < 0.0001; I2 = 65.6%, p < 0.0012; p < 0.01).In seven studies comparing the effect of EACP with the CM control, EACP significantly improved the response rate compared with the CM control  and Begg’s test .A contour-enhanced funnel plot analysis was performed to explore publication bias through the response rate, which was the outcome of most of the included studies. Since the pattern in the funnel plot displayed asymmetry, publication bias was deemed possible . This fiIn the comparison between the EAHM interventions and CM controls, the overall quality of evidence according to all outcome measures ranged from very low to moderate. The results of the GRADE assessment are presented in NMA was performed for all 16 treatments, and the network relationships between the treatments for each outcome are shown in The SUCRA plot for the response rate with MCB as a comparator is shown in The SUCRA plot for the MMNCV with MCB as a comparator is shown in The SUCRA plot for the PMNCV with MCB as a comparator is shown in The SUCRA plot for TMNCV with MCB as the comparator is shown in The SUCRA plot for UMNCV with MCB as the comparator is shown in The SUCRA plot for MSNCV with MCB as the comparator is shown in The SUCRA plot for PSNCV, with MCB as the comparator, is shown in The SUCRA plot for TSNCV, with MCB as the comparator, is shown in The SUCRA plot for USNCV with MCB as the comparator is shown in p = 0.9615; EACP vs. MCB, p = 0.9997; EAWP vs. ERT, p = 0.7399; EAWP vs. MCV, p = 0.6651; ECCP vs. ERT, p = 0.9645; ECCP vs. MCV, p = 0.9968; ECWP vs. ERT, p = 0.6720; ECWP vs. MCV, p = 0.7770). Additionally, for all studies related to response rate, no finding supporting heterogeneity was confirmed in the comparison of the posterior mean deviance between the consistency and inconsistency models  criterion index of OB ≥ 30% and DL ≥ 0.18 to identify the active components in the ACP. A total of 27 active ingredients derived from the ACP were identified. Of these, 20 compounds occurred in Astragali Radix, and seven occurred in Cinnamomi Ramulus . The DruThe ACP component–DPN target network was mapped using Cytoscape software version 3.9.1. As shown in Using the STRING 11.5 platform, we imported the common targets and constructed a PPI network, as shown in The results of the GO and KEGG analyses of the top 27 hub targets are shown in In our study, EAHM interventions were classified into four categories depending on the inclusion of the ACP and combination therapy with CM, and the comprehensive efficacy of EAHM interventions against DPN was compared with that of the CM control. EAHM showed considerably higher efficacy against DPN than the CM control, as determined by the response rate, SNCV, and MNCV indices, regardless of the mode of usage. The EAHM formula containing ACP was ranked highest in NMA for each treatment in terms of response rate, MMNCV, PMNCV, MSNCV, and USNCV. As a result, the ACP appears to be a candidate combination that can significantly influence the therapeutic response and nerve damage recovery in DPN. Based on network pharmacology analysis, the aforementioned study predicted that 10 compounds, including quercetin, kaempferol, isorhamnetin, formononetin, and beta-sitosterol, would act on 27 targets.This study has the following strengths: First, there are countless meta-analyses related to EAHM; however, to the best of our knowledge, this is the first network meta-analysis to investigate the synergistic effect of an herb-pair. The analysis performed in this study is expected to be useful in identifying the synergistic effects of EAHM through continuous improvements and developments in the future. Second, the mechanism of EAHM was reviewed at a deeper level using network pharmacology analysis in conjunction with NMA in clinical studies. Because the mechanism of action of EAHM is complex, detailed pharmacological information is often not discussed in clinical studies. Therefore, this study is valuable because it supports the efficacy hypothesis for DPN to be tested in EAHM clinical research. Third, the overall direction of this study was consistent with the proposal for determining candidate combinations for drug discovery. Meta-analysis is one of the most important clinical research methodologies; however, in the case of EAHM, personalized prescription is advantageous, and it is difficult to draw a firm conclusion about which material is valid owing to the heterogeneity between different EAHM formulae. The authors suggest that meta-analysis may be a useful tool for developing new drug candidates by scientifically validating the tacit knowledge associated with complex EAHM combinations.Due to the following limitations, caution should be exercised when interpreting the results of this study: First, although the EAHM formula containing the ACP at various NMA endpoints occupied the highest rank, the results were not consistent in terms of all indicators. This is mainly because the interactions with herbs other than the ACP also affect the efficacy, and few studies have performed a stable-effect comparison between multiple treatments. However, the design of this study was based on the premise that the ACP is a combination with appropriate compatibility, and its synergistic effect is stronger than that for other herbal combinations. To overcome these limitations, additional clinical trials are required to conduct updated NMA. Second, the quality of the studies included in the NMA was generally low, and no RCTs employed a double-blind design. This is another limitation that can affect the results. As a follow-up to this review, the validation of the effect of the ACP may be firmly established with new clinical trials with an improved design in the future. Third, in this study, the mechanism was analyzed using network pharmacology. However, as the compounds and targets of ACP have not yet been fully identified, database-based mechanism analysis based on data from previous studies revealed only predictive and not definitive mechanisms. Therefore, conclusions regarding the synergistic effects of the ACP and DPN can only be drawn through experimental studies. Prior to experimental testing, this study should be accepted to provide guidance.The significant difference between the effects of EAHM and the CM control, which was supported by the PMA data, is important because CM was used as a comparative treatment in most studies. Moreover, these results are encouraging because they are consistent with previous studies of similar design that investigated the effect of EAHM on DPN ,38,107. The EAHM formula containing the ACP occupied the highest rank among the multiple indicators included in the NMA target. Clinical evidence has established that EAHM formulae containing the ACP are useful for DPN, and the related mechanisms have been extensively explored ,42,108. It is important to understand the herb-pair theory of EAHM outlined in the introduction to accurately predict the synergistic effects of herbal medicine combinations and apply it for drug discovery ,114,115.Therefore, in recent years, an increasing number of studies have used various methods to identify the synergistic mechanisms of potentially useful herb-pairs ,124,125.The results of our study showed that ten active components, according to degree centrality, ensured the main effects of the ACP. Among these compounds, quercetin, kaempferol, isorhamnetin, formononetin, and beta-sitosterol are thought to exert synergistic effects ,136. QueEAHM may promote therapeutic efficacy in the management of DPN; when combined with CM, it can treat DPN significantly more effectively than CM alone. EAHM formulae containing the ACP may be more suitable than other EAHM formulae for improving NCV and the treatment response rate to DPN therapy, as determined by a network meta-analysis. The ACP has also been shown to cure DPN synergistically via multiple pathways and may work primarily through the IL-17 and MAPK signaling pathways to alter the pathophysiology of chronic diabetes mellitus in peripheral nerves. These observations suggest that ACP could be used to treat pain, paresthesia, neuroinflammation, and neurodegeneration in patients with DPN.The key ingredients in the ACP, including quercetin, kaempferol, isorhamnetin, formononetin, and beta-sitosterol, might have synergistic effects in neuroprotection, anti-inflammation, antioxidation, immunomodulation, and antinociception. These results suggest that ACP is a useful candidate for the treatment of DPN and should be studied further. However, the findings of this study should be verified through clinical trials and experimental studies. Despite these limitations, this study is valuable for proposing research hypotheses for candidate natural therapeutics based on herbal pairs and their synergistic effects.Finally, the above study exploring the synergistic effects of herbal medicine combinations is expected to verify the efficacy of licensed herbal medicines and classical EAHM prescriptions consisting of multiple herbs based on a traditional holistic perspective. Currently, the standardization of herbal formulations mainly depends on the content of a single indicator component; however, this information is insufficient to support efficacy. Therefore, an advanced test method to clarify the multilayered indications of herbal medicines is essential. To clarify the indications for complex herbal medicine prescriptions beyond the mechanisms and effects of individual herbal medicines, it is necessary to continuously and multidimensionally verify the synergistic effects explored in this study.


There have been no studies on the association between changes in smoking and alcohol consumption or combined changes in smoking and alcohol consumption frequencies and PD risk. To assess the influence of changes in smoking and alcohol consumption on the risk of Parkinson’s disease (PD).National Health Insurance Service (NHIS) database between January 2009 to December 2011 was analyzed. A total of 3,931,741 patients were included. Study participants were followed up for the incidence of PD until December 2017.Compared to the sustained non-smokers, sustained light smokers , sustained moderate smokers , and sustained heavy smokers  had a lower risk of PD. Compared to those who sustained non-drinking, sustained light drinkers (aHR 0.85 95% CI 0.89–0.91), sustained moderate drinkers , and sustained heavy drinkers  showed decreased risk of PD. Among non-drinkers, those who started drinking to a light level were at decreased risk of PD . Among non-smoking and non-drinking participants, those who initiated smoking only , drinking only , and both smoking and drinking  showed decreased risk of PD.Smoking is associated with decreased risk of PD with a dose–response relationship. Alcohol consumption at a light level may also be associated with decreased risk of PD. Further studies are warranted to find the possible mechanisms for the protective effects of smoking and drinking on PD, which may present insights into the etiology of PD. Neurological diseases are the leading cause of disability worldwide , and theRegarding smoking, previous studies have repeatedly reported a decreased risk association with PD. A previous meta-analysis of 61 case–control and 8 cohort studies reported that current smoking was associated with a 58% risk reduction of PD  . FurtherHowever, previous studies on the association between alcohol and risk of PD had conflicting results. A meta-analysis, in which eleven prospective studies were included, suggested that alcohol consumption was related with a decreased risk of PD  . The aboWhile numerous studies have explored the relationship between baseline frequencies of cigarette smoking and alcohol consumption with the risk of Parkinson’s Disease (PD), whether changes in these habits are associated with the risk of PD is rarely discussed. Understanding how modifying smoking and alcohol drinking habits can influence the risk of PD, especially when considering their combined effects, could help illuminate the potential link between reduced intake of both substances and an increased PD incidence. Given the sparce research on the influence of changes in consumption over time, our study aimed to examine how alterations in smoking and alcohol consumption—both separately and in combination—can affect the risk of Parkinson’s disease. To achieve this, we utilized a comprehensive dataset from the National Health Insurance Service (NHIS) of South Korea.This research used the NHIS database. The NHIS is the single government health insurer in South Korea. NHIS has covered approximately 97% of the South Korean population since being launched as the compulsory health insurance program in 1997 . The MedThe NHIS database includes reimbursement claims submitted by healthcare providers. This database also has data on demographics, medications, procedures, and disease diagnoses based on the International Classification of Disease, 10th revision (ICD-10). In addition, the NHIS supports biennial national health examination information. This information consists of anthropometric measurements, laboratory test results, and self-reported questionnaires. The questionnaire provides information on patient smoking frequency, alcohol consumption, and exercise programs. The detailed cohort profile of the NHIS database has been provided in a previous study .n = 7,241,136). To determine changes in smoking frequency and alcohol consumption, those who had a follow-up examination in 2011 were included as study participants . Since smoking and alcohol consumption are significant risk factors for cancer, myocardial infarction, and stroke, and because these conditions might affect one’s smoking and drinking habits, we excluded participants who (1) had a history of cancer , myocardial infarction , or stroke  prior to the index date  and (2) were diagnosed with cancer, stroke, myocardial infarction, PD, or had a recorded death within 1 year from the index date . We also excluded who had missing data for at least one variable . Since smoking frequency, alcohol consumption, and exercise information are self-reported, those who did not respond to any of these questions were also excluded from the study cohort. A total of 3,931,741 participants were selected for the final study population. This group was followed from 1 year after the date of the 2011 health examination until the date of death or until December 31, 2018  light smokers (< 10 cigarettes per day), (2) moderate smokers (10–19 cigarettes per day), and (3) heavy smokers (≥ 20 cigarettes per day). Past smokers were categorized into the same group based on their average daily smoking frequency and duration of smoking at the time they were smoking.Participants were asked about their total weekly alcohol consumption. The weekly alcohol consumption was calculated by multiplying the alcohol consumption frequency and amount per drinking occasion. We classified the amount of alcohol consumption into four categories: (1) none, (2) light drinker (less than 15 g/day on average), (3) moderate drinker (more than 15 g/day but less than 30 g/day), and (4) heavy drinker (more than 30 g/day) .The primary end point was newly diagnosed PD during the follow-up period. Newly diagnosed PD was defined by the ICD-10 code (G20 for Parkinson’s disease) and the registration code (V124) for PD. The Korean government has operated a registration program for rare intractable diseases (RID) since 2006. The enrollees of the program can reduce copayment by up to 10%, as compared to 20–30% for general medical care. Therefore, almost all PD patients are expected to be registered to this program. The inclusion criteria for V124 are based on parkinsonism-related symptoms but exclude secondary causes such as stroke or head injury. To participate in this program, a doctor, typically a neurologist or primary care physician, certifies the PD diagnosis in writing based on the clinical criteria . In S. KSeveral variables were included as covariates: age, sex, body mass index (BMI), and presence of diabetes mellitus or dyslipidemia. Diabetes and dyslipidemia were found to be related to the risk of PD in previous studies , 2020. Dt-tests, while categorical variables were assessed using either the χ2 test or the Fisher exact test.Continuous variables and categorical variables are presented as means ± standard deviations (SDs) and numbers (percentages), respectively, for descriptive statistics. For the baseline characteristics of the study participants, continuous variables were compared between the two groups using Student’s p <0.05 was considered statistically significant.The incidence of PD associated with changes in smoking and alcohol consumption habits was estimated by Cox proportional hazards regression analysis using a crude and multivariable-adjusted model. Final multivariate model was adjusted for age, sex, income, body mass index, and drinking status at baseline, diabetes mellitus, and dyslipidemia. Hazard ratios (HRs)were considered significantly different from the reference when 95% CI did not include 1. Hazard ratios (HRs) and 95% CIs were calculated. The comparisons were performed using two separate reference groups: sustained non-drinkers or non-smokers were used to compare the association across all categories, while sustainers  were used to compare the association within each baseline category. All statistical analyses were performed using SAS statistical package version 9.4 , and a value of During the mean follow-up period of 6.34 ± 0.62 years, 12,642 individuals were diagnosed with PD. PD patients were older (66.10 ± 8.80 years in the PD group vs. 55.02 ± 9.64 years in the non-PD group), and the PD group had a lower proportion of male subjects (48.99% in the PD group vs. 51.63% in the non-PD group) . AdditioCompared to the sustained non-smokers, sustained light smokers , sustained moderate smokers  and sustained heavy smokers  had lower risk of PD .We analyzed the effect of changes in smoking habit on PD incidence . Among nCompared to those who sustained non-drinking, sustained drinkers at all levels, sustained light drinkers (HR 0.85 95% CI 0.89–0.91), moderate sustainers , and heavy sustainers  had a decreased risk of PD. Among initial non-drinkers, those who began light drinking had a decreased risk of PD , and those who started heavy drinking showed even greater decrease in the risk , although the statistical significance was marginal . Among lAmong non-smoking and non-drinking participants at the first examination, those who initiated smoking only , drinking only , and both smoking and drinking  had decreased risks compared to those who continued to not smoke or drink.  Among noTo the best of our knowledge, this is the first study to demonstrate the effects of combined changes in frequency of smoking and alcohol consumption on PD incidence with a large-scale nationwide cohort database. We also found that smoking and alcohol consumption at moderate levels are associated with decreased risk of PD development.In our research, we found that sustained light smokers (aHR 0.80), sustained moderate smokers (aHR 0.54), and sustained heavy smokers (aHR 0.49) had lower risk of developing PD compared to sustained non-smokers. We also demonstrated that heavy smoking was associated with lower PD development risk with a clear dose–response relationship. In addition, we showed that smoking initiation or increased frequency of smoking was associated with decreased PD incidence, while smoking cessation or reduction was associated with increased risk of incident PD. Our results reinforce the conclusion of previous studies. The British study  and anotClinicalTrials.gov (Identifier: NCT038651210). This trial is a testament to the ongoing exploration of the efficacy of tobacco-derived components, particularly nicotine, in treating PD. The outcomes of such studies, combined with our findings, underscore the potential of tobacco chemicals as candidates for PD treatment.Findings in this study shed light on potential protective mechanisms of smoking against PD. Building upon previous research, there is a proposition that an effective medication for PD could be formulated using chemical substances derived from tobacco . NicotinOur study revealed that light sustained drinkers, moderate sustainers, and heavy sustainers showed a decreased risk of PD compared to those who sustained non-drinking. We also showed that those who started drinking at a light level (less than 15 g/day on average) had decreased risk of PD. This result is similar to findings in a previous meta-analysis of 11 prospective studies demonstrating that alcohol consumption was associated with decreased risk of PD. The association was nearly U-shaped . The risIn addition, we showed that initiation of alcohol consumption was associated with decreased future risk of PD, and drinking cessation was associated with increased risk of PD. Although a similar trend was observed, the increase or decrease in alcohol consumption did not reach statistical significance probably because of a smaller effect than that of the smoking and a U-shaped association. Furthermore, an increase of alcohol consumption to heavy drinking or reduction of alcohol consumption from heavy drinking was not associated with altered risk, further suggesting a U-shaped risk of PD with level of alcohol consumption . Our resAlcohol acts directly on the brain, increasing oxidative stress and inducing inflammatory reactions . Thus, aThe novel findings of our research involve the combined effect of smoking frequency and alcohol consumption on PD risk. In a previous study based on Korean NHIS data, the authors found that current smoking and alcohol consumption independently reduced the risk of PD in both men and women. In some populations, such as current male smokers and female ex-smokers, the authors discovered a super-additive interaction effect between smoking and alcohol use . HoweverOur study has several potential limitations. First, our analysis focused on individuals who participated in national health examinations in both 2009 and 2011. This may imply that our study population was generally healthier than the general population. Second, self-reported data on smoking and alcohol consumption might be underestimated, leading to a subdued inverse relationship in our findings. Third, our study did not account for the age at which individuals began cigarette smoking or alcohol consumption, as well as potential changes in smoking and drinking habit after the year 2011. Additionally, we did not factor in potential changes in smoking and drinking habits after the year 2011, which might have influenced the observed associations. Fourth, our study did not account for the duration of smoking and drinking habits, which could offer a more comprehensive perspective. Fifth, the estimation of PD incidence might not be accurate due to the challenges in identifying PD based on prolonged prodromal symptoms before an official diagnosis. However, it’s worth noting that in South Korea, patients with apparent clinical symptoms of PD are typically diagnosed and get medical care easily, given the national universal health coverage. Sixth, patients with cancer, stroke, and MI were excluded from our study. While these conditions were excluded based on their known associations with PD, they also represent some of the negative health consequences of smoking and drinking. This could potentially omit certain aspects of the relationship between smoking, drinking, and PD risk. Lastly, the smoking prevalence among South Korean women is notably lower than in Western countries. The smoking and drinking habits of South Koreans might also differ from other populations, potentially affecting the generalizability of our results. While the duration and quantity of alcohol consumption are crucial in establishing a cause-effect relationship, the frequency of heavy drinking episodes also emerges as a significant risk factor, warranting future research . MoreoveWe confirmed the presence of a dose–response protective effect of smoking and possibly alcohol consumption against the risk of PD. In addition, we also demonstrated that smoking and drinking habit changes can influence the incidence of PD. Further studies are warranted to find the possible mechanisms for the protective effects of smoking and drinking on PD, which may present insights into the etiology of PD.http://nhiss.nhis.or.kr). However, researchers should submit a research proposal to acquire the approval with an appropriate IRB document. Requests to access these datasets should be directed to KH, hkd917@naver.com.The data analyzed in this study is subject to the following licenses/restrictions: Data from the National Health Insurance Service (NHIS) of South Korea can be accessed from the website . The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.SYJ drafted the manuscript. DS and SC supervised the entire process. EC, KH, JY, YY, JEY, J-HM, and SMJ contributed to interpretation of data. All authors contributed to the article and approved the submitted version.J-HM received a grant from the National Research Foundation of Korea and SMC Research and Development Grant.J-HM has lectured, consulted, and received Honoria from Bayer Schering Pharma, Merck Serono, Biogen Idec, Sanofi Genzyme, Teva-Handok, UCB, Samsung Bioepis, Mitsubishi Tanabe Pharma, and Roche.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The development of artificial intelligence (AI)-based algorithms and advances in medical domains rely on large datasets. A recent advancement in text-to-image generative AI is GLIDE (Guided Language to Image Diffusion for Generation and Editing). There are a number of representations available in the GLIDE model, but it has not been refined for medical applications.For text-conditional image synthesis with classifier-free guidance, we have fine-tuned GLIDE using 10,015 dermoscopic images of seven diagnostic entities, including melanoma and melanocytic nevi. Photorealistic synthetic samples of each diagnostic entity were created by the algorithm. Following this, an experienced dermatologist reviewed 140 images (20 of each entity), with 10 samples originating from artificial intelligence and 10 from original images from the dataset. The dermatologist classified the provided images according to the seven diagnostic entities. Additionally, the dermatologist was asked to indicate whether or not a particular image was created by AI. Further, we trained a deep learning model to compare the diagnostic results of dermatologist versus machine for entity classification.The results indicate that the generated images possess varying degrees of quality and realism, with melanocytic nevi and melanoma having higher similarity to real images than other classes. The integration of synthetic images improved the classification performance of the model, resulting in higher accuracy and precision. The AI assessment showed superior classification performance compared to dermatologist.Overall, the results highlight the potential of synthetic images for training and improving AI models in dermatology to overcome data scarcity. With adDermatology, in particular, has witnessed substantial benefits from AI applications. The development of AI algorithms for the analysis of dermoscopic images has led to improved diagnosis of various skin conditions, including skin cancer . These aOne of the key challenges in the development of AI algorithms for medical applications is the need for large, high-quality datasets. However, obtaining such datasets can be problematic due to privacy concerns, limited access to data, and the time-consuming nature of data acquisition . This daTo address the issue of data scarcity, recent research has focused on the development of stable diffusion models, such as GLIDE (Guided Language to Image Diffusion for Generation and Editing), for generating high-quality synthetic images , 7. KathThe primary aim of this study is to explore the potential of the GLIDE model in generating synthetic dermoscopic images for use in AI algorithm development and dermatological education. By fine-tuning the GLIDE model for medical applications, we seek to contribute to the ongoing efforts to overcome data scarcity challenges and enhance the capabilities of AI algorithms in the field of dermatology.2.2.1.−5, Adam weight decay and unconditional probability set as zero, half-precision training set as false, batch size = 4, group sampling set as 8 for a total of 60 epochs.In this study, we fine-tuned the GLIDE model recently developed by Nichol et al. . This baWe began the fine-tuning process by initializing the GLIDE model with the pre-trained weights provided by the original authors. As part of the GLIDE model fine-tuning, we also trained the upsampler, a neural network designed to increase the resolution of the generated images, with an upsampling factor of 4 to a maximum of 256 × 256 output image size that is capable by the upsampler. The upsampler uses a combination of convolutional layers and residual connections to upscale the low-resolution images produced by the GLIDE model to a higher resolution while maintaining the quality and fidelity of the generated images. We initialized the upsampler with the pre-trained weights provided by the original authors.2.2.The evaluation of generated images was based on a combination of image quality metrics, including Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Frechet Inception Distance (FID), and Inception Score (IS). Ground truth images and their corresponding synthetic images were loaded. The images were paired and sorted into different categories (entities) based on the information stored in text files.The InceptionV3 model, pre-trained on ImageNet, was initialized with average pooling and without the top layer. The model was used to calculate FID and IS scores. For each category, the following metrics were calculated for the image pairs:SSIM: calculated separately for each color channel and averaged. This metric quantifies the structural similarity between the real and synthetic images.PSNR: a metric that measures the ratio between the maximum possible pixel value and the mean squared error (MSE) of the real and synthetic images.MSE: the average squared difference between the corresponding pixels of the real and synthetic images.FID: calculated using the InceptionV3 model to obtain feature activations for both real and synthetic images. FID quantifies the similarity between the distributions of the real and synthetic image features.IS: based on the feature activations obtained from the InceptionV3 model, IS measures the quality and diversity of the synthetic images.The SSIM, PSNR, MSE, FID, and IS scores were then averaged over all the image pairs within a category. The results were obtained for each category. Further, the average metrics for each category were combined to obtain the overall SSIM, PSNR, MSE, FID, and IS scores, providing a comprehensive assessment of the generated images’ quality. This evaluation approach ensures a thorough assessment of the generated images’ quality and similarity to the ground truth, considering various aspects such as structural similarity, pixel-level differences, feature distributions, and the diversity of the generated images.2.3.After completing the fine-tuning process for the GLIDE model and the upsampler and generation of the synthetic images, an experienced dermatologist (>10 years of dermoscopy experience) assessed the synthetic and ground truth images . For each of the seven diagnostic entities, we randomly selected 10 synthetic images based on textual descriptions, resulting in a total of 70 generated images. Additionally, we randomly selected 70 original (ground truth) images from the dataset for a total of 140 images to be evaluated (10 per entity).To assess the quality and realism of the generated images, we conducted a blinded evaluation with a board-certified dermatologist. Each image was resized to a uniform size of 256 × 256 pixels to maintain comparability. The dermatologist was provided with the 140 images  in a randomized order and asked to perform two tasks. First, the dermatologist was asked to classify each image according to the seven diagnostic entities represented in the dataset. Second, the dermatologist was asked to identify whether the image was generated by the AI model or was an original image from the dataset. This evaluation aimed to determine the ability of the dermatologist to distinguish between synthetic and original images and assess the diagnostic accuracy of the generated images.x-axis representing the predicted labels and the y-axis representing the true labels. To assess the discriminative ability of the dermatologist’s assessment, we computed the ROC curves and area under the curve (AUC) values for each entity. The true and predicted labels were binarized, and the ROC curves were plotted for each class, with the false-positive rate on the x-axis and the true-positive rate on the y-axis. Additionally, the ROC curve for AI-generated vs. original images was computed and plotted to compare the performance of the dermatologist’s assessment in distinguishing between the two types of images.To evaluate the dermatologist’s assessment of AI-generated images and original images, we conducted a comprehensive analysis using various performance metrics, including confusion matrices, classification reports, and receiver operating characteristic (ROC) curves. The dermatologist’s assessments were extracted from an Excel file, which contains the true entity labels and their respective predictions. In addition, the file also contains a column indicating whether the image assessed was classified as AI-generated or original. We then computed the classification report for AI-generated vs. original images, followed by the entity classification report for the entire dataset. Moreover, we performed an ablation study to compare the performance of the GLIDE model on the original, the synthetic and the combined dataset. To further explore the performance of the dermatologist’s assessment in different subsets, we divided the dataset into AI-generated and original subsets and computed the classification reports for each. Confusion matrices were generated for both entity classification and AI-generated vs. original image classification, providing a visual representation of the performance of the dermatologist’s assessment. These matrices were plotted with the 2.4.x-axis represents the epochs, while the y-axis represents the loss values. We evaluated the model’s performance using the test set. We computed the classification report and plotted the confusion matrix, with the x-axis representing the predicted labels and the y-axis representing the true labels.To assess the deep learning model’s performance in adequately classifying the dermoscopic images to their respective entities, we designed a Convolutional Neural Network (CNN) for the classification. We loaded the dataset of images and their respective labels . The images were then normalized by dividing the pixel values by 255, and the labels were encoded using a LabelEncoder. We divided the dataset into training and testing sets with an 80–20% ratio. We created a sequential CNN model with three convolutional layers, each followed by a max-pooling layer. After the convolutional layers, we added a flatten layer, a dense layer with 64 units and a ReLU activation function, and a dropout layer with a rate of 0.5. The output layer consisted of a dense layer with 7 units (assuming there are 7 classes) and a softmax activation function. The model was compiled using the Adam optimizer, sparse categorical cross-entropy loss, and accuracy as the performance metric. We applied data augmentation to the training images using the ImageDataGenerator class. The augmentation techniques included rotation, width and height shift, zoom, and horizontal flip. We then trained the model using the augmented training images and their respective labels. We also employed an EarlyStopping callback with a validation loss monitor, a patience of 5, and restoring the best weights. The model was trained for a maximum of 100 epochs with a batch size of 32. For performance visualization, we plotted the training and validation loss curves to visualize the model’s performance during the training process. The 2.5.All analyses were performed in Python. The following metrics were calculated for the assessment of the dermatologist and AI for classifying the entities:Precision: The proportion of true positive predictions among all positive predictions made by the classifier.Recall: the proportion of true positive predictions among all actual positive instances in the dataset.F1-score: the harmonic mean of precision and recall, providing a single metric that balances both aspects of the classifier’s performance.Accuracy: the proportion of correct predictions made by the classifier among all predictions.Macro avg.: the average of a particular metric  calculated separately for each class and then averaged without considering class imbalances.Weighted avg.: the average of a particular metric calculated separately for each class and then averaged, with each class’s contribution to the average weighted by its support .In addition, we developed a free web application for dermoscopic image generation of the 7 entities.3.3.1.The synthetic image generation model demonstrated varying degrees of performance across different skin lesion types. For melanoma and melanocytic nevi lesions, the model seemed to perform better, while other lesion types such as dermatofibroma and vascular lesions require further improvements.Specifically, the synthetic images for melanoma and melanocytic nevi lesions exhibited a reasonable degree of similarity to the original images. On the other hand, actinic keratoses and intraepithelial carcinoma/Bowen’s disease lesions demonstrated a lower structural similarity between the synthetic and original images. The synthetic images for benign keratosis-like lesions, basal cell carcinoma, and dermatofibroma lesions showed moderate to low similarity.The average metrics for all lesion types suggest that the model can generally reproduce the structural and visual features of the original lesions to a fair extent, albeit with room for further refinement. For a more detailed examination of the performance metrics such as SSIM, PSNR, MSE, FID, and IS, please refer to It is worth noting that the quality of the generated images varied across different categories of dermatological lesions. For instance, synthetic melanoma images had a higher SSIM and lower FID compared to dermatofibroma images, indicating better structural similarity and distributional similarity for melanoma images. Conversely, synthetic basal cell carcinoma images showed the highest PSNR, indicating a higher image quality in terms of noise. 3.2.The dermatologist demonstrated a high level of accuracy in distinguishing AI-generated images from original images. The overall accuracy in this classification task reached 96%. A balanced performance with a precision of 0.99 and 0.95, and recall of 0.94 and 0.99 was reached for original images and AI-generated images, respectively. The macro-average and weighted average f1-scores were 0.96 for both.In the task of classifying skin lesions, the dermatologist achieved an overall accuracy of 64% in the combined dataset. The performance varied across the different classes, with class 7  achieving the highest f1-score of 0.86, and class 2  exhibiting the lowest f1-score of 0.56. The macro-average and weighted average f1-scores were both 0.64.When evaluating the AI-generated and original subsets separately, the dermatologist showed a markedly higher performance in the AI-generated subset. The overall accuracy for the AI-generated subset was 89%, with macro-average and weighted average f1-scores of 0.88 and 0.89, respectively. In contrast, the overall accuracy for the original subset was 40%, with macro-average and weighted average f1-scores of 0.37 and 0.40, respectively. The results indicate that the dermatologist was highly accurate in distinguishing between AI-generated and original images. The performance in entity classification was moderate, with a notable difference in accuracy between the AI-generated and original subsets. The ROC curves for the dermatologist assessment of entities and AI versus the original are shown in 3.3.The dermatologist achieved an overall accuracy of 0.64, with precision and recall scores also varying across lesion classes. The highest precision and recall scores were observed for “vascular lesions” , while the lowest scores were seen for “benign keratosis-like lesions (solar lentigines/seborrheic keratoses and lichen-planus like keratoses)” .Comparing the AI model assessment to the dermatologist assessment, the AI model demonstrated a higher overall accuracy (0.86) compared to the dermatologist (0.64). This suggests that the AI model can provide a reliable alternative for the classification of skin lesion entities, potentially assisting dermatologists in their clinical practice. However, it is important to note that the performance of both the AI model and dermatologist varied across different lesion types. The confusion matrices for the classification of entities for AI and dermatologist are presented in 3.4.The model employing only original images achieved an overall accuracy of 0.65. Performance varied significantly across lesion classes, with relatively lower scores for “benign keratosis-like lesions”  and higher scores for “basal cell carcinoma” .The synthetic-only approach yielded an overall accuracy of 0.80. Notable improvements were observed in classes such as “melanoma”  and “vascular lesions” .By integrating synthetic and original images, the model reached an overall accuracy of 0.86. This combined approach enhanced precision and recall across all classes, with remarkable performance in “melanoma” , and “vascular lesions” . “Benign keratosis-like lesions” (Class 4) also saw a considerable boost .4.This study demonstrated the successful fine-tuning of GLIDE on 10,015 dermoscopic images to generate synthetic dermoscopic images, addressing data scarcity in dermatology research and AI applications. The results indicate that the generated images possess varying degrees of quality and realism, with melanocytic nevi and melanoma having higher similarity to real images than other classes. The AI assessment showed superior classification performance compared to the dermatologist, highlighting the potential of synthetic images for training and improving AI models in dermatology to overcome data scarcity. Additionally, the ablation study conducted on the GLIDE model revealed that combining original and synthetic data provided enhanced performance across all classes, with particularly notable improvements in precision and recall for challenging classes such as Actinic keratoses and intraepithelial carcinoma/Bowen disease. The combined approach yielded an accuracy of 0.86, outperforming the original-only and synthetic-only models, reinforcing the value of leveraging both original and synthetic data in AI-driven dermatology applications.The generation of synthetic dermoscopic images has the potential to revolutionize dermatology research and AI applications by providing a large, diverse dataset for training AI models . The resRecent advancements in text-conditional image models have enabled the synthesis of images based on free-form textual prompts, generating semantically plausible compositions with unrelated objects . HoweverInspired by the photorealistic sample generation capabilities of guided diffusion models and the versatility of text-to-image models in handling free-form prompts, we applied guided diffusion to text-conditional image synthesis in the medical field for the first time. Nichols et al. trained a 3.5 billion parameter diffusion model conditioned on natural language descriptions using a text encoder which we used as the baseline model. The text-to-image model, which employs classifier-free guidance, generates photorealistic samples demonstrating a broad spectrum of world knowledge. Human judges preferred the GLIDE samples to those from DALL-E 87% of the time when evaluating photorealism and 69% of the time when assessing caption similarity . When fuin-vivo confocal microscopy. Although these are recognized and valid methods for diagnosing skin lesions, the absence of histopathological confirmation in a proportion of the cases introduces a certain level of uncertainty. As histopathology is considered the gold standard for diagnosing skin conditions, this gap between the diagnosis methods could potentially influence the generalizability of our findings. In light of these considerations, while the HAM10000 dataset presents a valuable resource for developing and testing AI models for diagnosing skin lesions, future studies might benefit from incorporating natural lesion images and increasing the proportion of lesions confirmed through histopathology to further enhance the model’s real-world applicability and reliability.Despite the promising results, this study has some limitations. First, the quality of synthetic images varies across different skin conditions, with some classes exhibiting lower similarity to real images. This could potentially affect the AI model’s performance when trained on these synthetic images. Future research should aim to refine the image generation process for some entities and include a larger subset for these entities to ensure more consistent quality across all classes. Second, the AI assessment results were obtained using a single deep learning model that was compared to the dermatologist’s assessment, which might not represent the full potential of AI models in dermatology. Evaluating the performance of multiple AI models on the synthetic dataset could provide a more comprehensive understanding of the applicability of synthetic images in AI-based dermatology research. Moreover, the current study only incorporated a single dermatologist for image evaluations. Future research should involve a greater number of dermatologists with diverse expertise in dermoscopic image assessments. Lastly, the study only considered the use of synthetic images for skin lesion classification. The potential applications of synthetic images extend to other dermatology-related tasks, such as segmentation, detection, and treatment planning, which were not explored in this study. Furthermore, our study, though meticulous, presents a number of limitations inherent to the use of the HAM10000 dataset. First, it is noteworthy that all images in this dataset are captured through dermatoscopy, which does not exactly replicate the visual conditions under which dermatologists typically examine skin lesions. Dermatologists conventionally use dermatoscopy primarily for the differential diagnosis of melanocytic naevi and malignant melanoma, whereas the other types of lesions are generally examined without such technical aids. Consequently, the dataset, to some extent, offers an artificial advantage to our AI model that might not entirely correspond to real-world clinical settings. Second, while more than half of the lesions in the HAM10000 dataset are confirmed via histopathology, the remaining cases’ diagnoses are established through follow-up examinations, expert consensus, or In conclusion, this study demonstrates the potential of fine-tuning GLIDE to generate synthetic dermoscopic images for addressing data scarcity in dermatology research and AI applications. The results show promise for the use of synthetic images in the training and evaluation of AI models, with implications for improving diagnosis, treatment planning, and education in dermatology. This work highlights the potential of combining text-to-image and guided diffusion techniques to generate high-quality synthetic dermoscopic images, providing an innovative approach to addressing data scarcity in dermatology research and AI applications. Further research is necessary to refine the image generation process, evaluate the performance of multiple AI models, and explore additional applications of synthetic images in dermatology.The original contributions presented in the study are included in the article/AV, BS, MV, CS, ER, and VS: conceptualization. AV, MV, and BS: data curation. AV, VS, CZ, AK, JW, SH, and GL: formal analysis of results and datasets. AV, VS, CZ, and BS: methodological conception. AK, JW, CS, ER, and GL: resources for studies. AV, MV, VS, CZ, AK, and GL: validation of results. AV, MV, VS, and BS: visualization of results and writing – original draft. SH, CZ, AK, JW, CS, ER, and GL: writing – review and editing. All authors contributed to the article and approved the submitted version.


Eucryptorrhynchus brandti is a significant pest of the tree of heaven Ailanthus altissima, often leading to Ailanthus altissima death. Eucryptorrhynchus brandti adults usually aggregate to overwinter in soil or areas dense with weeds, and congregate on the bare phloem of the tree of heaven in the field during the non-winter months regardless of whether the trees are healthy or injured. Aggregation is a common phenomenon, and conspecific individuals gather in a limited area of the environment and increase social interaction. In this study, some factors were tested in the laboratory, indicating the environmental factors, potential chemical cues, and some physical signals that had the most significant effects on the aggregation behavior of E. brandti adults. This study of the aggregation behavior of E. brandti adults contributes to understanding communications and interactions among conspecific individuals.The tree-of-heaven trunk weevil Eucryptorrhynchus brandti (Harold) (Coleoptera: Curculionidae), is one of the most harmful pests that damage the tree of heaven, Ailanthus altissima Swingle . Aggregation behavior tests of E. brandti adults were conducted in laboratory conditions. The effects of temperature and light on the aggregation behavior of adults were tested, and the effect of sex and host was conducted with binomial choice experiments. The results showed that (1) the adults aggregate in both light and dark environments but preferred the dark environment, (2) temperature can drive the aggregation of E. brandti adults, (3) host plants could trigger E. brandti adults’ aggregation behavior, which is probably related to phytochemicals and insect feeding and localization, (4) there was mutual attraction of males and females and chemical attraction of crude intestinal extracts of males and females, and (5) aggregation behavior of E. brandti adults may also be related to the mediating of physical signals in insects. In this study, aggregation behavior can help us understand conspecific interactions and discover some strategies for effective control.The tree-of-heaven trunk weevil,  Eucryptorrhynchus brandti (Harold) (Coleoptera: Curculionidae) has one generation every year with overlapped generations and weak flight ability [Ailanthus altissima Swingle , a tree with a high ornamental value that plays an important role in China’s urban greening and shelterbelt construction [Eucryptorrhynchus brandti adults feed on the trunk of the tree of heaven, and larvae develop under the bark, destroying the phloem and xylem. Eucryptorrhynchus brandti is widely distributed in Beijing, Shandong, Hebei, Ningxia, Liaoning, Jilin, and other regions in China. The harm and the mortality rate of A. altissima damaged by E.brandti and close relative the weevil E.scrobicuatus are steadily rising in the whole second-generation farmland forest network in the Ningxia Hui Autonomous Region, China. Physical controls have been widely used to control E.brandti [E. brandti adults usually aggregate to overwinter in soil or in areas dense with weeds [The tree-of-heaven trunk weevil  ability . The spetruction ,3,4. Euc.brandti ,6. Howevth weeds , and conAllobates talamancae Cope and Eurasian scops owls Otus scops Linnaeus [Lasius niger Linnaeus and Schistocerca gregaria Forskal [Capnodis tenebrionis Linnaeus and Porcellio scaber Latreille [Aggregation behavior has been observed in lots of insects such as cockroaches and locusts ,9,10, anLinnaeus ,12. AggrLinnaeus . Aggrega Forskal ,15, and atreille ,17, and atreille .Hippodamia convergens Guerin-Meneville individuals prefer suitable temperature zones, and form clusters through the process of individual movement [Heterogeneities in the environment can drive animal aggregation. Animals always choose favorable environments and aggregate in limited space. For example, convergent lady beetle movement . Some ismovement . The premovement ,21,22, amovement .The interaction and communication between conspecific animal species can also contribute to the occurrence of aggregation behavior ,25. IndiE. brandti adult individuals and their response to environmental heterogeneity have been carried out. Here, we conducted choice and non-choice tests to compare the aggregation behavior or aggregation tendency of E. brandti reacting to multiple factors: (i) individual sex, (ii) light intensity of the habitat, (iii) the host plant A. altissima, and (iv) temperature. This study aims to investigate the effect of environmental factors, individual signals, and the host plant on the aggregation preferences and tendencies of adults of E. brandti, and provide a theoretical basis for new ways to control and manage the trunk pest. Few studies related to the individual interaction of Eucryptorrhynchus brandti adults were collected when they crawled out from the stems and fed on young branches of A. altissima for 3–5 days to adapt to the laboratory environment conditions. Approved by the Lingwu Natural Resources Bureau, several trees were felled in Lingwu Farm , Wudongshu Township , Wujiahu Village , Yinchuan City, Ningxia Hui Autonomous Region, China, and the cut wooden trunks were brought back to the laboratory . E. brandti adults to enter the shelters. Eucryptorrhynchus brandti adults were released at point C  containing two identical shelters placed symmetrically on either side of the center A. A 50 c point C A. One ofE. brandti adults under three temperature gradients, low temperature (5–15 °C), ordinary temperature (20–30 °C), and high temperature (30–40 °C), which were adjusted using an electronic semiconductor refrigeration and heating instrument . The filter paper was replaced in the glass arena under chamber A and chamber B. A semicircular hole with a radius of 1 cm was cut on each side of chambers A and B, to connect corridor C. A total of 600 males and 600 females were used in the experiment, of which 300 males and 300 females were used to measure aggregation behavior and the remaining 300 males and females were used to measure aggregation tendency. Aggregation degree  and max aggregation  [E. brandti adults at 10 and 90 min were counted and recorded. The experiment was repeated 10 times. Furthermore, the trend of aggregation was detected by observing the number of adults in the low-, ordinary-, or high-temperature zones arriving in the zone of ordinary temperature. Chamber A was kept at a low or high temperature or ordinary temperature, and chamber B was always at an ordinary temperature  for 30 s (flow rate 1.5 L/min) and observed the adults for 10 min. When the tested adults reached 1/2 of one of the arms or passed through it completely and stayed on the arm for more than 5 min, it was recorded that the tested adults could make a choice. If the tested adults did not decide within 10 min, the data of these adults were invalid. The inner arm was cleaned with 75 % ethanol and air-dried at ordinary temperature. After testing half of the adults in each group, the positions of the arms on both sides of the Y-tube olfactory instrument were reversed, and the remaining adults were tested by the same method.The intestines of 30 males or females treated with starvation were dissected, then soaked in 2 mL n-hexane for 45 min, and concentrated to 500 μL with a nitrogen concentrator to obtain the crude intestinal extract. A small blob of cotton with 50 μL crude extract was placed in the sample bottle as the odor source to compare with a small blob of cotton infused with 50 μL n-hexane in the sample bottle as the air source. The 90 males and 90 females were divided into three groups with 30 males and 30 females in each group. Each group of 30 females or males was tested. The experiment was operated according to the ‘gender factor’ experimental method. A total of 500 males and 500 females were tested. Ten males or females in each test were placed in two glass Petri dishes , and moistened filter paper was placed under the Petri dishes to maintain a level of humidity. The aggregation degree and maximum aggregation at 10 min and 90 min in the light environment (illuminated with a 40 W incandescent lamp) and the dark environment (glass Petri dishes covered with red glass film) number were recorded. This was repeated ten times. After each repetition, the Petri dish was cleaned with 75% ethanol. Additionally, one branch of the Y tube was wrapped with red film, one branch was left untreated, and both sides were illuminated with a 40 W incandescent lamp. Twenty males or females in each test were used to count the criteria for Y tubes in the ‘gender factor’ experiment, and the cleaning tube wall method was used to find the tendency to aggregate in light or dark environments. This was repeated ten times.E. brandti adults observed in the presence of the host plant and the absence of the host plant was compared using the Mann–Whitney U test and the Kruskal–Wallis test to study the influence of the host on the aggregation tendency. The paired t-test was used to compare the influence of the host on the aggregation tendency of males and females. Nonparametric tests  were also used to analyze the significance of the E. brandti adults’ aggregation in the temperature experiment and light–dark experiment. Moreover, the experimental data obtained through the Y-tube operation were analyzed with the independent sample t-test and chi-squared test.The number of E. brandti adults, both male and female, increased with time (p < 0.001). Moreover, there was no difference between males and females in choosing the host plant in the paired t-test analysis   . p < 0.001), max aggregation , and the number of selected adults  were significantly different when comparing the independent temperature variable. The aggregation degree was not significantly different between low temperature and high temperature , but there was a significant difference between low temperature and ordinary temperature , and between HT and OT  , but was significant between LT and HT , and between HT and OT  , but was significant between LT and OT , and between HT and OT  (p = 0.352), while the max aggregation  and the number of selected adults  were significantly different, using an exact sampling distribution for U . When tested at 10 min and 90 min, aggregation degrees, max aggregation, and the number of selected adults were statistically different.The test results show that aggregation degrees  a. The ma< 0.001) b. The nu< 0.001) c. MoreovEucryptorrhynchus brandti adults preferred to choose the odor source over the air (p < 0.001) and males (t = 25.732 p < 0.001) to air and odor sources  and also a difference in the response of males to air and odor sources  ( the air . There w sources a. Meanwh sources . When ma< 0.001) b, but th< 0.001) .t = −61.672, p < 0.001) and males  in the response to the crude extract odor sources and to air  and also a difference in the response of males to air and odor sources  when the crude extract of male adults was used as the odor source to attract E. brandti adults  (p = 0.126) (p = 0.046) and max aggregation  were statistically different between 10 min and 90 min. Comparing light with dark, the aggregation degree was not statistically significantly different , while the max aggregation was significantly different  , but the number of selected adults was not significantly different between females and males.The aggregation degree was not significantly different  a and the= 0.126) b when co= 0.784) . There wE. brandti adults [We found that host plants could trigger the adults’ aggregation behavior in this study. The number of adults that selected the host plant gradually increased within a certain time range, and most of the adults were found to stay on the host plants in experimental observations. This result showed that the host plant could further trigger the aggregation tendency of i adults ,32. In oi adults . A studyi adults . Furtheri adults ,36. MaleE. brandti was forced to aggregate at low or high temperatures and select the temperature zone of adaptation when faced with adverse stress of high and low temperatures. However, fire ants Thermobia domestica Packard are thermophilic, ranging from 30 to 43 °C, with an optimal temperature of approximately 37 °C, and high temperatures affect many aspects of T. domestica such as egg growth and development and microhabitat selection, and clusters are usually found in or near warm areas [E. brandti adults. Additionally, temperature can cause stress to the activity of E. brandti adults; we observed that adult activity was relatively slow at the low temperature, but they became rapidly active at the high temperature. Environmental heterogeneity, such as temperature and light, can drive the aggregation of the adults, resulting from the environmental preference to seek benefits and avoid harm. The aggregation degree, max aggregation, and the number of adults selected were different in different temperature zones. The weevils had a higher aggregation degree under the low temperature than under ordinary temperature, which indicated that the aggregation intensity of the weevil was slightly smaller, and the aggregation would be dispersed. Under the high temperature, the weevil clusters were larger both in both number and size compared to ordinary temperature, increasing the possibility of forming large clusters. Therefore, the results demonstrated that the weevils tended to cluster more under the low or high temperatures than under the ordinary temperature. rm areas . In somerm areas . HoweverE. brandti females and males under light and dark conditions. Eucryptorrhynchus brandti adults gather in both light and dark environments, but the adults clearly preferred the dark environment, whether female or male. Visual cues may play a role in selecting insects to search for suitable sites to perform a range of other behavioral functions [E. brandti adults can gather in both light and dark environments, which showed that the reason for the aggregation of E. brandti adults is not so much related to the difference in light and dark conditions in a narrow space, or the different light conditions is not dominant, but rather it is the signal from conspecific individuals that causes the aggregation. Communication signals between conspecifics include chemical and physical signals, which jointly mediate aggregation behavior [There were no significant differences in aggregation degree and maximum aggregation between unctions ,38,39. Ibehavior ,41,42. HE. brandti could attract male E. brandti but not females, and male E. brandti could attract both males and females. Similar results were seen in Cotesia marginiventris Cresson, in which it was found that the females did not attract or repel each other, but the males preferred the arms in which the females had been released [Tribolium castaneum Herbst presents one of the best-characterized examples in the Coleoptera [Rhinostomus barbirostris Fabricius [Metamasius spinolae Gyllenhal [In the conspecific interactive experiment, there were significant differences in the response of females and males to air and odor sources. When the female adults or the crude extract of the females were the odor source, the SRr of males was higher than that of females. When the male adults or the crude extract of males were the odor source, SRr of males and females was almost indistinguishable. These results showed that female released . We idenleoptera . Multiplabricius  and Metayllenhal . The potyllenhal . R. barbirostris was attracted by the aggregation pheromone released from the male, and a synergistic effect was found when the host volatiles functioned together with the aggregation pheromone [E.brandti and develop some more environmentally friendly traps using an attractant mixture of plant volatiles and pheromone. Additionally, pheromone-baited traps would provide an excellent means of identifying biodiversity hotspots, tracking population changes, identifying habitat thresholds for the persistence of target species at the landscape level, and providing feedback to evaluate the effects of conservation management efforts. Monitoring insects based on the exploitation of pheromones or other semiochemicals has the potential to revolutionize the conservation of many insect groups, such as Osmoderma eremita Scop. and Elater ferrugineus Linnaeus [E.brandti, but it is challenging to control E.brandti by temperature, and it is not easy to implement. From the point of view of speed and efficiency, the development of attractants is the most convenient means of control.Currently, pheromone attractants are widely used in pest control. For example, the beard weevil heromone . This coLinnaeus . MeanwhiIn conclusion, interactions between individuals within aggregates and collective decisions are especially relevant as synergistic responses of single individuals can lead to complex and nonintuitive behaviors ,50,51. E