Stroke is a leading cause of disability with limited effective interventions that improve recovery in the subacute phase. This protocol aims to evaluate the safety and efficacy of a non-invasive, extremely low-frequency, low-intensity, frequency-tuned electromagnetic field treatment [Electromagnetic Network Targeting Field (ENTF) therapy] in reducing disability and promoting recovery in people with subacute ischemic stroke (IS) with moderate-severe disability and upper extremity (UE) motor impairment. Following a sample-size adaptive design with a single interim analysis, at least 150 and up to 344 participants will be recruited to detect a 0.5-point (with a minimum of 0.33 points) difference on the modified Rankin Scale (mRS) between groups with 80% power at a 5% significance level. This ElectroMAGnetic field Ischemic stroke–Novel subacutE treatment (EMAGINE) trial is a multicenter, double-blind, randomized, sham-controlled, parallel two-arm study to be conducted at approximately 20 United States sites, and enroll participants with subacute IS and moderate-severe disability with UE motor impairment. Participants will be assigned to active (ENTF) or sham treatment, initiated 4–21 days after stroke onset. The intervention, applied to the central nervous system, is designed for suitability in multiple clinical settings and at home. Primary endpoint is change in mRS score from baseline to 90 days post-stroke. Secondary endpoints: change from baseline to 90 days post-stroke on the Fugl-Meyer Assessment - UE (lead secondary endpoint), Box and Block Test, 10-Meter Walk, and others, to be analyzed in a hierarchical manner. EMAGINE will evaluate whether ENTF therapy is safe and effective at reducing disability following subacute IS.www.ClinicalTrials.gov, NCT05044507 (14 September 2021). Stroke is a leading cause of long-term disability, especially as mortality rates are declining , 2. GiveBeyond the acute phase 1–7 days)  and into days  anFollowing injury, neuronal network connectivity is disrupted, with aberrant oscillatory patterns on electroencephalography (EEG) . As netwPreclinical results suggest that ENTF therapy post-stroke impacts cellular mechanisms and network reorganization , 19. In n = 21) found that ENTF therapy delivered in the early subacute phase  increased upper extremity (UE) motor function across multiple metrics and reduced disability (p = 0.007); baseline to week 8 (31.5 ±10.7 vs. 23.1 ± 14). Similar favorable effects at week 8 were observed for other UE assessments, including the Action Research Arm Test  and Box and Blocks Test . Reduction of global disability was assessed by the modified Rankin Scale (mRS)  , a globale (mRS) , moderatThe ElectroMAGnetic field Ischemic stroke–Novel subacutE treatment (EMAGINE) trial investigates the impact of ENTF therapy, on disability and functional recovery, in conjunction with SOC; ENTF treatment is introduced within 3 weeks post-stroke, a period in which the post-stroke brain has heightened plasticity potential –10. ENTFThe primary objective of EMAGINE is to determine the efficacy and safety of ENTF therapy in reducing disability in the subacute phase post-stroke. The hypothesis for efficacy is that mean improvement in the primary outcome, mRS score, from baseline to 90 days post-stroke will be significantly greater in participants allocated to active stimulation (ENTF group) than in participants allocated to sham stimulation (sham) group.This methods manuscript adheres to the methodology of the Standard protocol items: recommendations for interventional trials (SPIRIT) statement and checklist. Additionally, the description of the study intervention adheres to the methodology of the Template for Intervention Description and Replication statement and checklist (TIDieR). The primary results paper will adhere to the methodology of the Consolidated Standards of Reporting Trials (CONSORT) statement and checklist.EMAGINE is a multicenter, double-blind, randomized, sham-controlled, parallel, two-arm study following a sample-size-adaptive design with a single planned interim analysis. The study will be conducted at approximately 20 US-based acute care and inpatient rehabilitation facilities (IRFs) with enrollment from any site not exceeding 20% of the total sample size. Informed consent will be obtained 3–21 days post-stroke, and participation will continue up to 180 (±15) days post-stroke.Participants will undergo initial screening and detailed baseline assessment, followed by 45 treatment sessions over 9 weeks. Follow-up assessments will be at the 20th treatment session (±4 days), 90 (±15) days post-stroke, and 180 (±15) days post-stroke  .Prospective participants 3–21 days post-stroke will be consented to enter the study screening phase and at 4–21 days post-stroke, participants fully meeting entry criteria will proceed to the randomization phase and be allocated to a study treatment arm. This ensures that treatment will begin soon after stroke onset, when the brain has heightened plasticity potential –10. Key Participants will be equally allocated (1:1 ratio) based on a permuted block randomization scheme, stratified by site, age  and baseline mRS (3 or 4) to either the active (ENTF) or sham stimulation group. An authorized, trained, unblinded individual at the site will enter group assignment into the device based on a pre-programmed randomization algorithm embedded in an electronic data capture system (Syncrony). Participants, caregivers, outcome assessors, site investigators , and the sponsor will be unaware of group assignment. The device produces no noticeable sound, light or sensation in connection with stimulation, facilitating blinding. Blinding methods will incorporate appropriate security measures and access control. In an emergency, investigators may determine that a participant be unblinded.The investigational medical device  delivers a non-invasive, extremely low-frequency (1–100 Hz) and intensity ( ≤ 1 Gauss), frequency-tuned electromagnetic field. The device is portable, wearable and designed for use in multiple settings ; it includes embedded magnetic coils, a mobile device with a dedicated app that guides the treatment session, and a single-use adhesive electromagnetic sensor placed on the forehead to monitor treatment . The device can be used by a professional operator or lay person . The device does not require installation and it comes with all the necessary equipment and instructions for use. A video demonstrating the application of the device and general overview of the treatment session can be found in The first treatment session will be within 48 h from randomization and 4–21 days from stroke onset. There will be 45 treatment sessions over 9 weeks (5 per week). Each session will last up to 60 min, with ENTF or sham field applied for 40 min together with an evidence-based, functional, repetitive and graded PT/OT regimen (A trained site study team member (trained by sponsor personnel) will be responsible for training the participant's caregiver to operate sessions independently, whether in the clinic or at home. Sessions will be supervised, in person or remotely, until the caregiver is deemed capable of operating sessions independently with periodic study team oversight.All sites will provide concomitant SOC medical and rehabilitation therapies to prevent recurrent stroke and maximize recovery according to US national guidelines .The primary efficacy, secondary efficacy and exploratory endpoints are listed below, with a description of the clinical relevance of the main endpoints.EMAGINE's primary outcome measure is the modified Rankin Scale  change from baseline (4–21 days post-stroke) to 90 days post-stroke. The mRS is a broad scale with 7 levels from normal through increasing disability to death (scored 0–6). The modified Rankin Scale is the most frequently used outcome measure in stroke trials . The mRSA known limitation of the original mRS is score assignment based on poorly specified, non-operationalized distinctions between levels, leading to poor reproducibility of the score by various examiners , 28. IntGiven these limitations, the Rankin Focused Assessment (RFA) was developed with clear operationalized criteria for evaluating and determining disability levels; the RFA has high inter-rater reliability when used in a randomized control trial setting .To increase outcome comparability and minimize bias, the mRS assessments of global disability in the EMAGINE trial are obtained using the formal, algorithmic Rankin Focused Assessment-Ambulatory (RFA-A) method by RFA-A-certified raters , 31. A pRemote centralized assessment of the mRS via telehealth tools has been established in other trials –34. ConsSecondary efficacy endpoints will be analyzed in the following hierarchical order:Fugl-Meyer Assessment for Upper Extremity  – change from baseline (4–21 days post-stroke) to 90 days post-stroke. The Fugl-Meyer Assessment–Upper Extremity (FMA-UE) is a performance-based impairment index designed to assess motor functioning, balance, sensation, and joint function in patients with post-stroke hemiparesis . FMA-UE Box and Block Test –change from baseline (4–21 days post-stroke) to 90 days post-stroke. The BBT  measures10-Meter Walk Test –change from baseline (4–21 days post-stroke) to 90 days post-stroke.Stroke Impact Scale - Hand Domain –change from baseline (4–21 days post-stroke) to 90 days post-stroke.Stroke Impact Scale - 16  –change from baseline (4-21 days post-stroke) to 90 days post-stroke.5-level EuroQol-5 Dimension  at 90 days.Serious procedure- or device-related adverse events (AE).Device deficiencies to detect operational reliability.Montreal Cognitive Assessment –at 90 days post-stroke.Patient Health Questionnaire-8 –at 90 days post-stroke.Academic Medical Center Linear Disability Scale  at 90 days post-stroke.modified Rankin Scale –change from baseline to 180 days post-stroke.Stroke Impact Scale - Hand Domain –change from baseline to 180 days post-stroke.5-level EuroQol-5 Dimension  at 180 days post-stroke.Formal cost-effectiveness analysis over a lifetime horizon from the perspective of the United States healthcare system. An expert in cost-effectiveness analysis will provide the planned analysis for inclusion in the final statistical analysis plan prior to unblinding.Relationship between adherence to treatment and the clinical outcomes as measured by the adhesive electromagnetic sensor. Treatment adherence impact will be explored using correlation analysis, odds ratios, and cluster analysis .All assessments are performed by blinded assessors. To reduce inter-rater variability and enhance data integrity, in all participants nationally, the primary endpoint, as well as a set of additional questionnaire-based secondary and exploratory outcomes , will be assessed via telehealth tools by a centralized expert rater. The small panel of central expert raters will be highly experienced in outcome assessment and specifically certified in mRS administration. The remaining endpoints, which require physical presence  or access to records  are assessed or collected by blinded site clinical staff who underwent training and certification.An independent Data and Safety Monitoring Board (DSMB) will be established to oversee trial safety and efficacy. The DSMB will provide recommendations regarding recruitment, enrollment, AEs, modifying, or stopping the trial based on data review. The board will meet at least semiannually, either in person or by teleconference.Sample size was calculated to detect a 0.5-point difference between groups with 80% power at a 5% two-tailed level of significance, assuming a standard deviation (SD) of 1 point and 1:1 allocation ratio. The calculated sample size (PROC POWER in SAS V9.4) is 128 total participants, 64 participants per arm. Allowing for a 15% dropout rate, 150 participants should be randomized.Estimates for mean and SD of changes from baseline to Day 90 are taken from literature and pilot data , 43. In t-test or Wilcoxon rank sum test will be used. For comparison of proportions , chi-squared test or Fisher's exact test will be used.Study data will be summarized with descriptive statistics presenting: count and percent for categorical and discrete data, mean, standard deviation, minimum, and maximum for normally distributed continuous variables, and median, interquartile range, minimum and maximum for non-normally distributed continuous variables. Demographic and other baseline data will be compared between the groups. For comparison of continuous variables, two-sample The primary endpoint, mRS change from baseline to Day 90, will be compared between treatment groups using repeated measures analysis of covariance , treatment group by visit interaction with baseline mRS and age entered as covariates, random effect: site). Shift analysis of the primary endpoint will be performed as a sensitivity analysis. A hierarchical approach will be adopted for primary and secondary endpoints to control type I error due to multiple endpoint testing, such that the primary endpoint will be analyzed first and, only if the null hypothesis is rejected at a significance level of ≤ 5%, will secondary endpoints be tested.p-values will be reported for each endpoint even if, due to the hierarchy, the result is not considered statistically significant.Overall significance level will be 5% using two-tailed tests. Nominal The primary safety variable, the cumulative incidence  of AEs reported throughout the study in each of the study groups, will be presented in tabular format and will include incidence tables by severity and relationship to study device and/or procedure. Cumulative incidence will be summarized based on the percent of participants with one or more events with the associated confidence interval. Additionally, incidence will be summarized based on the total number of events allowing for multiple events per participant . AE rates will be compared between the study groups with a Fisher's exact test.The following analysis sets are defined for the EMAGINE study: Intent-to-treat (ITT) including all participants randomized, Modified ITT (mITT) including all participants from the ITT set for whom treatment was initiated, Per-protocol (PP) including all participants from the mITT analysis who have no clinically significant protocol deviations and were treated for a minimum of 20 completed sessions with one post-baseline data point, and Safety analyses set (SAF) including all participants who initiated treatment. The mITT analysis will serve as the principal analysis set for efficacy assessments and SAF as the principal analysis set for the analysis of safety. Further details and definitions can be found in Despite effective SOC acute interventions and established benefits of post-stroke rehabilitation programs, many patients have significant residual disability. Long-term disability is associated with economic burden, which is projected to increase with the aging population. Annual long-term post-hospitalization costs for patients with mRS 4 are estimated at $43,755 compared to $10,883 for mRS 1 . EMAGINEEMAGINE is designed to assess the safety and efficacy of ENTF therapy during the subacute phase post-ischemic stroke. Given high stroke prevalence and limited treatment options beyond the acute phase, EMAGINE results may indicate the viability of a post-stroke treatment that non-invasively targets and rehabilitates compromised brain networks to reduce disability and improve quality of life. If effective, ENTF may provide accessible and scalable treatment that follows the patient from clinic to home, unifying a fragmented care pathway.Written informed consent was obtained from the individual(s) for the publication of any identifiable images or data included in this article.JLS, PWD, and JS are coordinating principal investigators. JLS, PWD, JS, SCC, JJE, AL, AH, and NB were involved in study conception and design. AH led manuscript preparation. This manuscript was submitted by the authors on the behalf of the EMAGINE Investigators. The full list of EMAGINE Investigators and coordinators can be found in


Long-term cancer prognosis after initial surgical procedures is an unlikely endpoint for clinical trials. Medical claim databases may aid in addressing this issue regardless of limited information on disease and patient background. However, the long-term prognosis  following surgical procedures remains unclear. This study aimed to assess whether long-term outcomes, such as the exacerbation of long-term care needs and mortality, differ with surgical methods.Using a longitudinal study with linkage between medical claim and long-term care database, patients with primary colorectal cancer who underwent initial colonoscopies were identified through anonymized data in Japan . Odds ratios (ORs) for long-term outcomes  were analyzed using logistic regression to compare laparoscopy and endoscopic surgery to laparotomy.Overall, 3,744 primary colorectal cancer cases  were included. Compared to the laparotomy group, the crude OR for exacerbation of long-term care needs in the laparoscopic surgery group was 0.376 , while the OR for all-cause mortality was 0.22 .This is the first study to analyze long-term prognosis after surgery for patients with colorectal cancer to combine medical and long-term needs data. As the national health insurance claim database rarely includes information on cancer stage and comorbidities, better prognosis on endoscopic surgery may need careful interpretation. Therefore, laparoscopy has superior outcomes in terms of long-term care needs and mortality compared to those of laparotomy. The incidence of colorectal cancer (CRC) ranks second after that of lung cancer in Japan. Although the age-adjusted incidence rate is currently trending towards a decrease, 158,500 patients and 54,000 deaths were reported in recent annual reports . ConcurrAccording to a descriptive report from the Japan Cancer Registry in 2018, of the 24,611 registered patients with Stage I CRC, the distribution of first treatment regimen ratios was as follows: 26% received endoscopic surgery, 9% received laparotomy, and 58% received laparoscopy; among patients with Stage 0 CRC, >90% received endoscopic surgery , 13.This study aimed to investigate whether laparoscopic surgery was associated with a better long-term prognosis regarding the exacerbation of long-term care needs and mortality compared with laparotomy in patients with primary CRC using SKDB, linkage medical claim, and long-term care database.This was a retrospective cohort study using the SKDB administered by prefectural National Health Insurance (NHI) organizations . ShizuokFig 1 shows the data for 197,782 patients who underwent colonoscopy (D311) or rectal endoscopy (D312). Cases with modifier codes metastatic, recurrent, suspected, and postoperative CRC were excluded from the anonymized KDB data . Data on patients who underwent their first colonoscopy in 2012 were deleted to designate a disease-free period before the initial diagnosis. The final study was set for a 6.5-year follow-up period from January 2013 to September 2018. the initial procedures for primary CRC following initial colonoscopy or rectal endoscopy were classified into three groups: laparotomy, laparoscopy, and endoscopic surgery. An overlap of the surgical procedures was not considered in this study. Patients who underwent colorectal surgery with more invasive procedures, such as combined resection of other organs and those who received therapy other than surgical therapy were classified into the “others” group and were excluded from the final analysis.Based on the medical summary procedural codes shown in The primary evaluated outcome was an exacerbation of long-term care needs during a 6.5-year follow-up period following initial cancer surgery. In the Japanese LEMIS, an older adult in need can receive the necessary care service by self-pay (10–30%) at age ≥65 years . In caseThe secondary outcome evaluated herein was all-cause mortality. Information on this outcome was extracted from the “reasons for death withdrawal” provided in the NHI and LEMIS records. Both outcomes were followed up for 6.5 years in this claims database.post hoc power analysis indicated that the final sample size  was large enough to detect at least a medium-size effect of r = 0.3. Wilcoxon rank sum tests were used to compare continuous variables, and Chi-square tests were used to compare categorical variables. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression analysis with the laparotomy group as the reference. Age and sex were adjusted for Model 2, while possible confounders such as smoking status, alcohol consumption, physical activity, and significant variables at the baseline such as BMI, serum hemoglobin (Hb), and cardiovascular disease were adjusted for Model 3. Variables with a relatively large number of missing values in the health checkup data were excluded. As missing covariates did not occur completely at random among all participants, a simple missing data imputation was not carried out. Data from the SKDB were imported using SAS statistical software , and all analyses were performed using STATA statistical software .Given traditional assumptions , a S2 Table).All SKDB data were anonymized to protect participant confidentiality. Thus, informed consent from each enrollee was not required for this study . This stFig 1). Table 1 shows the characteristics of patients with CRC according to the type of surgery. The average age was highest in the laparotomy group (p < .001), and males were dominant among all groups.In total, 3,744 patients with primary CRC receiving surgery for inclusion in this study were identified: 822 patients who underwent laparotomy, 705 patients who underwent laparoscopic surgery, and 2,217 patients who underwent endoscopic surgery (p = 0.021). Laparotomy was performed less frequently with time, while laparoscopy was performed more frequently after 2017. Additionally, exacerbated long-term care needs (p < .001) and all-cause mortality (p = 0.013 for those aged ≥75 years and p < 0.001 for those aged <75 years) were the highest in the group undergoing laparotomy, although the influence of age and cancer stage may need to be considered in interpreting this information.The colon was the most common cancer site, and the rectum was the second most common site among all groups. The number of beneficiaries of long-term care insurance before surgery was 138 (16.8%) in the laparotomy group, 62 in the laparoscopy group (8.8%), and 105 (0.5%) in the endoscopic surgery group  and clinical data, showed no intergroup differences.Table 3 presents the logistic regression results for the exacerbation of long-term care needs in the laparoscopy and endoscopic surgery groups (Model 1) compared to those in the laparotomy group. The laparoscopy group showed significantly lower exacerbation of long-term care needs , while the endoscopic surgery group showed an OR of 0.231 . Similar results were obtained for all-cause mortality. These relationships did not significantly change on adjustment for age and sex (Model 2).In Model 3, the OR for long-term care was significantly lower in laparoscopic surgery than in laparotomy, with better goodness of fit of the overall model. However, the differences in beneficiaries of long-term insurance vary among groups and might affect the exacerbations of long-term needs. The risk was also significantly lower than the adjusted OR for all-cause mortality following laparoscopic surgery.The laparoscopy and endoscopic surgery groups showed a significantly better long-term prognosis than the laparotomy group regarding the exacerbation of long-term care needs and all-cause mortality risk. These findings were consistent even after adjusting for sex, age, and other relevant factors. However, the endoscopic surgery group results need to be carefully interpreted because most of these patients were identified with Stage 0 and 1 CRC after screening or follow-up for polyps by colonoscopy. Moreover, the treatment strategy for CRC is determined comprehensively based on factors such as prior abdominal surgeries, the stage of progression, obesity, and overall systemic conditions, including complications. However, the KDB database does not contain information; therefore, these contraindications were not considered in this analysis.The main strengths of this study were the large sample size collated from medical claims and long-term care data. It is generally difficult to configure natural prognoses in clinical trials. To the best of our knowledge, this is the first study to analyze post-discharge, long-term care needs, and all-cause mortality regarding CRC. Meanwhile, the total insurance benefits in 2019 (including long-term care preventive services) amounted to 837.8 billion yen in Japan  Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Extracellular newly identified receptor for advanced gycation end-products binding protein (EN-RAGE), C-C motif Chemokine ligand 23 (CCL23), Eukaryotic translation initiation factor 4—binding protein 1 (4E-BP1) and human glial cell-line derived neurotrophic factor (hGDNF) were significantly increased in plasma of endometriosis patients compared to controls, whereas Hepatocyte Growth factor (HGF) and TNF-related apoptosis inducing ligand (TRAIL) were decreased. In PF of endometriosis patients, we found Interleukin 18 (IL-18) to be decreased, yet Interleukin 8 (IL-8) and Interleukin 6 (IL-6) to be increased. TNF-related activation-induced cytokine (TRANCE) and C-C motif Chemokine ligand 11 (CCL11) were significantly decreased in plasma, whereas C-C motif Chemokine ligand 23 (CCL23), Stem Cell Factor (SCF) and C-X-C motif chemokine 5 (CXCL5) were significantly increased in PF of patients with DIE compared to endometriosis patients without DIE. Although DIE lesions are characterized by increased angiogenetic and pro-inflammatory properties, our current study seems to support the theory that the systemic immune system does not play a major role in the pathogenesis of these lesions.Endometriotic lesions are able to infiltrate surrounding tissue. This is made possible partly by an altered local and systemic immune response that helps achieve neoangiogenesis, cell proliferation and immune escape. Deep-infiltrating endometriosis (DIE) differs from other subtypes through the invasion of its lesions over 5 mm into affected tissue. Despite the invasive nature of these lesions and the wider range of symptoms they can trigger, DIE is described as a stable disease. This elicits the need for a better understanding of the underlying pathogenesis. We used the “Proseek Endometriosis is a benign gynecological disease, defined as the implantation of endometrial-like tissue outside of the uterine cavity , occurriDepending on the locations and characteristics of the lesions, three endometriosis subtypes have been defined:  peritoneal lesions, ovarian endometriosis cysts (known as endometriomas) and deep-infiltrating endometriosis (DIE), defined as an infiltration of ≥5 mm into the surrounding tissue, such as, more commonly, the uterosacral ligaments, the bladder or the rectum . This unThe exact pathophysiology of this disease still remains unknown. Numerous studies over the years have described inflammation as a key feature in the pathogenesis of endometriosis. Endometriotic lesions are characterized by their ability to implant and proliferate ,10, thusA simultaneous detection of different proteins involved in inflammatory processes has been made possible by multiplex technologies. Several studies have evaluated the usefulness of multiplex technologies for the detection of proteins associated with endometriosis ,17. Whil® Multiplex Inflammation I Panel” in order to expand our understanding of the local intraperitoneal as well as systemic inflammatory mechanisms that underlie the pathogenesis of endometriosis. First, we sought to compare patients suffering from endometriosis to controls to define differences in the immune response brought on by the disease itself. Since characteristics of the endometriosis subtypes differ from each other, we then attempted to define distinct local and systemic inflammatory properties of patients with deep-infiltrating endometriotic lesions.In this study, we used the “ProseekEighty-four patients were included in our study. In three of these study participants, however , ≥50% of markers were found to be under the limit of detection (LOD), either in plasma or in PF. These three patients were therefore excluded from further analysis. Thus, 81 patients were included in the final calculations, 51 patients with endometriosis (18 of which suffered from DIE) and 30 controls. Patient characteristics are summarized in For further data evaluation, we disregarded 23 , Chemokine ligand 23 (CCL23—1.22-fold), Eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1—1.43-fold) and human Glial-derived Neurotrophic factor (hGDNF—1.12-fold), whereas Hepatocyte Growth Factor (HGF) and TNF-Related Apoptosis Inducing Ligand (TRAIL) were significantly decreased in this patient group.When analyzing PF, we merely found three protein concentrations that significantly differed between the groups: Interleukin-6 (IL-6) and Interleukin-8 (IL-8) were increased (2.13- and 2.68-fold respectively), while Interleukin-18 (IL-18) was decreased 0.67-fold in PF of women suffering from endometriosis, compared to controls. In order to determine the influence of a deep-infiltrating lesion on the local and systemic inflammatory milieu, we compared the inflammation markers in plasma and PF of patients with DIE to women with endometriosis but without DIE. The proteins that differed significantly between these groups are summarized in In plasma, two proteins were significantly decreased in patients with DIE. Tumor necrosis factor-related activation-induced cytokine (TRANCE) and chemokine (C-C Motif) ligand 11 (CCL11) were decreased 0.70- and 0.84-fold, respectively, in patients with DIE. In PF four proteins were increased in patients with deep-infiltrating lesions: CCL23 (1.7-fold), CCL11 (1.46-fold), Stem Cell Factor (SCF—1.23-fold) and C-X-C motif chemokine 5 (CXCL5—2.54-fold).The key role of inflammatory processes in the development of endometriosis and its lesions has been evaluated in multiple studies. While many theories have been discussed regarding the pathogenesis of endometriosis, many questions still remain to be answered. In our study, we used a multiplex technology which enabled a simultaneous detection of inflammatory proteins in PF and plasma of patients suffering from endometriosis and controls, in hopes of gaining new insights into underlying pathophysiological mechanisms of endometriosis, and especially of DIE. We therefore performed two sub-analyses in our study, comparing plasma and PF of patients with endometriosis to samples of controls, and in a second step, comparing patients suffering from DIE to endometriosis patients without deep-infiltrating lesions. In Two markers (EN-RAGE and CCL23) known for their chemoattractant characteristics were found to be increased in plasma of endometriosis patients compared to controls.EN-RAGE genes were found to be significantly higher expressed in endometrial stromal cells (ESC) in endometriosis patients compared to controls by Sharma et al. . EN-RAGEThe association between HGF and endometriosis has been described in numerous studies , promotiGene expression of 4EBP1was found to be increased in eutopic endometrium of endometriosis patients yet decreased in ovarian endometriotic lesions in these patients . These cThe main function of TRAIL lies in its ability to induce apoptosis. Kim et al. showed results similar to those found in our analyses, with a reduced TRAIL expression in serum of women suffering from endometriosis , a traitVery little data have been published to date regarding the involvement of hGDNF in endometriotic lesions. Chang et al. and Streiter et al. demonstrated the importance of this neurotrophic factor in normal ovarian function and follicular development and suggested that an altered expression might lead to the development of ovarian diseases such as polycystic ovary syndrome (PCOS) or endometriosis ,37. To oIn PF of endometriosis patients compared to controls, three pro-inflammatory cytokines  were significantly differently expressed. The data published regarding IL-18 expression in endometriosis patients show discrepant results. Our results are in line with those described by Zhang et al.: Here too, IL-18 expression was significantly reduced in PF of patients with endometriosis, while this did not correlate with expression in plasma. This finding was used to explain the known reduced NK-cell activity and impaired T-helper-cell immune response seen in patients with endometriosis . In contOn the other hand, IL-6 and IL-8 were overexpressed in PF of women suffering from endometriosis, which underlines the local pro-inflammatory environment seen in our patient collective. Our data confirm the results found by Wang et al., who described a proliferation-stimulating effect on peritoneal endometriotic lesions . Li et aDIE differs from other endometriosis subtypes in that these lesions penetrate exceeding 5 mm into surrounding tissue. Because of its particular characteristics, the understanding of the pathophysiology of this subtype has become the center of recent studies. Although DIE should not be considered a progressive disease, the impact on patient’s symptoms and the challenges for surgeons dealing with these lesions are high . Zhou et al. recently described different cytokine signatures differentiating between the three previously mentioned subtypes of the disease . The authors used a multiplex assay of 48 cytokines on PF of endometriosis patients and found a six-cytokine signature of IL-8, IL-12p70, IL-16, IL-18, MCP-1 and MIP-1α, all upregulated in patients with DIE compared to peritoneal endometriosis, as well as a seven-cytokine signature comprising IL-1α, IL-1RA, IL-8, IL-12p40, IL-12p70, IL-16 and TNF-α when comparing patients with ovarian endometriomas and patients with DIE . AlthougCompared to endometriosis patients without DIE, two proteins were found to be significantly decreased in plasma of patients with DIE. To our knowledge, TRANCE has never been connected to the pathophysiology of endometriosis. Our data show lower values in plasma of patients suffering from DIE, which might contribute to immune escape seen in endometriotic lesions.The important role of CCL11 in endometriotic lesions has been attributed to its recently described angiogenetic potency . In our Suzumori et al. have described significantly elevated levels of CXCL5 in PF of endometriosis patients compared to controls, in particular, however, in patients suffering from more severe stages (stage III and IV) . While oAnother cytokine involved in neoangiogenesis is the previously mentioned CCL23. This cytokine was found to be significantly overexpressed in our patient collective suffering from DIE compared to endometriosis patients without DIE, which might suggest an increased vascularization of these lesions.Osuga et al. analyzed the concentration of SCF in plasma and PF of patients with endometriosis and controls. The authors described an increased expression of SCF only in PF of affected patients. When comparing patients according to their endometriosis rASRM stage, this significant difference was only maintained in patients with stages I and II, compared to controls . Here, iIn vivo data have shown that there seems to be a causal effect of a systemic immune response on the development of endometriotic lesions . However® Multiplex Inflammation I Panel” in patients suffering from DIE. Although DIE is a subtype characterized by increased angiogenetic and pro-inflammatory properties [In conclusion, while the important role of inflammatory processes in the development and persistence of endometriotic lesions has been repeatedly demonstrated, our multiplex analysis of 92 inflammatory proteins failed to show drastic differences regarding local and systemic inflammatory response in patients suffering from endometriosis compared to controls. Surprising to us were especially the very few differences found in patients with deep-infiltrating lesions. As such, we believe it may be difficult to identify biomarkers that allow the non-invasive diagnosis of DIE. Although these lesions invade deeper into surrounding tissue and create more anatomic distortions, adhesions and tissue fibrosis, our data showed only few differences regarding inflammatory marker levels measured in the “Proseekoperties , our curThe plasma and peritoneal fluid (PF) samples were collected as part of the Endometriosis Marker Austria (EMMA) study, a prospective cohort study conducted at the tertiary, certified referral Endometriosis Center. This study was approved by the Ethics Committee of the Medical University of Vienna (EK 545/2010). The premenopausal patients selected for this particular study were between 18 and 50 years of age and all underwent laparoscopic surgery during the period of 2010 to 2015 at the Department of Obstetrics and Gynecology at the Medical University of Vienna, for suspected endometriosis, benign ovarian cysts, uterine fibroids, chronic pelvic pain or infertility. All patients included in the “endometriosis group” all had histologically confirmed endometriotic lesions, whereas patients without endometriosis were defined as the “control group”. During the surgery, the endometriosis cases were scored according the rASRM score as minimal (I), mild (II), moderate (III) or severe (IV). In cases of DIE, the disease was furthermore classified according to the ENZIAN scoring system.Information on baseline patient characteristics such as age, BMI, gravidity, parity, as well as the intensity of endometriosis-associated symptoms, notably dysmenorrhea and dyspareunia evaluated using the visual analog scale (VAS), was obtained preoperatively. Statistical testing on patient characteristics included Chi-squared tests, parametric t-tests and Mann–Whitney-U test, and was performed using IBM SPSS statistics version 27.Citrate plasma and PF were collected prospectively in accordance with the harmonization guidelines, plasma in a fasting state in the morning on the day of surgery, and peritoneal fluid after a lavage of the peritoneal cavity with 10 mL of sterile 0.9% NaCl. The samples were immediately centrifuged (plasma at 1000 rounds per minute (rpm) and PF at 3000 rpm at 4 °C for ten minutes) and stored in aliquots at −80 °C until analysis.® Multiplex Inflammation I Panel” . A list of all analyzed proteins is shown in Ninety-two proteins were measured simultaneously using the “ProseekThe measurement was carried out according to the Proseek Multiplex 96 × 96 User Manual. Specifically, the Proseek reagents are based on Proximity Extension Assay technology, in which 96 oligonucleotide-labeled antibody probe pairs bind to their respective protein targets in the sample. A polymerase chain reaction (PCR) reporter is formed by a proximity-dependent DNA polymerization event which is detected and quantified by real-time PCR, generating quantitative values in arbitrary units.The Fluidigm raw data’s quality was assessed according to OLINK guidelines (Data Preprocessing v1.0). Values in PF were normalized to the total protein amount (measured with the Bradford Assay) in order to compensate for technical differences due to potentially slightly different lavage volumes during PF sample collection.Biostatistical analysis was performed using BRB Array Tools Version 4.4.1  and included class comparison (conducted at a significance level of 0.05), whereby different feature selection criteria were applied. The Normalized Protein eXpression (NPX) values were imported into BRB Tools, whereby all normalization methods were disabled, since the data gained in PF were already normalized in the data preprocessing procedure. According to BRB Array Tools software, the log2 transformed data were used for multiplex data analysis, and geometric mean as well as linear fold change between the groups (patients with endometriosis vs. controls and endometriosis with DIE vs. endometriosis patients without DIE) were calculated .


The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods. Unfortunately, COVID-19 remains a deadly pandemic claiming over 621 million confirmed cases and 6.5 million deaths worldwide Laboratory-based methods such as nasopharyngeal swab test and reverse transcription-polymerase chain reaction RT-PCR are the common gold standard that detects the specific genetic material of the virus. However, these laboratory-based tests are time-consuming and require expert laboratory technicians and clinical facilities Recently, there has been a significant increase in scientific community cooperation Recently, medical image diagnosis based on CXR image analytics has been gaining popularity for COVID-19 detection. CXR is widely available and economical, besides the automatic diagnosis based on CXR images has advantages over radiologists' examination, including repeatability and improvements too subtle to be noticed visually. Also, cough sounds can be used as pre-screening tests for infected patients. Coughing sounds can be converted to image vectors using DL approaches then these images can be analyzed by a CAD system to detect respiratory abnormalities like fine crackles and coarse crackles remotely. Cough sound classification utilizing artificial intelligence is an urgently needed area of research Deep learning (DL) technology has assisted radiologists in detecting various respiratory problems with high diagnostic accuracy through medical imaging According to the literature review, most DL model research uses pre-trained deep-learning architectures to diagnose COVID-19 based on cough sounds and medical images. Furthermore, most studies generally focus on diagnosing COVID-19 based on a single modality. The research gap in this review are as follows: (1) Due to the virus's constant gene mutation, CXR images alone cannot accurately detect the virus in its early stages -Propose a framework for automatic and accurate COVID-19 classification based on multimedia Vital Signs using CNN, TL, FIS, and MRFO Algorithms.-Four cascade non-invasive diagnostic phases to reliably determine if a patient is infected with COVID-19.-The hyperparameters are optimized using the state-of-the-art Manta-Ray Foraging Algorithm (MRFO), which allows for reporting optimal performance data.-To correct the problem of underrepresentation, the synthetic Minority Oversampling Method (SMOTE) is used to perform oversampling.-The suggested method is flexible since the hyperparameter values of the CNN architecture are automatically determined.-Concerning accuracy, DenseNet201 provides the highest performance metrics.-Four models suggest a ratio of more than 50% for TF learning, and four models offer a percentage greater than 40% for dropout.-Tests are conducted five times to obtain validated results, and the CI for each statistic is measured.The main objective of this study is to introduce a four-layer diagnostic framework for COVID-19 based on elementary patient diagnosis, cough profile, and CXR. Furthermore, a fuzzy logic inference system is used to report an accurate decision. The contributions of this study can be summarized in the following points:The rest of the paper is organized as follows: Section 2Rapid growth has been observed in using deep learning (DL) architectures in medical imaging, including computer-assisted diagnosis systems and medical image analysis, enabling more accurate screening, diagnosis, prognosis, and treatment of numerous relevant disorders Current research on the classification of medical datasets reveals substantial constraints, notwithstanding the effectiveness of the applications mentioned above. There is much writing on data imbalances between classes since insufficient training data exists. High accuracy under these settings is no guarantee of excellent illness identification Up till now, DL techniques have been implemented in isolated environments. These algorithms are “trained” to handle specific problems. When the distribution of the feature space changes, the models must be rebuilt. Transfer learning is using a model developed for one purpose as the basis for developing a model for another. More refined adjustments to the new model are possible by adding more training data and modernized neural layers By learning from abstract representations, CNNs may evaluate images with a high level of semantics. CNN utilizes filter banks instead of manually generated ones to optimize the image texture. It is well acknowledged that the availability of enormous amounts of data is one of the literature's limitations. The practical use of the technologies mentioned above is unreliable since DL models perform better with more datasets. In addition, these models are constructed using standard parameters. The dataset and the hyperparameters chosen determine the classification performance of a CNN. The hyperparameter parameters have a significant effect on a CNN's training phase. It is essential to remember that model weights and hyperparameters differ. The former is calculated beforehand, whereas the latter is enhanced by training Practical engineering problems generally have numerous locally optimal solutions; therefore, deterministic algorithms struggle with them. However, high-dimensional search spaces demand precise optimization. With search spaces expanding exponentially with problem size, thorough searches are unfeasible. In contrast to deterministic algorithms, metaheuristic algorithms can generate an approximate answer without the exact solution. Consequently, computations can be greatly streamlined. In addition, randomness introduced by meta-heuristic algorithms can eliminate the local optimal problem. These advantages apply to metaheuristic approaches to global optimization problems. In the past decade, numerous Swarm intelligence algorithms, such as the Particle Swarm Optimization Algorithm, the Krill Herd Optimization Algorithm, and the Beetle Antenna Search Algorithm, have been proposed 2.1Metaheuristics explore large sets of solutions with less processing power than calculus-based or simple heuristics. Adaptability, self-learning, flexibility, unpredictability, mutually balanced intensifications, diversifications, intuition, robustness, and efficiency evolved to produce intelligent behavior and biological phenomena. Bio-inspired computation is one of the AI fields with the most study. The most recent accomplishment in this field was the development of Swarm Intelligence (SI). Swarm Intelligence, a subfield of bio-inspired computation, is founded on the collective intelligence of enormous populations of animals with basic communication and interaction patterns. Ant colony optimization (ACO), particle swarm optimization (PSO), cuckoo search (CSA), elephant herding optimization (EHO), whale optimization algorithm (WOA), and Manta-Ray Foraging Algorithm (MRFO) are common SI methods (SSO). A highly useful taxonomy of natural optimization approaches contains 132 separate entries The Manta Ray Foraging Optimization (MRFO) algorithm models the food-seeking behaviors of manta rays, such as chain foraging, cyclone foraging, and somersault foraging Manta ray foraging optimization (MRFO) is one of the recently published bioinspired metaheuristic algorithms with the potential to solve multiple engineering optimization challenges. MRFO is a suitable method for defining unknown model parameters of commercial photovoltaic units, solving economic power and heating dispatch problems, tracking maximum power points in solar cells, and maintaining thermo-economic optimization of air-fin coolers Manta rays employ a chain technique to approach food sources by aligning themselves head-to-tail. MRFO's area with the greatest concentration is regarded as the best. Chains of foraging manta rays mimic the natural process of capturing food Additionally, the MRFO adopts the cyclone strategy The performance of two distinct mechanisms of metaheuristic algorithms, exploration, and exploitation, depends on cycle foraging Finally, somersaulting foraging is one of the most stunning natural behaviors living organisms perform. Manta rays encircle their prey plankton and spin backward as they ingest them In this part, the basic processes of MRFO are described. MRFO models the three feeding techniques of manta rays: chain foraging, cyclonic foraging, and somersaulting foraging. Similar to other swarm-based metaheuristic algorithms, MRFO first produces the initial populations at random. It is then updated using the three methods mentioned above.2.2-A database of IF-THEN rules;-The membership functions of all fuzzy sets featured in the rules;-The decision-making model executes inference operations on the rules.-The fuzzification model converts the results of the inference into a crisp output.When uncertainty and imprecision exist, precise logic cannot be applied. In fuzzy logic, it is feasible to calculate results by describing partial membership in sets. The purpose of fuzzy logic is to build a computing paradigm based on how humans think since most classes of real-world objects lack clearly defined membership criteria. Several problems have been modeled with fuzzy set theory and inference systems, particularly those involving decision-making with two or more assessment criteria. It has been proposed that fuzzy inference systems can be used to model qualitative and non-numerical assertions. FIS uses fuzzy numbers and operations to express knowledge via the behavior of linguistic variables governed by “IF-THEN” rules In a fuzzy inference system, the input and output variables might take on various linguistic values , which must be expressed as fuzzy numbers. The rule base determines the result, which depends on the input variables' values and the established rules. For example, IF “Fever” is “high” AND “Saturation” is “low,” THEN “Severity” is “high,” where Fever and Saturation are input variables, and Severity is the output variable. The output fuzzy number of each rule is determined by the input variables' values and the implication relation.3This section reviews the most recent literature on COVID-19 classification based on cough sounds and imaging modalities. Loey and Mirjalili Unais et al. Ahmadian et al. Khan et al. In the study of Loay Aly and Alotaibi Swarup and Anupam Munish et al. 4The main objective of this research is to develop a non-invasive diagnostic framework for accurately detecting COVID-19 in patients, using four cascaded layers, as depicted in 4.1As mentioned before, the first layer of the suggested non-invasive diagnostic framework initiates the first insight into the patient's case as it handles the elementary diagnosis such as heart rate, temperature, blood oxygen level, food taste, and breathing profile. COVID-19 infected patients have reported a wide range of symptoms ranging from mild to severe, typically appearing 2 to 14 days after virus exposure In Wang et al. ∘F (37 ∘C). According to various studies, normal body temperature can range between 97 ∘F (36.1 ∘C) to 99 ∘F (37.2 ∘C). A fever of further than 100.4 ∘F (38 ∘C) is typically caused by an infection (or disease). The temperature of the body changes during the day From that (1) when tested with a pulse oximeter, people with healthy lungs should have an oxygen level of 80-100 mmHg, or 95% to 100% 4.2t-second coughing audio profile. COVID-19 patients are diagnosed by a dry cough and shortness of breath In the second layer, the patient records a To perform that, a DNN model is required to be trained and optimized. The current study used the “Cough Dataset” there are two challenges: (1) the different audio lengths and (2) the dataset imbalanced criteria. Both of these challenges are solved before the training and classification process. Each audio record is segmented into equal 5-second segments and 16 feature types are extracted from each segment as presented graphically in The records do not have a standard  length . For example, some records can last for 5 seconds while another some can last for 10 seconds. From these details, From the audio records, as noted, 16 feature types are extracted from each segment. They are (1) Mel-Frequency Cepstral Coefficients (MFCC) Slaney a and locating its k-nearest minority class neighbors. The synthetic instance is then created by randomly selecting one of the k-nearest neighbors b and connecting a and b in the feature space to create a line segment. Convexly combining the two selected examples a and b yields the synthetic instances To overcome the imbalanced issue, oversampling is applied using Synthetic Minority Oversampling Technique (SMOTE) The data is now ready to be injected in the classification and optimization phase. As noted, a DNN architecture is used in this phase. It consists of 12 layers as shown in 4.3In the third layer, the patient is projected to a chest X-ray (CXR). This scan is diagnosed after that and graded into four classes  which is considered a multiclass classification problem. This layer utilizes the transfer learning approach and metaheuristic optimization. The transfer learning is applied on five pre-train CNN model . The utilized metaheuristic optimizer in the current study is the Manta-Ray Foraging Algorithm (MRFO) Initially, at the start of the optimization phase, the population is generated at random. The working population has a total of After that, the fitness function score of each solution in the population is computed. It is divided into four steps: (1) conversion of hyperparameters, (2) model preparation, (3) training, and (4) model validation. The hyperparameters conversion process transforms the solution into the actual hyperparameters as shown in t, α (and β) are weight coefficients, S is the somersault factor, and r . They are (1) accuracy, (2) balanced accuracy, (3) precision, (4) sensitivity, (5) specificity, and (6) F1-score. Accuracy Equation  seeks to4.5In this layer, a fuzzy logic inference system is utilized based on the previous three layers to report a trusted (or accurate) decision. The Fuzzy Inference System is the central core of a fuzzy logic system, with decision making as its major function. It creates basic decision rules using the “IF-THEN” rules and connectors “OR” or “AND” 5The experiments are performed in two sections. The first section discusses and reported the experiments related to the second layer while the second section handles the third layer. The working environment is Google Colab and the used programming language is Python.5.1As mentioned in the suggested approach in the second diagnosis layer, the “Cough Dataset” To study the effect of oversampling using SMOTE using the cough dataset, the experiment is run again using a batch size of 512 but without oversampling. 5.2As mentioned in the suggested approach in the third diagnosis layer, the “COVID-19 Radiography Database” The experimental results demonstrate that the proposed framework is trustworthy and effective in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy as follows: (1) audio-based classification: 96.55%, 93.78%, 99.72%, 93.39%, 96.66%, and 96.45% respectively. (2) CXR-based classification: The DenseNet201 reports 98.55%, 97.13%, 97.07%, 99.04%, 97.10%, and 98.05% respectively. The accuracy achieved in this study is comparable to the accuracies reported in other studies that used CXR images, such as Ahmadian et al. 5.3The clinical outlook for the proposed algorithm is positive as it introduces a four-layer diagnostic framework for COVID-19 that is flexible, automated, and non-invasive. The method utilizes multiple modalities, such as temperature, blood oxygen levels, breathing patterns, cough sounds, and chest X-rays, achieving high accuracy in COVID-19 diagnosis. Furthermore, the system has the potential to minimize the risk of virus transmission as it does not require close contact between patients and healthcare providers, which is crucial during the ongoing COVID-19 pandemic.In terms of the technical outlook, the proposed four-layer diagnostic framework for COVID-19 is based on machine learning techniques such as CNN, TL, FIS, and MRFO algorithms. The CNN architecture extracts features from input data, and TL fine-tunes the CNN model for optimal performance. The FIS algorithm handles uncertainty and imprecision, and MRFO integrates the outputs of different diagnosis layers. Performance metrics show high accuracy, sensitivity, and specificity in diagnosing COVID-19, and the CNN model's hyperparameters are automatically determined, making the method flexible. Overall, the technical outlook of the system is promising, and it has the potential to accurately and efficiently diagnose COVID-19 using multimedia vital signs.6The suggested non-invasive diagnostic framework has some limitations that should be considered. Firstly, the framework employs two datasets that may not be representative of the global population, which could limit the accuracy and applicability of the model. Secondly, the research outcomes were derived from a controlled environment and have not been tested in real-world medical scenarios, so the model's performance may differ in clinical settings. Thirdly, the proposed framework relies on multiple modalities, including temperature, blood oxygen levels, breathing patterns, cough sounds, and chest X-rays, which could increase the complexity of the diagnostic process and require additional resources. It is important to note that obtaining consistent data from the same patient for all four phases was not feasible in the current research, and as a result, the fourth phase was not evaluated. However, a detailed description of the fourth phase is provided in Subsection 7The COVID-19 pandemic impacts society and dramatically affects healthcare centers. Deep learning advancements have significantly aided in proper COVID-19 control and management. Cough sounds and medical image modalities of real-life patient datasets are used for training deep-learning models to diagnose COVID-19. However, relying on a single modality cannot accurately detect the virus. This study suggests a multimodal classification framework that consists of four layers. The first layer is the diagnosis layer that captures the elementary diagnosis of patients, such as Heart Rate, Blood Oxygen Level, Temperature, Food Taste, and Breathing Profile. The second layer is Diagnosis Layer which applies audio features extraction on the coughing profile, preprocesses the features, and classifies them using a deep neural network. The patient is projected to a chest X-ray in the third layer. Following that, the scan is diagnosed and classified into four classes . A fuzzy logic inference system in the fourth layer is used to report a trusted (or accurate) decision based on the previous three layers. Two datasets were used to apply the suggested approach (1) “Cough Dataset contains 3,805 train and 5,732 test records. (2) “COVID-19 Radiography Database “dataset consists of 21,165 images in four classes. The experimental results demonstrate that the proposed framework is trustworthy and effective in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy as follows: (1) audio-based classification: 96.55%, 93.78%, 99.72%, 93.39%, 96.66%, and 96.45% respectively. (2) CXR-based classification: The DenseNet201 reports 98.55%, 97.13%, 97.07%, 99.04%, 97.10%, and 98.05% respectively.Saleh Ateeq Almutairi contributed to all aspects of this research and manuscript. Specifically, he: (1) conceived and designed the study, (2) conducted the experiments and collected the data, (3) analyzed and interpreted the data, (4) drafted and revised the manuscript, and (5) approved the final version of the manuscript for submission.The author, Saleh Ateeq Almutairi, certifies that he has NO conflict of interest with another person or organization that might influence the work of this study.


Carcharodon carcharias) were caught using SMART drumlines and tagged with satellite-linked radio transmitters (SLRTs) and acoustic tags before being released to examine the short-term post-release movements and longer-term fate of White Sharks after capture, tagging, and release. During the first three days after release, the sharks moved away from the shore and stayed mostly offshore. Although sharks gradually moved closer to the shore 10 days after release, 77% of the sharks remained more than 1.9 km away from the coast and an average of 5 km away from where they were tagged. The sharks were acoustically detected for an average of 591 days after release, with detections ranging from 45 to 1075 days, highlighting longer-term survival. Although five out of the 36 sharks were not detected by the acoustic receivers, the SLRTs indicated that these sharks were alive and well, with detections ranging from 43 to 639 days after release. These findings demonstrate the effectiveness of SMART drumlines as a non-lethal method to mitigate bites by White Sharks.Conflicts between humans and sharks have often been dealt with by catching and killing sharks. However, there is now a growing demand for methods that protect water users from shark bites while minimizing harm to all species. Shark-Management-Alert-in-Real-Time (SMART) drumlines, a new non-lethal shark mitigation method, alert responders when an animal takes the bait, giving them the opportunity to quickly respond. In a study conducted in New South Wales, Australia, 36 White Sharks (Carcharodon carcharias), given their status as a Threatened, Endangered, or Protected (TEP) species. A new non-lethal shark bite mitigation method, known as the Shark-Management-Alert-in-Real-Time (SMART) drumline, alerts responders when an animal takes the bait and thereby provides an opportunity for rapid response to the catch and potentially to relocate, tag, and release sharks. Thirty-six White Sharks were caught on SMART drumlines in New South Wales, Australia, and tagged with dorsal fin-mounted satellite-linked radio transmitters (SLRTs) and acoustic tags before release. Thirty-one sharks were located within 10 days, 22 of which provided high-quality locations (classes 1 to 3) suitable for analysis. Twenty-seven percent and 59% of these sharks were first detected within 10 and 50 h of release, respectively. For the first three days post-release, sharks moved and mostly remained offshore (>3.5 km from the coast), irrespective of shark sex and length. Thereafter, tagged sharks progressively moved inshore; however, 77% remained more than 1.9 km off the coast and an average of 5 km away from the tagging location, 10 days post-release. Sharks were acoustically detected for an average of 591 days post-release (ranging from 45 to 1075 days). Although five of the 36 sharks were not detected on acoustic receivers, SLRT detections for these five sharks ranged between 43 and 639 days post-release, indicating zero mortality associated with capture. These results highlight the suitability of SMART drumlines as a potential non-lethal shark bite mitigation tool for TEP species such as White Sharks, as they initially move away from the capture site, and thereby this bather protection tool diminishes the immediate risk of shark interactions at that site.Human-shark conflict has been managed through catch-and-kill policies in most parts of the world. More recently, there has been a greater demand for shark bite mitigation measures to improve protection for water users whilst minimizing harm to non-target and target species, particularly White Sharks ( Globally, shark interactions have increased over the past three decades ,2,3. TheCarcharhinus leucas, the Tiger Shark Galeocerdo cuvier, and the White Shark Carcharodon carcharias in New South Wales, NSW). In some regions, ongoing shark bite mitigation measures have been in place for decades [Traditionally, shark mitigation programs have initiated a response that incorporates catch-and-kill measures against a suite of species thought to be responsible for the majority of shark bites  incorporates substantial by-catch of marine mammals, reptiles, rays, and non-target sharks ,13,14,15In south-eastern Australia, the majority of shark bites occur in nearshore areas, with a seasonal peak between November and April . Most sehttps://www.sharksmart.nsw.gov.au, accessed on 17 July 2023). One of these new shark bite mitigation measures was SMART  drumlines, which were developed on Réunion Island in 2013 [Following a cluster of shark bites in northern NSW in 2014/2015, the NSW Government established the NSW Shark Management Strategy (SMS), which trialed a suite of new and emerging shark bite mitigation measures with the objective of increasing protection for water users while minimizing harm to target and non-target species , Evans Head (−29.1 S 153.4 E), Coffs Harbour (−30.3 S 153.2 E), Crowdy Head (−31.8 S 152.7 E), and Tuncurry , technical parameters of time of capture and release (h:m), and release location coordinates. All sharks were tagged with dorsal fin-mounted satellite-linked radio transmitting (SLRT) tags following the methods of Bruce and Bradford . These tEach shark was also tagged with a uniquely numbered identification tag  to enable individual identification if they were recaptured by other fishers. These tags were inserted into the musculature at the base of the first dorsal fin. As part of a larger research project, acoustic transmitters with transmission intervals of 40–80 s and a 10-year battery life were surgically implanted into the abdominal cavity following the general procedure described by Bruce and Bradford . These tOnly satellite detections with location classes of 1 to 3 were included in the analyses , so that all the locations had a maximum error radius of <1500 m. All locations were reprojected to an equal-area projection system .https://data.gov.au/dataset/geodata-coast-100k-2004, accessed on 17 July 2023). Data were derived from a 1:100,000 scale topographic map, and coastline features were defined from the mean high-water mark. This shapefile was reprojected into the same datum as the satellite tag locations. The ‘near’ tool in ArcGIS 10.5 , Redlands, CA, USA) was used to calculate the distance from (i) the coast for each of the locations for the first ten days post-tagging and (ii) the post-tagging release location to the first detection. The Geodesic method was used for both, as this accounts for the curvature of the earth to correctly measure the distance between two features. The location of the VR4G acoustic receivers was used as the shark location for all locations from acoustic tags.A shapefile of the coastline of Australia was downloaded from the Geoscience Australia data repository , or time since release (in days) influenced the distance from the coast that each shark was detected following tagging. The response variable (distance from the coast) was positively skewed, so it wasn’t appropriate to use a linear model. Mixed models were used to account for the repeated measurements on individual sharks (with the unique shark tag code used as a random effect). A Generalised Additive Mixed Model (GAMM) and a generalized linear mixed model (GLMM) with a Gamma link function were tested, and the GLMM had the lowest Akaike information criterion corrected for sample size (AICc), so it was used for subsequent modeling. The GAMM was implemented using the mgcv package  and the Thirty-six White Sharks, comprising 19 females  and 17 males , were caught on SMART drumlines, tagged, and released. Sharks were released 34–121 min (mean 56.7 ± 15.0 min) post-hooking, with an elapsed time (mean and range) from when each shark was secured at the boat to release of 31.8 ± 8.2 min ; however, only 22 sharks had locations with classes 1 to 3 . The aveOnly five of the 36 acoustically tagged sharks were not detected on any of the 21 tagged shark listening stations across the 1324 to 1461-day post-release period . For theFour sharks were detected relatively shortly after release at the same location as their release . Three oModel selection for a GLMM produced eight candidate models with an AICc of <10 . RelativThis is the first study to quantify the short-term movement and longer-term survival of White Sharks after catch and release from SMART drumlines. Post release, White Sharks moved offshore before moving back to the nearshore coastline. While SMART drumlines are a useful tool for catching White Sharks by intercepting them before they can interact with water users, their offshore movement after release provides a means of minimizing further shark-human interactions by those individuals in that area in the short term. The potential benefits that SMART drumlines offer for reducing shark-human interactions are discussed below by considering the movement and longer-term survival of White Sharks after release. Although White Sharks are a globally targeted species for shark bite mitigation measures, they are also protected throughout much of their range  and, as Carcharhinus obscurus) and Sandbar Shark (Carcharhinus plumbeus) [All White Sharks were detected post-release, either through their SLRT or acoustic tags. The distance from the release site to the first detection indicates that the sharks actively swam away after release. All sharks swam offshore once released, a behavioral post-release response to capture that is well documented for other shark species such as Dusky Shark (lumbeus) . When usUsing animal-borne cameras incorporating multi-sensor biologging tags, Grainger et al.  monitore−1 have been documented in White Sharks off eastern Australia using drones [−1, [−1, [−1 have been recorded when White Sharks are near potential prey, such as seal colonies [The speed of movement post-release varied among individuals, yet most individuals moved relatively fast and generally offshore. Some individuals were detected up to 40 km offshore from the release site in the first 24 h post-release. Average swimming speeds of 0.8 m sg drones  and SLRTg drones . These snes [−1, ; average−1, [−1, ) and maycolonies  and schocolonies , it is eAlthough the explanatory variables investigated in this study were not key predictors for the distance from the coast where sharks were located within the first 10 days post-tagging, female and smaller White Sharks were most likely to return inshore during this period. Previous studies have also indicated a tendency for females to favor inshore habitats, with an inclination among immature male White Sharks towards large-scale migrations ,38,71. Thttps://www.sharksmart.nsw.gov.au/technology-trials-and-research/smart-drumlines/ accessed on 1 June 2023) suggests that female White Sharks are more likely to be found within nearshore waters off the east Australian coast. Subsequently, they are more likely to move back inshore into their apparent preferred habitat.The size  of the shark affected distance to the coast at 10 days post-release, implying that larger sharks were continuing long-shore movements in deeper water. Bruce and Bradford  hypothesPrevious research has shown that White Sharks undergo regular long-distance movements ,75,76,77The rapid response coupled with careful and timely handling of White Sharks caught on SMART drumlines has led to their successful release, with sharks showing little physiological response to the capture experience ,53,54 anWhite Sharks are the species most implicated in shark bites in Australia, and their seasonal occurrence in nearshore areas of NSW has implications for human-wildlife conflict management. By using satellite and acoustic tagging technologies to monitor the post-release movements of White Sharks caught on SMART drumlines, we have shown that most White Sharks move away from the capture site, thereby reducing the likelihood of shark-human interactions at that site. Furthermore, the survival of White Sharks highlights the efficacy of SMART drumlines as a potential non-lethal shark bite mitigation tool for a Threatened, Endangered, or Protected species.


This cohort study examines the frequency of postdischarge follow-up visits among US emergency department encounters for bronchiolitis and assesses whether follow-up was associated with decreased hospital reutilization or increased treatment with nonrecommended medications. Follow-up visits offer an opportunity to reassess the clinical trajectory and to provide reassurance to families, but they also may result in financial costs, missed work, and overtreatment.2 Although anecdotal evidence suggests that recommending postdischarge follow-up is the default practice for many ED clinicians, to our knowledge, national practice patterns have not been described. Our objectives were to describe the frequency of postdischarge follow-up visits in a national cohort of ED encounters for bronchiolitis and to assess whether follow-up was associated with decreased hospital reutilization or increased treatment with nonrecommended medications.Although the value of routine posthospitalization follow-up visits for otherwise healthy children with acute bronchiolitis has been questioned,3 The database includes insurance claims for more than 30 million employees and their dependents in the United States. We excluded children with complex chronic conditions.4 The Harvard Pilgrim Health Care Institute institutional review board approved this study. The need for informed consent was waived because data were obtained in a deidentified format. We followed the STROBE reporting guideline.This was a retrospective cohort study of ED discharges between 2018 and 2021 among children with commercial insurance aged less than 2 years with acute bronchiolitis in the MarketScan Commercial Claims and Encounters Database.5 Administrative codes are listed in the eAppendix in The primary outcome was a follow-up visit in primary care within 7 days of ED discharge. The secondary outcomes were ED revisits without hospitalization, hospitalization, and dispensation of medications not recommended by national guidelines .P < .05. Analyses were performed using R version 3.2.1  from December 2022 to July 2023.We used Poisson regression with robust clustered SEs to identify characteristics associated with attending follow-up. We clustered on individuals to account for nonindependence of observations among children with more than 1 illness episode. To assess the association between follow-up and subsequent hospital reutilization, we used multivariable Cox proportional hazard models. These models included follow-up as a time-varying exposure and adjusted for year, age, sex, region, weekend discharge, number of outpatient and ED visits in the prior year, whether there was an outpatient visit for bronchiolitis in the 7 days prior to the index ED visit, and testing  and treatments  at the index visit. Statistical significance was considered at a 2-sided P < .001). Several demographic and clinical characteristics were associated with follow-up (Among 28 338 ED discharges (27 000 unique patients), 63.3%  were less than 1 year of age and 58.8%  were male. Follow-up within 7 days occurred for 47.6%  of discharges. The proportion of discharges with follow-up decreased from 48.9%  in 2018 to 43.4%  in 2021  of discharges had an ED revisit and 5.2%  were subsequently hospitalized. Follow-up was not associated with ED revisits  but was positively associated with subsequent hospitalization . Prescriptions for albuterol, corticosteroids, and antibiotics were filled more frequently among children who attended follow-up . Among c6 Nonrecommended medications were dispensed for a modest proportion of children after follow-up. Limitations of this study included incomplete data to adjust for clinical severity, an inability to distinguish scheduled from unscheduled follow-up visits, and inclusion of only patients with commercial insurance. Given that follow-up visits are common, vary in frequency according to visit characteristics, have uncertain benefits, may result in overtreatment, and carry costs to families and the health care system, prospective investigation is warranted to determine which children with bronchiolitis may benefit from routine follow-up.Approximately half of commercially insured children with acute bronchiolitis completed a follow-up visit within 7 days of ED discharge. Follow-up visits were not associated with ED revisits. The association between follow-up and hospitalization could be explained by confounding by indication  or by overdiagnosis at follow-up.