Allergic rhinitis (AR) is a common condition that can be treated with a number of different therapies. Treatments such as intranasal antihistamines (INAs) and intranasal steroids (INSs) are widely used by AR patients. For some allergy sufferers, a combination of therapies, specifically an INA and an INS, is required to address their symptoms. A new treatment, the formulation of azelastine hydrochloride and fluticasone propionate used as a single spray (MP-AzeFlu), has become available for AR patients who need both types of treatment. In this regard, the comparison with the alternative concomitant use of INAs and INSs is of interest. The current study examines the health care resource utilization and costs for each cohort.To examine the resource utilization and costs associated with AR for patients treated with MP-AzeFlu or concurrent therapy with single-ingredient INA and INS sprays (free-combination therapy).A retrospective administrative claims study for commercially insured patients from a large U.S. health plan was performed. Patients with an AR diagnosis and a prescription claim for MP-AzeFlu or free-combination therapy between September 1, 2012, and September 30, 2013, were identified. Patients were aged at least 12 years at index date  and were required to have 12 months pre-index and 6 months post-index of continuous enrollment. Health care resource utilization and costs were assessed for the post-index period. The cohorts were adjusted on baseline demographic and clinical characteristics using inverse propensity treatment weights. Other covariates, prescriber specialty, product switching during the post-index period, and pre-index total costs were included in the regression models measuring outcomes. One clinical characteristic of interest was the presence of asthma as comorbidity. A subset analysis of AR patients with asthma was also performed.P < 0.001) with no significant difference in outpatient services and specialist visits . Six-month AR-related pharmacy and total costs were significantly lower (P < 0.001 and P = 0.001) for the MP-AzeFlu cohort  than the free-combination cohort . There was no statistically significant difference in AR-related medical costs between the 2 cohorts (P = 0.454). For the subcohort of AR patients with asthma, the MP-AzeFlu cohort had lower 6-month asthma resource utilization and costs than the free-combination cohort.All-cause-related pharmacy fills as well as pharmacy, medical, and total costs were significantly reduced by using MP-AzeFlu (N = 810) instead of the free combination of drugs (N = 726). For AR-related health care resource utilization, the MP-AzeFlu cohort had significantly fewer pharmacy fills than the free-combination cohort (1.01 and 1.17, respectively; These findings suggest that, for AR patients needing INAs and INSs, the single-spray formulation MP-AzeFlu had better economic outcomes than for patients who rely on the free combination of these agents. MP-AzeFlu also appears to keep asthma-related utilization and costs down for those AR patients who also suffer from asthma. Potential explanations for these findings are explored. What is already known about this subjectAllergic rhinitis (AR) is one of the most common chronic conditions in the United States, with close to 38% of patients also suffering from asthma.Successful management of AR can be a challenge and can lead to dissatisfaction with the more common pharmacotherapies .MP-AzeFlu is a safe and effective treatment of AR in patients who require dual therapy with an INS and an INA.What this study addsThis study compared MP-AzeFlu with free-combination therapy and examined the use of MP-AzeFlu and its effect on health care resource utilization and costs for patients with AR and those with concurrent asthma.AR patients prescribed MP-AzeFlu had similar or less utilization than patients taking a combination of INA and INS drugs (free combination); pharmacy costs and total costs were lower for patients on MP-AzeFlu, while AR-related medical costs were similar to those on the free combination of therapies.For AR patients also diagnosed with asthma, those prescribed MP-AzeFlu had similar use of emergency department services, outpatient services, and specialist visits, but significantly fewer pharmacy fills than those prescribed a free combination of INA and INS products.4 The prevalence of AR has been on the rise for many years, and it is currently the sixth most common chronic disease in the United States, leading to $18 billion of medical costs annually.5Allergic rhinitis (AR), characterized by sneezing, nasal congestion, nasal itching, and rhinorrhea, is one of the most common chronic conditions in the United States, affecting approximately 22% of adults and up to 40% of children.9 Asthma is a common comorbid condition in patients with AR.10 In the United States, up to 38% of AR patients suffer from asthma, and as many as 90% of patients with asthma suffer from concurrent AR.2 It is unclear whether AR is a trigger or component cause for asthma, but AR is a source of diminished quality of life for individuals with asthma. Additionally, in individuals with both conditions, AR is associated with poorly controlled asthma, including more frequent asthma exacerbations and asthma-related emergency department (ED) visits, while treatment of AR is associated with fewer asthma-related hospital admissions and ED visits.14Not surprisingly, AR has a significant detrimental impact on the quality of life of afflicted individuals as well as leading to substantially increased health care costs.15Most cases of AR respond to pharmacotherapy, including oral antihistamines, decongestants, intranasal steroid (INS) sprays, intranasal antihistamine (INA) sprays, and immuno-therapy. INSs and INAs are the primary medical therapies used and may alternatively be used as first-line monotherapy agents for patients with intermittent or mild persistent forms of AR.16 Escalation of medical management to include combination therapies is recommended for patients with more difficult to control disease, such as those with moderate-to-severe, persistent AR.15Despite the wide variety of therapies that exist, successful management of AR remains a challenge. Inadequate symptom control can often lead to dissatisfaction in patients taking prescription AR treatments, resulting in poor medication adherence or increasing reliance on over-the-counter medication.15 Previous research has demonstrated that this new MP-AzeFlu formulation has an excellent safety profile and has been shown to be more effective in controlling AR symptoms than other first-line therapies.17, 18 While previous studies have examined the clinical effectiveness of MP-AzeFlu compared with other therapies, there is a paucity of research on the impact of this AR treatment regimen on health care resource utilization and costs.Until recently, patients needing a stronger pharmaceutical treatment than INSs had limited options. A new treatment, the formulation of azelastine hydrochloride and fluticasone propionate used as a single spray (MP-AzeFlu), has become available for AR patients who need treatment with an INS and an INA.20 We focus specifically on health care utilization and costs related to AR and asthma in AR patients treated with either MP-AzeFlu or the free combination of INS and INA.The purpose of this study was to determine health care utilization and costs of patients with AR using MP-AzeFlu in comparison with those patients using the potential alternative free combination of INS spray and INA spray. In order to investigate this relationship, we performed a retrospective claims-based study of commercially insured individuals. Previous studies have likewise used insurance claims data to compare resource utilization and costs for medical treatments of AR.International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) associated with medical encounters and financial information. Pharmacy claims included fill dates for prescriptions, national drug codes, and drug cost data. The current study was submitted and approved by Schulman Associates Institutional Review Board before study initiation.All data for the current study were from the Humana administrative claims database between September 1, 2011, and March 31, 2014, for commercially insured patients. The database contained patient enrollment and medical and pharmacy claims data. Enrollment data included patient demographics such as age, sex, and geographic region. Medical claims data included diagnosis codes based on the Two cohorts of commercially insured patients were identified based on the type of intranasal treatment prescribed— MP-AzeFlu or free-combination therapy (INS and INA). Patients in both cohorts had to have a prescription claim for either MP-AzeFlu or free-combination therapy between September 1, 2012, (corresponding to the month of launch of MP-AzeFlu) and September 30, 2013, with 1 claim for a diagnosis of AR (ICD-9-CM: 477.xx) or chronic rhinitis (ICD-9-CM: 472.0) between September 1, 2011, and March 31, 2014. The first prescription claim identified for patients was designated as their index date. The index date for the MP-AzeFlu cohort consisted of the first prescription claim for that treatment, while the free-combination therapy cohort index date was the first prescription claim for an INA and an INS that occurred within 7 days of each other or showed an overlap of prescriptions for 90 days. All patients included in the current study had to be newly initiating on either of the therapies. This meant there could be no claim for an intranasal treatment prior to the index date for any participant to be included. To ensure study patients had no prior intranasal therapy claims, inclusion criteria included a 12-month period of health care coverage with no prescriptions and pharmacy fills for intranasal therapy.Further inclusion criteria consisted of patients being between the ages of 12 and 89 years and having at least 12 months pre- and 6 months post-index continuous enrollment. Patients were excluded from either cohort if they had 1 claim for a diagnosis at any point during the identification period  for endstage renal disease (ICD-9-CM: 585.6), cancer (ICD-9-CM: 140.xx-172.xx and 174.xx-208.xx), pregnancy , or organ transplant (ICD-9-CM: V42.xx); were older than 89 years; or had any claims for an INS or an INA within 1 year of the index date. Patients aged younger than 19 years and residing in Nebraska, Delaware, or Alabama were also excluded due to laws in those states prohibiting research using patients younger than 19 years. Demographic and clinical characteristics were examined for the 12-month baseline period for patients in each cohort. Demographic characteristics included age, sex, and geographic locale. Geographic locale was further broken out by states with the largest plan populations  and then by regions . The season for each patient’s index date was also tracked as winter ; spring ; summer ; and fall .21 Other pre-index conditions that were flagged and tracked included asthma (ICD-9-CM: 493.xx), acute sinusitis (ICD-9-CM: 461.xx), chronic sinusitis (ICD-9-CM: 473.xx), sleep apnea (ICD-9-CM: 327.23 and 780.57), nasal polyps (ICD-9-CM: 471.xx), allergic rhinoconjunctivitis (ICD-9-CM: 372.14), and nonallergic conjunctivitis (ICD-9-CM: 372.05).Clinical characteristics included a Deyo-Charlson Comorbidity Index (DCCI) score that was calculated for each patient.All-cause and AR-related health care resource utilization were calculated using all post-index medical and pharmacy claims. The index event was excluded from the post-index period. For the AR- and asthma-related utilization, only those medical and pharmacy claims associated with a diagnosis of AR or asthma were used. A diagnosis for AR or asthma could appear in any of the 9 diagnoses positions within the claims data. However, more than 90% of the claims for AR for the cohort patients were found in the primary position. Health care utilization was calculated for all 3 breakdowns  for ED services, outpatient services, specialist visits, and pharmacy fills. ED services were assessed by counting the distinct service dates at an ED as defined by the place of treatment code found on the medical claim. Outpatient services included the count of distinct service dates and distinct providers for claims where the place of treatment is listed as outpatient. Specialist visits were a subset of outpatient visits where the provider is a specialist as specified by the physician specialty code on the claim. Pharmacy fills consisted of the count for distinct service dates (fill date) and national drug code. Pharmacy fills were normalized so that each fill could be equated to a 30-day supply. Furthermore, the INA and INS fill pairings for the free-combination cohort were counted as 1 fill for the index fill and all subsequent pharmacy fills. Both prescriptions were key for patients to be included in the cohort. By counting the combination of prescriptions as 1 fill, there would be no artificial representation by including each product as 1 fill.For AR-related utilization, medical utilization was defined as any claim containing a diagnosis code for AR ; chronic rhinitis (ICD-9-CM: 472.0); acute sinusitis (ICD-9-CM: 461.xx); or chronic sinusitis (ICD-9-CM: 473.xx); or if the claim line procedure code was associated with allergy testing  or allergy immunotherapy (CPT-4: 95115-95199). Pharmacy utilization was defined as any pharmacy claim for a product used to treat allergies .For asthma-related utilization, medical utilization was defined as any claim containing a diagnosis code for asthma (ICD-9-CM: 493.xx). Pharmacy utilization was defined as any claim for a product used to treat asthma (see Appendix for complete list).Similar to calculations performed for health care utilization, all-cause, AR-related, and asthma-related health care costs were calculated. Costs were examined for all medical, pharmacy, and total  costs. The allowable amount, defined as the amount remitted to the providers, found in the claims data was used in the analysis of health care costs for the current study. As in health care resource utilization, the index fill cost was excluded from the post-index period calculations with all postindex costs measured during the 6-month period following the index date for patients. All health care costs were adjusted to 2013 dollars, as it was the last complete calendar year for the study observation. The medical consumer price index is used for the adjustment.All analyses were conducted using SAS software package 9.3 . For the demographic and clinical characteristics, means and standard deviations were calculated for continuous variables. For any categorical variables, counts and percentages were calculated. For the demographic and clinical characteristics, comparisons between the cohorts were conducted using t-tests for all continuous variable data and chi-square tests for all categorical data.22 IPTW was used to balance the differential characteristics of patient cohorts. Age, sex, DCCI and other comorbid conditions, geographic region, and season were the characteristics that contributed to the weights used for the IPTW adjustment. Unadjusted (scores prior to matching) and adjusted (scores following matching) results are presented for demographic and clinical baseline characteristics. All health care resource utilization and cost outcomes presented are adjusted.To minimize the risk of selection bias for the 2 cohorts before computing any outcome measures, inverse propensity treatment score weighting (IPTW) was employed.Generalized linear models using a log-link function and gamma distribution were generated to assess health care costs, and linear regression models were generated to assess measures of health care utilization as our dependent variables and AR treatment (MP-AzeFlu vs. the free-combination therapy) as the independent variable. The demographic and clinical characteristics identified for patients in each cohort were included in the models as covariates. Least squares means were computed for both utilization and costs. Along with the IPTW adjustment, the least squares means for utilization and costs were also adjusted for prescriber type , switching status (whether the patient switched treatments during the post-index period), and pre-index total costs . Significance for all analyses was set at a 0.05 alpha level a priori.P = 0.255; outpatient services, P = 0.624). Patients in both cohorts had approximately 5 specialist visits during the 6 months following their AR diagnosis and index therapy prescription. However, patients in the free-combination cohort had significantly more pharmacy fills  following their index prescription than those using MP-AzeFlu (11.26).Health care utilization was examined using all medical and pharmacy claims  as well as for AR-related claims during the 6 months following patients’ index prescription. P = 0.139; specialist visits, P = 0.117). Neither cohort had claims for ED visits during the 6 months after filling the index therapy. Just as with the all-cause analysis, patients in the free-combination cohort had significantly more AR-related pharmacy fills post-index (1.17) than the MP-AzeFlu cohort .With respect to AR-related utilization, patients in the MP-AzeFlu cohort had slightly more outpatient services (2.13) and specialist visits (1.73) related to AR than the free-combination cohort , but this was not statistically significant . Total medical costs and total costs were significantly lower  for the MP-AzeFlu patients  than for the free-combination patients .Health care costs were likewise classified as all-cause or AR-related. These costs included all pertinent medical, pharmacy, and total  costs. P < 0.001) for the MP-AzeFlu cohort ($128) compared with the free-combination cohort ($268). If the cost of the nasal spray fills on the index dates is included, the mean AR-related pharmacy costs increase about $150—from $128 to $279 for the MP-AzeFlu cohort and from $268 to $418 for the free-combination cohort, and this cost differential between the 2 cohorts remains statistically significant (P = 0.048). AR-related medical costs were similar for the MP-AzeFlu patients ($206) and the patients in the free-combination cohort ($190) 6 months following their index prescription fill, and this was not statistically significant (P = 0.454). Total costs were also lower for the MP-AzeFlu cohort patients ($334) than for the free-combination cohort . This difference in total costs remains quantitatively similar and statistically significant even if the cost of the nasal spray fills on the index date is included.AR-related pharmacy costs were significantly lower . ED visits were similar across both cohorts. Free-combination patients also had significantly more prescription fills (3.35) than the MP-AzeFlu cohort patients (1.50) following the index day (P < 0.001).Patients in both cohorts with a concurrent diagnosis of asthma were examined separately for utilization and costs. Of the originally identified cohorts, 109 patients on MP-AzeFlu and 113 patients on the free-combination therapy also had a diagnosis of asthma. For these patients, asthma-related claims  were assessed and are reported in P = 0.008) for the free-combination cohort patients ($928) than for the MP-AzeFlu patients ($192). Total medical costs were low but similar for the MP-AzeFlu cohort ($351) and free-combination cohort . Total costs were significantly higher for the free-combination cohort  than for the MP-AzeFlu cohort .Total pharmacy costs were significantly higher  In order for any significant difference to occur between these 2 cohorts, there needed to be a large difference in the count of utilization services. While the free-combination cohort had higher means for ED visits and outpatient services, this did not equate to significantly higher utilization. However, the higher count of services would be associated with higher costs, since 1 ED visit can generate higher costs.Current research shows that there is an incremental increase in the utilization and costs for individuals diagnosed with AR.We additionally found that AR-related pharmacy costs were lower in patients on MP-AzeFlu than in patients on the free combination of INA and INS. Previous arguments made by third-party payers against the use of MP-AzeFlu were based on the immediate and direct cost associated with MP-AzeFlu relative to the free combination of INA and INS. However, this approach does not take long-term costs into consideration. We found that patients on the free combination of INA and INS had more prescription fills for their nasal sprays over our study period. Additionally, over time, AR-related pharmacy costs are only partially composed of either the cost of MP-AzeFlu or the free combination of an INS and an INA. AR-related pharmacy costs consisting of other medications such as decongestants or nasal treatments such as saline irrigations must also be considered.In our study, AR patients on the free combination of INA and INS may have used more expensive ancillary AR-related therapeutics to supplement their INA and INS, which led to higher overall AR-related pharmacy costs in comparison with patients using MP-AzeFlu. The decision was made to exclude the index fill cost from all analyses, as this helped to ensure a clearer examination of all post-index outcomes. While there are arguments to include the index fill, the only impact of including this cost was found for pharmacy utilization, where the fill count increased by 1 for both groups. The health care costs increased for both groups, but the cost differential between the groups was identical and the statistical difference was the same.26 One explanation for the finding that the MP-AzeFlu cohort had lower asthma-related pharmacy costs could be better control of AR by MP-AzeFlu, leading to better asthma control, although this remains speculation at this point and other explanations may exist.Interestingly, we also found lower asthma-related health care utilization and pharmacy costs in AR patients with asthma on MP-AzeFlu compared with those on the INA and INS free combination. The “unified airway hypothesis” suggests that upper and lower airway inflammation may be linked and that the exacerbation of one may in turn lead to exacerbation of the other.29 When examining utilization and costs, only 6 months of data were available for use, which may not be comparable to other studies on AR treatments that used 12 months of post-index data.This study has several limitations that should be considered when interpreting the current findings. These findings are only applicable to the members identified in the study population. Additionally, the analyses were only completed for those patients who had newly initiated to MP-AzeFlu or a free combination of products. The study did not examine patients in the Medicaid population, who have previously been shown to have different utilization patterns and severity of disease than patients with private insurance  or Medicare coverage.While there is a case to be made for using prevalent AR patients, by focusing on new initiators, confounders to subsequent utilization associated with the effects of previous nasal spray use were removed. The analyses performed here were also completed for all members, not just new initiators, and results were similar to those presented here.The severity of AR-related symptoms or asthma, which is not available in claims databases, could also not be accounted for in this study. The relative similarity in unadjusted frequency of atopic comorbidities such as acute and chronic sinusitis and nasal polyps between the MP-AzeFlu and free-combination cohorts suggests that on the whole these 2 cohorts were similar. However, the definitive lack of AR and asthma severity nonetheless potentially confounds our results. Also, as this is a retrospective claims-based study, based on the patterns of a select sample of patients, no causal inferences can be ascertained from the results. Because these results are limited to a specific cohort of commercially insured patients at a large health plan, they cannot be generalized to the larger, external population.Adherence was not an outcome of interest for this study, partly because patients’ utilization may vary as a function of the severity of the symptoms they experience, and the severity of AR is not available on claims data.Finally, pharmacy costs included for analyses were limited to the medications that require a prescription and could be found in the pharmacy claims, leaving over-the-counter medication treatments out.In a comprehensive claims database of commercially insured patients with AR requiring dual therapy with an INA and an INS, we found the cohort taking MP-AzeFlu had lower overall, AR-related, and asthma-related health care utilization and costs compared with a matched cohort taking the free combination of an INA and an INS. MP-AzeFlu may offer advantages with respect to medical costs, in particular, AR- and asthma-related pharmacy costs, over the free combination of INA and INS. At the very least, MP-AzeFlu does not appear to be associated with higher costs. This information should be considered when treating AR patients who require dual INS and INA therapy, in particular, those with asthma.


Objectives: To describe the frequency of and reasons for changes in healthcare utilization in those requiring ongoing treatment, and to assess characteristics associated with change, during the second wave of the pandemic.Methods: Corona Immunitas e-cohort study (age ≥20 years) participants completed monthly questionnaires. We compared participants reporting a change in healthcare utilization with those who did not using descriptive and bivariate statistics. We explored characteristics associated with the number of changes using negative binomial regression.Results: The study included 3,190 participants from nine research sites. One-fifth reported requiring regular treatment. Among these, 14% reported a change in healthcare utilization, defined as events in which participants reported that they changed their ongoing treatment, irrespective of the reason. Reasons for change were medication changes and side-effects, specifically for hypertension, or pulmonary embolism treatment. Females were more likely to report changes . Those with hypertension were least likely to report changes .Conclusion: Few of those requiring regular treatment reported changes in healthcare utilization. Continuity of care for females and chronic diseases besides hypertension must be emphasized. Due to the COVID-19 pandemic, healthcare providers and policymakers had to reorganize their care management to allocate sufficient resources for the treatment of persons affected by COVID-19, while simultaneously trying to limit contamination risks and continuing to care for non-COVID-19 patients. These efforts have resulted in a decrease in healthcare service provision and healthcare utilization for non-COVID-19-related routine medical care at the beginning of the pandemic .Few studies have examined the specific reasons behind missed appointments and lack of care , 3. It hIn Switzerland, although a complete lockdown did not occur, the Federal Office of Public Health (FOPH) issued recommendations to stay at home . ReducedPrevious studies on healthcare utilization during the pandemic in Switzerland were limited to specific types of care such as general practices (GP) and psychological counseling, focused on patients in need of consistent disease-specific monitoring, such as diabetes, CVD, hypertension, cancer treatment, chronic obstructive pulmonary disease (COPD), and post-transplant, or included specific vulnerable populations , 10–13. The main objective of this study was to assess the impact of the pandemic on the utilization of healthcare services in the general Swiss population between September 2020 and February 2021 (the second wave of the pandemic). We first aimed to describe healthcare utilization during the COVID-19 pandemic in the general population, specifically the frequency of changes and reasons therefore in those needing regular treatment, and healthcare utilization in those not needing regular treatment. Second, we aimed to assess the characteristics of those who reported changes in regular healthcare utilization, and characteristics associated with the frequency of changes in regular healthcare utilization.Corona Immunitas is a Swiss-wide research program of coordinated population-based, seroprevalence studies coordinated by the Swiss School of Public Health (SSPH+) and implemented in Swiss cantons of all language regions since spring 2020. Until 2022, 13 sites participated in the program: Basel-City (BS), Basel-Land (BL), Bern (BE), Fribourg (FR), Eastern Switzerland [St. Gallen (SG) and Grisons (GR)], Geneva (GE), Lucerne (LU), Neuchatel (NE), Ticino (TI), Valais (VS), Vaud (VD) and Zurich (ZH). The program’s main objective was to measure the spread of COVID-19 infections in the general population by measuring antibodies in individuals randomly selected by the Swiss Federal Statistical Office (SFSO) across Switzerland . All parThe study was conducted according to the Declaration of Helsinki guidelines. The ethics committees of the various cantons approved this study: BS and BL, BASEC No 2020-00927; BE, GR, SG, FR, LU, NE, VS and ZH, BASEC No. 2020-01247; GE, BASEC No. 2020-00881; TI, BASEC No. 2020-01514; VD, BASEC No. 2020-00887.Eligible individuals from age-stratified random samples of the general Swiss population (20–64 years and 65+ years) provided by the SFSO were recruited. Individuals received an invitation letter, including study information, informed consent forms and a pre-paid return envelope. In the first part, participants completed a baseline questionnaire, that included sociodemographic and health-related information, such as the presence of chronic conditions  . ParticiFor this study, we used available longitudinal data from nine research sites BS, BL, BE, FR, GR, LU, NE, SG, and ZH collected between September 2020 and March 2021, which corresponded to a period of high incidence of COVID-19 infections  (2022). The data has been visualized using the word cloud feature, where words used in a text are separated and the most frequently used words appear larger or bolder.Out of 6,252 individuals of the eCohort in the nine centers, 3,190 adults (response rate: 51%) completed at least one monthly questionnaire assessing healthcare utilization from September 2020 to February 2021.n = 658, 21%, n = 90) reported at least one change in regular healthcare utilization during the observation period.About one-fifth of the participants , and having only hypertension (p = 0.044).The change in the incidence of healthcare utilization was one in 1,000 person-days. Those, who reported a change in healthcare utilization were significantly more likely female  these were mostly attributed to treatment for pulmonary embolism.Reasons for changes in healthcare utilization in the German-speaking area  and those with hypertension were least likely to report a change  as compared to those with other chronic conditions needed care in health systems and implementing new solutions to maintain essential routine monitoring to achieve greater sustainability in post-pandemic recovery , 30.The main strength of this analysis is the longitudinal data from the Swiss general population collected monthly over 6 months through standardized questionnaires. Another strength is the digital data collection permitting participation from anywhere and at any time. The participation rate is in line with other survey-based studies. Despite the digital design, we report the successful participation of many individuals older than 65 years. Our study included representative samples in both age groups (20–64 years and 65+ years) within the Swiss general population. Unfortunately, the data on fear about potential COVID-19 infection could not be harmonized and only two out of nine research sites were included in this analysis. However, we expect that reports of fear in Switzerland would have been similar in all centers. As in other studies, selection bias cannot be ruled out, as the majority did not report a chronic condition and were highly educated. The relatively low response rate is another limitation. Individuals with more severe conditions and health needs may therefore be underrepresented in this study’s findings. We did not perform a non-participant analysis due to a lack of information on these factors among non-participants.Our study contributes to understanding changes in healthcare utilization in the general population in Switzerland during the COVID-19 pandemic. Changes in healthcare utilization were reported by few persons who needed regular treatment, corresponding to an incidence of one in 1’000 person-days. The importance of continuity of care for chronic diseases other than hypertension must be emphasized. Careful and encouraging instructions for self-management may be helpful. Changes were more pronounced in women than in men. This calls for tailored disease surveillance, considering gender disparities.Previously reported disruptions in chronic disease surveillance make it imperative for future studies to assess the long-term impact of healthcare utilization during the pandemic on health outcomes.


Pre-exposure prophylaxis (PrEP) is an effective method for preventing Human Immunodeficiency Virus (HIV) transmission through sex and injection drug use. However, studies have shown that HIV prevention services are limited in underserved populations. Nationally in non-research settings, data estimates 15-62% PrEP retention in care. At our clinic, approximately 15% of patients were retained in PrEP care between 2018 and 2022, with limited data on reasons why. The objective of this study is to provide data on PrEP patients lost to follow-up.This was a single-center, retrospective, descriptive study of PrEP patients seen at the clinic who received at least one PrEP prescription and were lost to follow-up between September 2018 and June 2022. Between July and December 2022, patients lost to follow-up were contacted via telephone to re-engage in care and administered a patient questionnaire. Patients were included if they were 18 years or older, at high risk for HIV acquisition per the Centers for Disease Control, and were lost to follow up for PrEP services, defined as at least three months' time from last clinic visit. Patients were excluded if they had a previous HIV diagnosis or a clinic encounter for HIV post-exposure or HIV/sexually transmitted infection screening only. Endpoints included baseline demographics for all patients and questionnaire results from those who consented to participate. Data collection via EPIC™ chart review and statistical analysis via Microsoft Excel. Descriptive statistics were used to present results.Sixty-one patients were included. Patients had an average age of 37 years, 70% were male, and 77% were Black/African American. Most patients engaged in high-risk heterosexual behavior with multiple sexual partners of unknown HIV status and inconsistent condom use. Thirty-five patients participated in the questionnaire. Twenty-four patients endorsed self-discontinuing PrEP therapy due to: medication cost/access (6/24); difficulty adhering to clinic visits (8/24); changes to perceived risk of HIV transmission (9/24).Medication cost/access, adherence to clinic visits, and perceived risk were the top three reasons for PrEP discontinuation. Further research is needed to address barriers to retaining PrEP patients in care.All Authors: No reported disclosures


Conclusions: The tele-HHUS system can help rural GPs perform HHUS successfully in remote and rural communities. This novel mobile telemedicine model is valuable in resource-limited areas.Background: Rural general practitioners (GPs) have insufficient diagnostic information to deal with complex clinical scenarios due to the inequality in medical imaging resources in rural and remote communities. The objective of this study is to explore the value of a tele-mentored handheld ultrasound (tele-HHUS) system, allowing GPs to provide ultrasound (US) services in rural and remote communities. Methods: Overall, 708 patients underwent tele-HHUS examination between March and October 2021 and March and April 2022 across thirteen primary hospitals and two tertiary-care general hospitals. All US examinations were guided and supervised remotely in real time by US experts more than 300 km away using the tele-HHUS system. The following details were recorded: location of tele-HHUS scanning, primary complaints, clinical diagnosis, and US findings. The recommendations  based on clinical experience alone were compared with those based on clinical experience with tele-HHUS information. Results: Tele-HHUS examinations were performed both in hospital settings  and out of hospital settings . Leaving aside routine physical examinations, flank pain  was the most common complaint in inpatients, while chest distress  and flank discomfort  were the most common complaints in out-of-hospital settings. Additionally, the referral rate increased from 5.9% to 8.3% (kappa = 0.202;  According to the World Health Organization (WHO), simple ultrasound (US) or X-rays can cover the needs of approximately two-thirds of diagnostic imaging services . HoweverWith the development of newer technologies, US equipment has become miniaturized ,4. NovelHowever, the application of HHUS poses serious challenges. Although rural doctors may receive basic US training with HHUS devices, they usually cannot perform HHUS examinations skillfully at the outset. Additionally, insufficient operating backup support from US experts may discourage them from performing HHUS.With the development of telemedicine technology, studies have demonstrated that novices without US experience can successfully perform US examinations with the guidance of remote experts ,9,10,11.In recent years, with the advancement of US technology, the makers of US devices have developed portable handheld equipment connected to smart mobile devices that allow real-time US images to be shared remotely through commercial chat software or digital software platforms ,12. HoweIn this instance, the healthcare providers were exclusively focused on a single clinical scenario. However, rural general practitioners (GPs) usually encounter complex clinical scenarios. Whether tele-US could help rural GPs perform HHUS successfully remained unconfirmed. Therefore, we conducted this multicenter study in real-world conditions to explore the value of a tele-HHUS system in rural and remote communities.This prospective, descriptive study was conducted across thirteen primary hospitals in Taishun County and two tertiary-care general hospitals. These community hospitals were 446 and 306 km away from the tertiary-care hospitals, respectively . This stThe study comprised 28 rural doctors from 13 primary hospitals, who were all GPs without any US experience. They had 5–15 years of clinical work experience, usually diagnosed and treated frequent and common diseases, recorded data of chronic diseases, and followed up regularly. The US diagnoses were supported by 9 independent remote experts with at least 5 years of experience in US using the tele-HHUS system . All the participants were listed in Between March and October 2021 and March and April 2022, 726 patients underwent tele-HHUS examination. The inclusion criteria were as follows: (a) age ≥ 18 years; (b) various indications for US examinations, such as establishing a disease diagnosis, follow-ups, and routine physical examinations. The exclusion criterion was incomplete information.The rural GPs received face-to-face lessons from experienced US specialists in Taishun County. The training content was carefully designed according to the clinical scenarios faced by local GPs. The training course included 3 days of theoretical learning and hands-on practice with HHUS and 2 weeks of US internship at the local second-level hospital. The course syllabus included the principles of ultrasonography and instrument operation, abdominal and cardiovascular US anatomy, US assessment of abdominal trauma and acute abdomen, and US assessment of hypotension or hypoxemia. The rural GPs who finally passed the theory and practical exams received a certificate of qualification, which was approved by the local health commission.The equipment used in this study was a HHUS system . This system consisted of a wireless low-frequency (2–5 MHz) HHUS transducer ; an “on-site screen” (a proprietary tablet) and a “camera and microphone” (an Android device) for the use of the rural GPs; and a “remote screen”  for the experts, along with the network that connected them .The US transducer was connected with the “on-site screen” through the transducer’s built-in Wi-Fi. A custom mobile application or web-based system was integrated with the workstation, which had a doctor-side and a patient-side and could carry out all the telemedicine work. Real-time US images were displayed on the screen and uploaded to the cloud. The “camera and microphone” utilized an additional on-site Android device, such as a smartphone, which allowed for simultaneous visualization of the operator’s hand position and voice interaction with remote experts.The real-time US images, hand position of the operator, and voice interactions were uploaded to the cloud and observed by the remote experts using the “remote screen”. Data transmission was based on a 4G or 5G network (with a downlink/uplink rate of 300 Mbps).A GP performed HHUS scans of each patient with online guidance from a remote expert. Remote experts could guide the GPs in utilizing the correct position, acquiring standard images, improving image quality, and deriving potential diagnoses. Additionally, they could communicate with the patients, together with the GP. When either of them detected a positive finding, both could save the image for further analysis. Finally, an expert completed the US report and uploaded it to the cloud. Subsequently, the rural GP could download and print it.For each patient, we recorded the location of the tele-HHUS scanning, primary complaints, the clinical diagnosis, and US findings. Recommendations based on clinical experience alone, and those based on clinical experience with additional tele-HHUS information, were compared. The recommendations were categorized as follows: (1) follow-up; (2) referral for other medical interventions .Statistical analyses were performed using SPSS v26.0  and Microsoft Excel 2019 . Quantitative variables are presented as mean ± standard deviation and range. Categorical variables are presented as numbers and percentages.A total of 708 patients were included in this study . Of these, 43.7% (310/708) were males and 56.7% (398/708) were females. Eighteen patients were excluded due to incomplete information. Tele-HHUS examinations were performed in in-hospital settings  and out-of-hospital settings . The out-of-hospital settings included patients’ homes , committee centers , nursing homes , and mobile service vehicles .Leaving aside routine physical examinations, flank pain  was the most common reason for patients to seek treatment in in-hospital settings, while chest distress  and flank discomfort  were the most common complaints in patients in out-of-hospital settings .Among 34 patients who received tele-HHUS at home (whose average age was 78.4), 2 were blind with glaucoma, 1 had a hand disability, 4 suffered from lower-limb paralysis, 6 had hypertension, 6 had a history of stroke, and 6 suffered from diabetes. A total of 64.7% (22/34) of these patients had not undergone US examination for three to five years.A total of 16 anatomical sites were scanned, and more than 30 diseases were diagnosed using the tele-HHUS system . The kidWith the aid of the tele-HHUS system, 59 (8.3%) patients were recommended referrals based on US findings. By contrast, when diagnoses were based on clinical experience alone, only 42 (5.9%) patients were referred. Of these, 13 patients were recommended referrals with the aid of the tele-HHUS system. Furthermore, tele-HHUS helped modify the decision to consider follow-ups based on clinical experience alone in another 46 patients .In this prospective, descriptive study, we showed a wide application for tele-HHUS both in in-hospital settings and out of hospital in remote communities. Using the tele-HHUS system, remote US experts could help rural GPs perform US successfully in different real-world conditions. Hundreds of local villagers from 13 communities received a high-quality ultrasound diagnosis service and definite referral recommendations in resource-limited settings. We demonstrated that the tele-HHUS system, supported by a network connection to remote experts, is valuable not only for the GPs but also for the local patients and the entire healthcare environment in remote and rural communities.In particular, we proved the benefits of the tele-HHUS system for patients in out-of-hospital settings. For example, seeking high-quality healthcare is more difficult for elderly people with reduced mobility  and multiple comorbidities. Long distances, high costs, and lack of good medical resources nearby hindered their access to timely medical check-ups. Although rural GPs may visit and provide basic medical care, US examinations are extremely difficult to perform in such settings. Therefore, based on our data, 75% of the patients had not undergone US examination for more than 5 years. With the aid of the tele-HHUS system, elderly people can undergo point-of-care US performed by rural GPs in their homes instead of visiting hospitals. Visualization of their internal physical condition, especially for patients with symptoms, could be used to determine whether they require referrals to higher centers. With timely visual information, the diagnostic efficiency for elderly patients is improved, and the diagnosis time of potential diseases is significantly advanced. One study showed that a handheld ultrasound device could effectively improve clinical decision-making at the point of care of geriatric patients , which mIn the present study, we described wide potential applications for the tele-HHUS system to access multiple organs. We believe that most patients could benefit from this novel system. For example, a 37-year-old man with severe flank pain visited his nearest clinic; however, there was no US device equipped with the tele-HHUS system at that clinic. Using the tele-HHUS system, the rural GP confirmed the presence of kidney stones with hydronephrosis, and the patient was immediately treated appropriately. Similarly, a 67-year-old man with severe abdominal pain and abdominal distention was immediately diagnosed with acute cholecystitis using the tele-HHUS system and received appropriate treatment. Therefore, owing to the universal applicability of the tele-HHUS system, we believe that it could play a key role in providing basic healthcare services for the public, especially in resource-limited settings . Many stBased on our results, we believe that rural GPs can benefit from this novel telemedicine model. Using a network connection, the online guidance of experts is a good method for acquiring professional knowledge, one that is conducive to improving the US skills of rural GPs. At the same time, it mitigates the shortage of medical resources in rural primary hospitals. For example, some clinics equipped with US devices only provide US services periodically  because of the unavailability of full-time sonographers. Furthermore, some clinics do not have US devices. Therefore, the government provides standalone HHUS devices (without networking) and relatively brief training for rural GPs in the hopes that they can fill the gap in US services. However, the GPs may not be proficient at performing US. We believe that rural GPs require continuous instruction before being able to perform US with the requisite skill. Therefore, the tele-HHUS system using remote experts can not only increase confidence in clinical diagnoses, but also continuously train rural GPs to perform better US examinations in order to mitigate the shortage of sonographers in remote communities.With the advancement of technology and the development of health devices, mobile health  has become an emerging field . The sigCompared with other similar studies, our tele-HHUS model displayed some advantages. First, the system was synchronously supported using a 4G or 5G network so that remote experts could support rural GPs in a timely manner. By contrast, in two previous studies on HHUS-based remote screening for heart disease in the community, experts interpreted images asynchronously on a web-based system within a 2-day period, which significantly delayed the diagnoses ,24. SecoThe present study has some limitations. First, patients’ and rural doctors’ satisfaction with the tele-HHUS system was not evaluated. In future studies, questionnaires can be designed to fill this gap in knowledge. Second, there was no follow-up information concerning the patients, which makes it difficult to ascertain whether the changes in clinical management based on the tele-HHUS system were effective. Telephonic follow-up may be a feasible approach in future studies. Third, the tele-HHUS system is currently free of charge. However, it is necessary to discuss if it should be charged for, as well as investigating its cost performance. Fourth, the training protocol in this study was based on the experiences of individual experts. However, there is no consensus on a standard protocol for HHUS training . A unifoIn conclusion, the tele-HHUS system is valuable in remote and rural communities. With the advances in mobile technology and digitization of healthcare, HHUS combined with 4G or 5G networks connected rural GPs and remote experts effectively to improve access to ultrasound services in rural communities. This novel mobile telemedicine model is worth popularizing and utilizing in resource-limited areas.


Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial bones, primarily affecting the spine. AS is classified as spondyloarthritis because it affects the spine and other joints. AS has several presentations and, in some cases, can be insidious, making it difficult to diagnose. We encountered a patient on long-term follow-up for rheumatoid arthritis with an appearance of AS. This case suggests that patients with long-term rheumatoid arthritis can develop AS during follow-up and that the seropositivity of rheumatoid factors and anti-cyclic citrullinated peptide antibodies cannot rule out AS. Therefore, the possibility of AS should be considered, even in patients diagnosed with rheumatoid arthritis. Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial bones, primarily affecting the spine. AS is classified as spondyloarthritis because it affects the spine and other joints . SymptomAS has several presentations and, in some cases, can be insidious, making it difficult to diagnose. The Assessment of Spondyloarthritis International Society criteria can be used to evaluate patient symptoms, physical examination results, and imaging results to make a diagnosis . PhysicaThe coexistence of other collagenous diseases can lead to a delayed diagnosis of AS. Here, we report a case of rheumatoid arthritis followed by AS. Previous studies have shown that AS is followed by rheumatoid arthritis . HoweverA 52-year-old female visited the outpatient department of a rural community hospital with the chief complaint of new-onset back and hand pain for six weeks. She noticed back and hand pain six weeks ago when waking up in the morning. Motion exacerbated the pain after resting; however, the pain improved with continuous movement. She did not experience night sweats, fever, weight loss, or eye or finger pain. She had a medical history of osteoarthritis in her hand and rheumatoid arthritis for 10 years with methotrexate (MTX) (14 mg per week) and tacrolimus (1.5 mg per day) treatments.Vital signs at the visit were as follows: blood pressure of 110/75 mmHg; pulse rate of 65 beats/minute; body temperature of 36.7°C; respiratory rate of 16 breaths/minute; and oxygen saturation of 96% on room air. The patient was oriented toward time, place, and person. Physical examination revealed tenderness in both sacroiliac joints. Extremities examinations did not reveal any swelling, erythema, or tenderness of either hand's proximal interphalangeal (PIP) joints. No other neurological abnormalities were observed. There were no apparent abnormalities in the chest or abdomen and no skin eruptions. Laboratory tests showed mild inflammation exacerbation and no liver or kidney abnormalities  showed a high-intensity lesion on the joints, indicating the presence of sacroiliac joint inflammation Figure .A follow-up physical examination revealed limited flexion and extension of the spinal joints. Finally, we diagnosed her with AS and started subcutaneous etanercept at 50 mg/week, which improved her pain and quality of life.This case suggests that patients with long-term rheumatoid arthritis can develop AS during follow-up and that the seropositivity of rheumatoid factors and anti-cyclic citrullinated peptide (anti-CCP) antibodies cannot rule out AS. Therefore, the diagnosis of rheumatological diseases should include various differential diagnoses, even in patients already diagnosed with rheumatological diseases .The coexistence of rheumatoid arthritis and AS is not common, but it is possible based on the pathophysiology, including cytokine interactions in each disease. Rheumatoid arthritis can be triggered mainly by aberrant lymphocytes with increased levels of cytokines, such as tumor necrosis factor (TNF) and interleukins (IL) 1 and 6 . These cA diagnosis of AS may require constant follow-up, even in patients with rheumatoid arthritis. In this case, the patient had new-onset back and joint pain with morning stiffness during long-term treatment of rheumatoid arthritis with MTX. Methotrexate can treat peripheral arthritis in AS but is ineffective in enthesitis . Even inEspecially in rural contexts with a lack of resources, rheumatologists need to consider the appearance of AS in patients with rheumatoid arthritis who are being treated with conventional synthetic DMARDs and new-onset axial pain and stiffness during long-term follow-up. In addition, in rural contexts, the rate of rheumatic diseases in older patients is increasing, and these patients have vague symptoms . In suchPatients with long-term rheumatoid arthritis can develop AS during a long follow-up period, and seropositivity for rheumatoid factors and anti-CCP antibodies cannot rule out AS. Therefore, the diagnosis of rheumatic diseases should include various differential diagnoses, even in patients already diagnosed with rheumatic diseases. In rural contexts, older patients with rheumatological diseases show vague symptoms; therefore, rural rheumatologists should comprehensively investigate these symptoms during follow-up.


Global epidemiological data indicates that despite implementation of multiple interventions and significant financial investment, the HIV/AIDS epidemic remained inadequately controlled as of 2020. E-health presents a novel approach in delivering health information and health care and has gained popularity in HIV prevention worldwide. However, evidence on the effectiveness of e-health interventions on HIV prevention among diverse populations remains inadequate. Our study aims to systematically evaluate the effectiveness of varying e-health interventions on HIV prevention, with the objective of providing data support and guidance for the development of future e-health HIV intervention strategies.A systematic search of electronic English databases, including MEDLINE through PubMed, Embase, Scopus, and Web of Science, along with three Chinese databases, including National Knowledge Infrastructure (CNKI), Chinese Wanfang Digital Periodicals (WANFANG), and Chinese Science and Technology Periodicals (VIP) database, will be conducted for the period of 1 January 1980 to 31 December 2022. Additionally, gray literature and unpublished trials in trial registers will be searched. Studies aimed at HIV prevention through e-health interventions, with full-text publications available in either English or Chinese, will be included. Study types will be limited to RCT, cluster RCT, and quasi-experiment study. The risk of bias in individual studies will be assessed following the guideline highlighted by the Cochrane Handbook for Systematic Reviews of Interventions. The outcomes will cover cognitive, behavioral, psychological, management, and biological measures of individuals involved in e-health interventions. The quality of evidence will be assessed by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Ultimately, a systematic review with meta-analysis will be conducted to compare the effectiveness of e-health interventions among diverse populations.This systematic review seeks to establish novel insights into the effectiveness of e-health interventions in diverse populations worldwide. It will inform the design and use of e-health interventions to optimize HIV-related strategies.PROSPERO CRD42022295909.The online version contains supplementary material available at 10.1186/s13643-023-02274-6. The HIV epidemic has presented significant global challenges, prompting a major focus on HIV prevention as a public health priority. In this context, the United Nations has established ambitious goals, including the end of the AIDS epidemic by 2030 , as wellDespite the implementation of numerous intervention strategies such as immediate start of antiretroviral therapy (ART), promotion of condom uses, opioid drug substitution, needle and syringe exchange program, and the provision of voluntary consultation and testing (VCT) services, targeted interventions for key populations, school-based awareness campaigns, promotion of HIV self-testing , 5, the The proliferation of the Internet and the increasing emergence of dating apps or websites have significantly enhanced the ease and privacy of meeting and approaching causal sexual partners, potential romantic partners, one-night stand partners, and Internet partners –12. InteE-health is defined as a collection of electronic technologies that utilize the Internet to provide healthcare services, thereby improving the quality of life and facilitating healthcare delivery , 21. TheCurrently, there is increasing academic interest in the effectiveness of e-health interventions for HIV prevention worldwide. For example, Marhefka, Turner, and Lockhart  utilizedNevertheless, previous systematic reviews on this topic have provided incomplete evidence. On one hand, most reviews have only focused on promoting treatment among persons living with HIV, disregarding the effectiveness of e-health in preventing HIV infection –31. On tA systematic review with meta-analysis will be implemented in this study. This protocol of meta-analysis will be performed on the basis of the Preferred Reporting Items for Systematic review and Meta-Analysis Protocol (PRISMA-P) statement . However, considering the inherent characteristics of public health interventions, individualized randomization may be impractical in some circumstances. Therefore, cluster RCTs and quasi-experimental studies with self-control will also be considered for inclusion. Cross-sectional studies utilizing only online surveys will not be incorporated in this analysis. Studies that meet the following inclusion criteria will be included: (1) focused on HIV prevention, (2) the study conducting an e-health intervention originally, (3) written in English and retrieved from electronic English databases or in Chinese and retrieved from electronic Chinese databases with full-text access, and (4) published within the timeframe of January 1, 1980, to December 31, 2022, as the first documented case of HIV was reported in Los Angeles in 1981 .In this study, participants who received an e-health intervention will be included. The subgroup analysis will primarily concentrate on evaluating the effectiveness of e-health interventions among university students, women, adolescents, and HIV key populations .Original researches conducting e-health interventions on HIV prevention will be included. E-health interventions are defined as those implemented through the Internet, such as m-health and telehealth, which utilize electronic technologies to provide healthcare resources, services, and information. Studies that solely conducted online surveys without any intervention will be excluded from the analysis.We will consider the systematic reviews with the following outcomes; the details of these outcomes are shown in Table We plan to conduct a comprehensive and systematic search of various electronic English databases, including MEDLINE, PubMed, Embase, Scopus, and Web of Science, as well as three Chinese databases, which are National Knowledge Infrastructure (CNKI), Chinese Wanfang Digital Periodicals (WANFANG), and Chinese Science and Technology Periodicals (VIP) database. The detail of these search strategies is shown in Additional file Two reviewers (L. W. and X. Y.) will independently assess the titles and abstracts that meet the initial retrieval criteria. Studies that do not satisfy the eligibility criteria will be excluded. Subsequently, the remaining publications will undergo a full-text screening by both investigators (L. W. and X. Y.) according to the same inclusion criteria. In the event of any disagreements, discussion will be employed to seek consensus. Any discrepancy that cannot be resolved will be referred to a third reviewer to determine the final decision. Furthermore, excluded publications and their respective reasons for elimination will be confirmed by a third reviewer. A PRISMA 2009 flow chart Fig.  will be The studies selected through the study selection process will undergo data extraction, wherein information from the studies will be extracted after a thorough reading of the full text, and presented in Table Data extraction will be conducted by two independent reviewers (L. W. and X. Y.) utilizing the designed data extraction form. Following this process, the records extracted by the reviewers will be cross-checked, and any disputed points will be resolved through a third reviewer who will make the final decision. Upon completion of the extraction process, the data records will be sorted by region and year. This will result in a dataset of included studies for data analysis. A pilot test will be conducted on selected studies published in 2022 with adjustments made as necessary thereafter.The Cochrane Collaboration risk-of-bias (ROB) tool or Cochrane risk-of-bias tool for cluster-randomized control trials (RoB2.0) will be used by two independent authors (L. W. and X. Y.) to assess the risk of bias in the included studies –42. In tI2) based on the chi-squared test will be utilized to assess overall heterogeneity, classified into three levels: I2 ≤ 50%, low heterogeneity; I2 > 50% to I2 < 75%, moderate heterogeneity; and I2 ≥ 75%, high heterogeneity. A random-effects model is preferable, particularly when I2 > 50% [p < 0.05. Furthermore, for studies investigating various types of e-health interventions for the same outcome, network meta-analysis using the Bayesian approach will be utilized to compare the effectiveness among different types of e-health interventions if possible. For outcomes that cannot be quantitatively synthesized due to too few studies available or high heterogeneity in intervention methods or study populations, a narrative synthesis will be conducted.The extracted data will be reported using descriptive statistics. Meta-analysis will be employed to generate pooled estimates. Relevant indicators will utilize the standardized mean difference (SMD)  for contI2 > 50% , 46. OutIf feasible, subgroup analysis will be conducted to evaluate intervention effects in various subpopulations, including (1) target populations, such as key populations  and other focused population subgroups ; (2) sex ; (3) age groups; (4) types of e-health interventions; (5) types of countries, encompassing western or eastern countries and developing or developed countries; (6) prevalence states of countries, including those with the highest, moderate, and lowest rates of HIV prevalence; and (7) interactive and noninteractive e-health interventions.Sensitivity analysis will be conducted to assess the stability and reliability of the study by excluding studies with special characteristics, such as those of lower quality . Furthermore, we will perform additional sensitivity analyses by iteratively excluding each study to gauge its impact on overall estimates and to ascertain the stability of results.Publication bias will be assessed by using a funnel plot  and Egger’s test . To testIf cluster-randomized trials are incorporated, we will perform sample size adjustment utilizing estimates to evaluate covariance with cluster-level adjustment for participants with measurements at both baseline and final follow-up. Furthermore, interaction terms will be employed to examine the consistency of effects among subgroups , 49.The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) will be utilized to assess the level of certainty of evidence . A ratinBased on the protocol of this study, a pioneering systematic review with meta-analysis will be conducted to summarize and assess the impact of e-health interventions on HIV prevention across various outcome indicators and populations worldwide. Although several factors, including imperfect e-health implementation and demographic factors such as gender, residence, income, education, and culture, can influence the adoption of e-health, it still holds significant potential in promoting people’s health status . Our finAdditional file 1. PRISMA-P 2015 Checklist.Additional file 2. Search strategy.


The diagnosis of parathyroid diseases by imaging still has some intrinsic technical limitations due to the differential diagnosis of different structures of the neck that mimic the parathyroid glands. In this view, ultrasound (US) is an established, low-cost, and non-invasive imaging technique that still represents the first-line approach for evaluating patients with parathyroid disease. The objective of this article is to provide a comprehensive review of the applications of USs in clinical practice, discussing the histopathological and US characteristics of the parathyroid glands in normal and pathological conditions, the advantages of preoperative imaging, and novel updates on the most useful and currently available multiparameter US techniques. Ultrasound (US) is considered the gold-standard diagnostic tool for neck organic disorders. In fact, the high-resolution and dynamic nature of this low-cost and non-invasive technique perfectly matches the superficial but complex anatomy of the neck, allowing the operator to obtain precise anatomic details and to identify pathological lesions in most cases .Against this background, parathyroid disease still remains a diagnostic dilemma on US evaluation. In fact, whereas the clinical and biochemical diagnoses of altered parathyroid glands’ (PGs) function is in most cases straightforward, the radiological identification of the pathological glands, just like that of normal ones, can be arduous.In case of hyperfunctioning PG(s), a clinical syndrome known as hyperparathyroidism (HPT) occurs, whose surgical-treatment planning is the main indication for performing imaging techniques to locate pathological parathyroid tissue.99mTechnetium (99mTc)-Sestamibi scintigraphy or single-photon emission computed tomography/computed tomography (SPECT/CT)] is acknowledged as the most widely used first-line strategy ,2019,20]The identification of an intrathyroidal parathyroid adenoma can be challenging. Characteristically it appears as a solid, profoundly hypoechoic nodule compared with other thyroid nodules or thyroid parenchyma, partially or fully enveloped with thyroid tissue, and a polar feeding vessel identified on Color Doppler US. The operator should be more suspicious of an intrathyroidal parathyroid adenoma especially in front of a patient with high clinical and biochemical probability of HPT when a nodule with these characteristics is noted and an extrathyroidal parathyroid adenoma is not visualized .99mTc-Sestamibi scintigraphy and magnetic-resonance imaging (MRI), must be considered as this has a clear advantage in improving preoperative imaging accuracy, allowing for a minimally invasive surgical approach for patient’s treatment ,7214,72]Branchial cleft cysts, benign lesions caused by the anomalous development of the branchial apparatus during embryogenesis, must be considered in the differential diagnosis. Anomalies of the second branchial cleft account for approximately 90% of all cases and sonographically typically present as round-oval, hypo- to anechoic masses with well-defined margins, thin walls, and posterior wall enhancement, which compress the surrounding soft tissues . HoweverHemangiomas are common benign vascular tumors characterized by the abnormal proliferation of endothelial cells and abnormal blood vessel structures which, unless thrombosed, have characteristic imaging features . On the 99mTc-Sestamibi SPECT/CT is the first-line imaging strategy [18F-fluorocholine PET/CT with its high spatial resolution, low radiation burden and short scanning time has been proposed as an alternative first-line “one-stop-shop” imaging method [18F-fluorocholine PET and MRI [18F-fluorocholine PET/MRI can be particularly useful in patients with low 18F-fluorocholine uptake [18F-fluorocholine PET/MRI-integrated systems are not widely available. Patients with a distorted cervical and chest anatomy or with small ectopical hyperfunctioning PGs may benefit from a four-dimensional (4D)-CT, which has a comparable sensitivity to 99mTc-Sestamibi SPECT/CT, with the drawback of a relatively low specificity and a significant radiation burden [18F-fluorocholine PET and 4D-CT can be useful in complicated cases, being superior to 18F-fluorocholine PET and 4D-CT alone in the localization of the hyperfunctioning PG [The identification of hyperfunctioning PGs is mandatory for an adequate treatment of HPT, and in most of cases a stepwise approach with US and subsequent strategy ,78. In cg method ,78. A fu and MRI ,78. Moree uptake , as welle uptake ,78. Howen burden ,78. A cooning PG . MRI alooning PG ,78.For most of other neck organic disorders, the US cervical examination is the first-choice imaging tool for HPT-affected patients. Regardless of HPT etiology, surgical treatment is the recommended management strategy in patients meeting the international guidelines criteria and with no contraindications . BilaterThere is no doubt that the preoperative US assessment of HPT patients has many advantages, such as low costs, high availability, no patient exposure to ionizing radiation, and good visualization of the neck morphology. In most situations its sensitivity and specificity match those of other imaging techniques, but it should be noted that there are some limitations too, as in cases of uncommon PGs features and locations and the presence of anterior cervical mimics that may represent pitfalls in the US differential diagnosis of parathyroid diseases.In these cases, a multiparametric approach, including beside the conventional US and other imaging techniques such as CEUS and USE, can be helpful, and should be adopted to guide a proper work-up.


Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information.2CRIS) was developed for single-sample classification.A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification.Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the MLThese results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.The online version contains supplementary material available at 10.1186/s13073-023-01176-5. Despite the advancements in translational oncology, colorectal carcinoma (CRC) is still the second leading cause of cancer death over the world . To imprDespite their great potential for clinical applications, transcriptional classifiers tend to summarize biological traits into separate, non-overlapping subtypes. However, as already shown , subtypeTo tackle these issues, we employed a large collection of human tumors and experimental models to explore the heterogeneity of CRC phenotypes, by fuzzy assignments and single-sample class prediction in bulk and single-cell profiling, unraveling the basis of multiple continuous phenotypes of CRC. These are the foundations for future clinical translation into transcriptome-based nomograms.We used raw counts of RNA-seq expression data from the Colon adenocarcinoma (COAD) and Rectum adenocarcinoma (READ) projects of The Cancer Genome Atlas . We downSELECT (gdc__project__project_id =  = "TCGA-COAD" OR.gdc__project__project_id =  = "TCGA-READ")The dataset includes 698 primary tumor samples aligned to the GRCh38 human assembly, each with 58,387 profiled genes. Furthermore, we collected molecular and survival annotations for the TCGA dataset –23.All the samples were obtained from patients treated by liver metastasectomy or primary tumor excision. Samples were procured and the study was conducted under the approval of the review boards of the institutions . Clinical and pathologic data were entered and maintained in our prospective database. All patients provided informed consent.3 pieces in antibiotic-containing medium; 2 other pieces were coated in Matrigel (BD Biosciences) and implanted subcutaneously in 4- to 6-week-old female NOD (nonobese diabetic)/SCID (severe combined immunodeficient) mice, as previously described [Tumor material not required for histopathologic analysis was collected and placed in medium 199 supplemented with 200 U/mL penicillin, 200 μg/mL streptomycin, and 100 μg/mL levofloxacin. Each sample was cut into 25- to 30-mmescribed , 25. At escribed . All aniWe generated RNA-seq profiles from 646 liver metastatic and primary CRC PDXs   account for at least the 20% of the sample total raw counts: indeed, these samples cannot be considered well-sequenced at the used sequencing depth. Moreover, for each patient, we kept a unique sample only, if needed choosing the one with the highest number of raw counts. Preprocessing of PDX batches included removing technical replicas, keeping only the samples with the highest number of raw counts. At the end of the preprocessing pipeline, we obtained 620 primary tumoral samples in the TCGA dataset and 606 samples in the PDX dataset, with a common set of 15,084 genes.For both TCGA and PDX datasets, we computed the CPM (counts per million) expression values  from thePublic single-cell RNA-seq data were downloaded from the NCBI Gene Expression Omnibus (GEO) database under the accession code GSE132465 . These dWe generated scRNA-seq and bulk profiles for an in-house collection of 5 CRC patient-derived organoids . The PDO single-cell profiles were generated by 10xGenomics, obtaining 15,766 single-cell profiles across all 5 organoid samples. To remove the potential doublets, we used the DoubletFinder tool . Then, wThe pseudo-bulks of GSE132465  and PDO Z-score value for each gene in each sample: these Z-score normalized profiles are then compared with the five CRIS class templates  to assign the sample to the class whose centroid is at the minimum significant distance from the sample normalized profile [https://github.com/cisella/multiCRIS, We employed, as a reference, the original CRIS classifier based on the Nearest Template Prediction algorithm (NTP) , which r profile . The thr profile , 11, 35.For single-cell data, all genes with at least one read per gene in a sample were included, for a total of 95% of the CRIS gene signatures. For the implementation of single-sample classifiers on the CRIS classification task, we used only confident NTP single-label assignments as state-of-the-art references for the training and testing phases of each alternative method; specifically, this meant using 562 confidently classified TCGA samples and 550 confidently classified PDX samples.We implemented and compared the performances of several state-of-the-art single-label classifiers to assign the most prominent CRIS class (primary class) to each sample individually: Random Forest (RF) , ExtremeUsing NTP single-label assignments as target references and CRIS gene signature as feature space, we trained all the assessed classifiers with a tenfold stratified cross-validation on 70% of the TCGA confident samples (BH.FDR < 0.2); the remaining 30% was instead kept aside for a first testing. We included 393 TCGA samples in the training set and 169 TCGA samples in the testing set, while all 550 PDX samples were used entirely as an independent testing set to further evaluate the performances of all the classifiers on completely independent data of a different type (liver metastasis PDXs instead of primary human tumors). During the training phase, we also performed hyperparameter tuning to optimize the performances of the classifiers by finding the best hyperparameter values, then used to train each model one last time on the entire training set.All the details about the mentioned models, data splitting, hyperparameter tuning, and training Supplementary and testing phases are described in To move towards multi-label classification, we made use of algorithm adaptation strategies ; this kiTo assess the performances of our multi-label single-sample classifiers, we used multi-label calls coming from our multiCRIS approach. In multiCRIS, a sample can be assigned to one or more “secondary” classes for which the membership is lower than for the primary class but still significant (BH.FDR < 0.2). To define reliable secondary classes as reference targets, the NTP multiCRIS calls of each sample were further compared to class-specific thresholds. These thresholds were computed for each class as the 5th percentile of the primary class NTP scores on the entire TCGA and PDX sets separately  between 36 and 60 months in the considered datasets. Because of this, all samples with disease-free survival greater than 36 months have been considered as disease-free at the 36-month endpoint. To assess the statistical significance of the difference between two compared survival distributions, we used the log-rank test, a non-parametric hypothesis test that can be used also when some observations are censored.p-value threshold of 0.05 to discriminate statistically significant annotations from not significant ones. Also, to enrich our evaluation of each clinical correlate, we computed odds ratio and effect sizes, which respectively make comparisons with the overall scenario or with the complementary cases, as detailed in For Fisher tests, we used a To improve stratification and capture the biological traits of CRC according to the CRIS classification, we reasoned that its Nearest Template Prediction (NTP) algorithm can be employed not only to assign the single most prominent class, but also to evaluate the assignment of each sample to all CRIS classes, as well as the false discovery rate of each assignment . Thus, wMultiCRIS was first applied to the 620-sample RNA-seq dataset from The Cancer Genome Atlas (TCGA) , to confTherefore, each tumor may be assigned either to a single class (48% of the assigned samples), to whom it displays the only significantly low distance, or to multiple classes Fig. b, of whiNotably, for all CRIS subtypes, the number of secondary assignments was grossly equivalent to primary assignments  and bulk profiles in an in-house collection of 5 CRC organoids derived from PDXs. These data allowed direct comparisons of single-cell and bulk transcriptional profiles. As a third option, pseudo-bulk profiles were obtained by aggregating all single-cell profiles derived from one sample. Notably, while the profiles from individual cells captured on average 1116 transcripts with at least 5 supporting reads  , focusin2465 , foAltogether, these results show that the heterogeneity of the CRIS transcriptional profile is rooted at the single-cell level and that the phenotypes of individual cells sum up to define the CRIS classification of the bulk tumor. Therefore, the evidence of multiCRIS tumors can be mainly explained either by a mosaic composition of different cell populations with specific functional characteristics, or by a small portion of hybrid cells with mixed phenotype.Z-score calculated on batches of samples, without allowing single-sample classification [MultiCRIS paves the way for complex biological and clinical readouts; however, it is influenced by its NTP implementation, which relies on centroid distance and gene-level fication . To overfication . The confication , Supportfication , Neural fication , and Extfication . These sn = 562) and of the cohort of patient-derived xenograft . TCGA data were divided in training and testing sets, keeping the same CRIS class proportions of the entire dataset. Each classifier was trained considering only the expression values of the CRIS genes as feature space and using the NTP primary class as target reference [To evaluate each algorithm and identify the one most suitable to predict CRIS class memberships in a clinically applicable single-sample classifier, we took advantage of the collection of primary CRC samples from the TCGA project ), and threshold-based measures , as to evaluate the performances of the considered algorithms and compare them with the ones of the original NTP approach  Additio. AlthougFurthermore, in the single-label scenario, the machine learning methods were compared with the TSP approach, a first attempt of a single-sample CRIS classifier, that had originally shown quite limited concordance with NTP classification . Its subAlso, on the PDX set, LSVM reaches the most convincing results. The graphs in Additional file Thus, based on performance evaluations, we identified LSVM as the best single-label classifier in predicting the primary CRIS class of each single CRC sample. However, all the three trained algorithms are capable of computing memberships to all the 5 CRIS classes by means of algorithm adaptation techniques , which pFollowing an algorithm adaptation strategy, we developed multi-label adapted (mla) single-sample CRIS classifiers. Specifically, each mla algorithm inherits the primary class assignment from its single-label version but can associate any heterogeneous sample with one or more additional secondary classes.To evaluate mla classifiers, we used both metrics analogous to the single-label ones, but adapted for the multi-label context  and specific multi-label measures . All these metrics compare the results of mla algorithms with target assignments obtained from the MultiCRIS approach, introduced in this work.Among mla algorithms, LSVM still reached the best overall performance when considering class precisions and recalls, revealing to be the most robust approach also in the multi-label context  and multi-label adapted  LSVM on the TCGA testing and PDX sets, respectively, are reported in Additional file Both the single-label and multi-label LSVM classification results were evaluated for molecular and clinical correlates.We first evaluated the prognostic value of LSVM-based models, on the TCGA dataset considering different scenarios: only the samples assigned to the class as primary class, only the samples assigned to the class as secondary class, or all the tumors classified to the CRIS class regardless of whether it is a primary or secondary assignment. In all such cases, any comparison using the Fisher test  is alwayResponse to anti-EGFR treatment was evaluated in the PDX cohort. We confirmed associations of CRIS-C and sensitivity to Cetuximab, for which single-label LSVM (odds ratio (O.R.) = 3.281, confidence interval (CI) = 1.66–6.73) and multi-label LSVM, including also secondary assignments in the CRIS-C cohort, displayed similar performances  , while assessing each patient individually in a clinical usage setting.All these evidences confirmed the reliability of the predictions obtained with our LSVM single-sample models, especially the mla LSVM, henceforth referred to as MLThe global transcriptome profile of bulk tumors provides and aggregates portraits of several cell types composing the whole tumor ecosystem, including cancer cells, vessels, fibroblasts, and immune cells. These data provide invaluable information to discern the biology of different tumor types and improve patient stratification for clinical practice. However, such data stem from different tissues, and in current biomedical research, it is essential to dissect the contribution of each cell type to tailor the most suited therapeutic strategy.In this perspective, the CRIS classifier was designed to subdivide CRC into five subtypes specifically based on the intrinsic epithelial cancer cell transcriptome , 11. ThiIn this work, we show that multiple CRIS subtypes can coexist in the same tumor and that this is a quite common occurrence in CRC. To investigate this aspect, we implemented a multi-label CRIS NTP classifier to enable statistically significant assignment of each tumor sample to one or more classes. With multi-label classification, the majority of TCGA CRC cases received multiple assignments. The observation that key biological and molecular features were associated with CRIS classes, even when a secondary assignment was attributed to the sample, indicates that a fuzzy classification reliably reflects the complexity of the tumor biology. Multiple assignments were driven by two main causes: (i) concomitant presence of cell subpopulations with distinct CRIS phenotypes, or (ii) a homogeneous cell population carrying a hybrid CRIS phenotype. To discern the relative contribution of each of the abovementioned drivers of heterogeneity, we took advantage of matched single-cell and bulk RNA-seq profiles to compare the fuzzy assignment of the tumor bulk with single-cell multi-label CRIS assignments. The observed concordance of classification between organoid bulk and single-cell RNA-seq profiles confirmed the adequacy of multiCRIS also for single-cell classification. It should be noted that the robustness of multiCRIS derives from its use of lists of signature genes, without any quantitative parameters assigned to them. This approach has proven robust to cross-platform analyses, which typically involve dropout of signature genes ; further2CRIS. Indeed, the here designed multi-label adaptation procedure allows enriching the original single-label assignments of the model with the addition of secondary classes. ML2CRIS correctly assigns the CRIS classes based on the original single-label NTP algorithm in 91.7% of TCGA test cases, with an average precision of 92.6% in estimating the multi-label characterization of each sample. Besides these encouraging performances, primary and secondary CRIS assignments of ML2CRIS confirmed that the molecular traits characterizing each of the five subtypes  were maintained also in secondary assignments. When the single-label LSVM-based classifier was applied, CRIS-C cases were significantly associated with Cetuximab sensitivity . In the multi-label scenario of ML2CRIS, when secondary CRIS-C cases were added to primary ones, the odds ratio increased (3.359), although with reduced significance . However, when secondary CRIS-C cases were removed from both groups, and CRIS-C primary cases were compared with non-CRIS-C (not even secondary), the odds ratio increased together with its significance .To overcome the need of NTP for a large sample series, machine learning-based single-sample classification methods  have been evaluated here. These methods can reflect sample inner complexity while overcoming the dataset-dependence issue of the multiCRIS NTP approach. This is essential towards a clinical application of such transcriptional classification of patients’ samples, single or in small batches, to assist in therapy decision in clinical practice. Furthermore, this method can also be successfully applied to data from clinical trials, which typically target a specific subgroup of cases that are likely not balanced in terms of CRIS class representation. Among all the tested classifiers, the Linear Support Vector Machine resulted as the best algorithm, both in single-label and in its multi-label adapted configuration, named ML2CRIS shows promising capabilities in predicting CRIS-C patients responsive to Cetuximab pharmacological treatment, with adjustable sensitivity and specificity depending on the assignments of CRIS-C subtype secondary samples.Therefore, MLThese results confirm that tumors harboring multiple CRIS phenotypes also express clinical features that reflect their multiple assignments. However, our study was limited to broad multi-label analysis. Dedicated studies with a higher number of multi-label samples may allow, in the future, to explore relations between different classes and how they affect the course of the disease.In summary, this work provides the biological and methodological basis for multi-label fuzzy classification of CRC. The results presented here confirm that capturing intratumor heterogeneity provides a more comprehensive picture of the biological and clinical features of human tumors. This information represents a milestone for deciphering tumor biology and for the development of novel therapeutic strategies, and it could be extended to other tumor types.Additional file 1: Table S1. Gene expression profile measured as reads count per genes of PDX collectionaccording to GeneCode version 27 quantified by featureCounts. Reads mapped to Mus Musculus genome were removed from the gene quantification.Additional file 2: Table S2. Assignment of 620 TCGA samples using the Nearest Template PredictionmultiCRIS classifier. In the results, we identified samples with single label class CRIS, multi-label classes CRIS, and not classified. For classassignmentby NTP, FDR < 0.2 was considered significant.Additional file 3: Table S3. Numbers and percentage of CRIS call assignments with different BH.FDR thresholds.Additional file 4: Table S4. NTP-based multiCRIS classification of the RNA-seq bulk profiles of TCGA samples. Summary table of results with the number of samples with single-label class CRIS, multi-label class CRIS, and not primary assignment, primary assignment and any assignment.Additional file 5: Table S5. Average of distance to the centroids of CRIS classes for primary and not primary assignments. The statistical significance has been computed by Student's T-test.Additional file 6: Table S6. Overlap between CRIS classes in NTP multilabel assignments. Analysis performed on TCGA dataset.Additional file 7: Table S7. Summary of the number of samples assigned to secondary classes with peculiar molecular features as MSI status, BRAF/KRAS mutation and EMT, WNT and paneth scoring. For dichotomous variables, hypergeometric p-value has been computed to assign statistical significance at the ratio Observed over the Expected. For continuous variable, Student's T-test was performed. NC, not assigned to reference class, i.e. not assigned to that class.Additional file 8: Figure S1. Distribution of genes detected in scRNA-seq data of patient derived organoidswith a coverage of 1 to 5 reads for transcripts. Figure S2. Distribution of Pearson correlation values obtained by analysing unmatchedand matchedCRC PDO bulk / PDO pseudo-bulk pairs. Figure S3. CRIS classification of CRC single sample data from GSE132465, pseudo-bulk, and at scRNA-seq resolution. B) represents the distribution of CRIS distances evaluated on each single cell, grey points. Black points represent distances for cells with significant assignment. Red dots, represent the class distances on pseudo-bulks. C) waterfall of CRIS distances for significant assigned cells. Supplementary Fig. 4. Distribution of the CRIS classes on testing samples of TCGA and PDX datasets, using LSVM single-labeland adapted to multi-label. The TCGA dataset has 169 samples, while the PDX dataset 550 samples. Figure S5. Forest plots for LSVM-based models. Along y-axis, an identifier number for each test is reported. For each test, the odds ratioand its confidence interval are shown; the numeric label annoated on each odds ratio represents the p-value. Other details of the testsare listed below the corresponding plot.Additional file 9: Table S8a. Counts data of pseudo-bulk RNA-seq profiles of CRC organoids. Pseudo-bulk profiles have been computed by summing the gene counts of scRNA-seq profiles. Table S8b. Counts data of bulk RNA-seq profiles of CRC organoids.Additional file 10: Table S9. NTP-based multiCRIS classification in the bulk CRC organoids. NTP analysis has been performed on bulk PDO data identifying sample with Single-label CRIS classification, Multi-labels CRIS classification, and Not classification. For classassignmentby NTP, FDR < 0.2 was considered significant.Additional file 11: Table S10a. NTP-based multiCRIS classification on scRNA-seq profiles of CRC organoids. NTP analysis has been performed on scRNA-seq PDO data identifying cell with single-label class CRIS, Multi-label classes CRIS, and not classification. For classassignmentby NTP, FDR < 0.2 was considered significant. Table S10b. NTP-based multiCRIS classification in the CRC organoids. Summary table of NTP results on total single-cell datasets and at sample levels with percentages of cells assigned, not classified, single-label CRIS class assignment and multi-labels CRIS assignments.Additional file 12: Table S11. NTP-based multiCRIS classification on pseudo-bulk CRC patients. NTP analysis has been performed on CRC patients data identifying sample with Single-label CRIS classification, Multi-labels CRIS classification, and Not classification. For classassignmentby NTP, FDR < 0.2 was considered significant.Additional file 13: Table S12a. NTP-based multiCRIS classification on the single-cell RNA-seq profiles from CRC patient. NTP analysis has been performed on scRNA-seq CRC patient data identifying cell with Single-label class CRIS, Multi-labels classes CRIS, and Not classification. For classassignmentby NTP, FDR < 0.2 was considered significant. Table S12b. NTP-based multiCRIS classification on the single-cell RNA-seq profiles from CRC patient. Summary table of NTP results on total single-cells datasets and at sample levels with percentages of Assigned, Not Classified, Single-label CRIS class assignment and Multi-labels CRIS assignments.Additional file 14: Table S13. Single-label performance evaluations of the considered algorithms on testing data, comparing their results with the ones of the original NTP approach.Additional file 15: Table S14. Multi-label performance evaluations of the considered algorithm-adapted strategies on testing data, comparing their results with multi-label assignments obtained from the multiCRIS version of the NTP approach.Additional file 16: Table S15. Multi-label assignments of the algorithm-adapted LSVM on TCGA testing samples, compared with multi-label assignments from the multiCRIS version of the NTP approach.Additional file 17: Table S16. Multi-label assignments of the algorithm-adapted LSVM on PDX samples, compared with multi-label assignments from the multiCRIS version of the NTP approach.Additional file 18: Table S17. Genes selected based on feature importance.Additional file 19. Supplementary material of "Multi-label transcriptional classification of colorectal cancer reflects tumour cell population heterogeneity.