Open-angle glaucoma (OAG) is a collective term for various subgroups of glaucoma of which primary open-angle glaucoma (POAG) and pseudoexfoliation glaucoma (PEG) are the most common. There is increasing evidence that both conditions have systemic ramifications. We wanted to examine to what extent lifespan and cause of death are influenced by POAG, pseudoexfoliation syndrome (PES), and PEG.Of 1864 people who underwent an eye examination in 1985–86, the presence of PES and/or glaucoma, along with date and cause of death were recorded. Based on information from the National Death Registry, the individuals were classified into the following groups of systemic diseases regarded as causing death: Cardiovascular disease (with two subgroups), cerebrovascular disease and neoplasms.All 1864 persons were followed to death, up to 30 years after examination. No difference in lifespan was observed when comparing OAG (i.e. POAG and PEG together) with the rest of the population. When adjusting for gender and age at inclusion, patients with POAG showed a reduced lifespan in the cardiovascular death group . When comparing lifespan in the neoplastic group in the glaucoma patients, POAG and PEG, directly against each other, a mean age difference of 6.87 years (p = 0.017) was found.POAG patients showed reduced lifespan due to neoplasia and cardiovascular disease. Persons with PES and PEG did not show these lifespan reductions. Our main conclusion is that POAG and PEG, the two main OAG subgroups, are very different disease entities both from an ocular and a systemic point of view. Primary open-angle glaucoma, pseudoexfoliation glaucoma, pseudoexfoliation syndrome, lifespan, systemic diseases It iTo what extent POAG and PEG share pathogenetic mechanisms is still open for debate. POAG eyes have no visible abnormality in the anterior chamber angle by gonioscopy. Both subgroups show abnormal extracellular material in the trabecular meshwork. However, the aberrant matrix in the two conditions reveals marked morphological differences: The amount of sheath-derived material  is significantly greater in the trabecular area of POAG compared to PEG , whereasRegarding systemic associations with POAG, a major review concluded that this condition is not just a process involving the visual system, but more likely the manifestation of generalized systemic dysfunction . More spIn contrast to patients with POAG, people with PEG (and PES) show morphological changes in vascular and connective tissues throughout the body , 14, 15.Thus, the possible systemic associations of POAG and PES are still unclear and it is reasonable to conclude that a mortality and/or lifespan change in these diseases cannot be excluded. Therefore, the aim of the present study is to evaluate the impact on lifespan and cause of death in patients suffering from POAG and PES.2This study is based on data from an epidemiological survey conducted in the three municipalities Hitra (Hi), Holtålen (Ho), Rennebu (Re) in the county of Sør-Trøndelag, Norway in 1985–86 . All inhA temporary test center was established in each municipality and screening was performed by an ophthalmologist according to the following schedule: Intraocular pressure (IOP) was measured with a Goldmann applanation device after 0.5% proxymetacaine anesthesia, followed by pupil dilation with 0.5% tropicamide. Subsequently, conventional slit-lamp examination was performed to check for PE-material and optic disc excavation, including vertical cup/disc-ratio judged by direct ophthalmoscopy. Due to various ocular conditions  43 persons had only one eye examined. The presence of PES, PEG, and POAG are all irreversible diagnoses once they have been confirmed.Individuals with IOP ≥25 mmHg and/or glaucomatous cupping were referred to the University Eye Department in Trondheim for registration of visual acuity, refraction, IOP, and visual field examination . Some patients, already under glaucomatous treatment, preferred to attend their own ophthalmologist, from whom the information was collected. These cases had also visual field registration, though with methods differing from the hospital-based set-up.A person was classified as having glaucoma when 2 of the following 3 criteria were present : 1) IOP Total = all cases with PES regardless of the presence of glaucoma or not (n = 317)PESAlone = PES cases without glaucoma (n = 228)PESPEG = pseudoexfoliation cases with glaucoma (n = 89)POAG = glaucoma without pseudoexfoliation (n = 63)OAG = POAG and PEG combined (n = 152)The following abbreviations have been used:Ocular hypertension (OHT) was defined as IOP ≥25 mm Hg in otherwise normal eyes, a definition not uncommon at that time. Persons showing PES in one or both eyes were registered as PES-positive. If only one eye could be evaluated, and found PES-negative, the case was grouped as undefined. Calculations on POAG and PEG did not include OHT, angle-closure glaucoma, neovascular glaucoma, and uveitic glaucoma. Glaucoma in one eye only was grouped as a glaucoma case.Following approval from the Regional Ethical Committee, mortality information, including death date and cause(s) of death, was obtained from The Norwegian Cause of Death Registry. Every death in Norway is reviewed by a medical doctor who examines the deceased person and fills in a death certificate with compulsory diagnostic coding. Neoplasia cases are routinely examined with either biopsy or autopsy. Autopsy is otherwise performed in the minority of cases, usually when death is unexpected, or the cause is uncertain. As of 01.01.16, of the 1888 initially examined individuals, 13 were still alive, and 11 persons were recorded as deceased without any diagnostic coding. Thus, 1864 participants (98.7%) remained for analysis.The cause of death was coded according to WHOs International Statistical Classification of Diseases and Related Health Problems (ICD) version 8 (used 1969–85), 9 (used 1986–95), and 10 (used from 1996). 574 persons (30.1%) had a single diagnosis indicating cause of death, and the remaining 1290 persons had one diagnosis indicating the primary cause of death, and in addition one or more (up to a maximum of 9) diagnoses that were considered contributing to death. Based on the ICD-coding, the deceased were classified in the following groups according to the cause of death: Cardiovascular disease (further divided into two subgroups), cerebrovascular disease, and neoplasms. Lifespan in various subgroups are compared with lifespan in the remaining population.2.1In order to compare mean lifespan in two subgroups, and adjust for age at inclusion and gender, linear regression analysis was performed with lifespan as the dependent variable, and age at inclusion, gender and cause of death as independent variables. A significance level of 5% was used. The statistical analysis was performed using IBM-SPSS version 20 software.3Total prevalence in the three municipalities Hi, Ho, and Re was 10.2 %, 21.0 %, and 19.6 %, respectively. The municipalities Ho + Re showed very similar prevalence, and accordingly, the possible impact of PES per se on lifespan could therefore be estimated by comparing numbers from Hi versus Ho + Re: Lifespan in Hi was 85.6 (SD 6.7) years, versus 85.7 (SD 6.7) years in Ho + Re (p = 0.714). There was no difference in sex distribution between the two groups (p = 0.639).PESTotal, and PEG, respectively. Of the 1864  deceased with registered cause of death, 17.0 % showed ocular PES at examination in 1985–86, against 17.9 % in the neoplasm group (397 persons). In total, 28.1 % of the PESTotal cases were recorded with PEG at baseline. 152 persons (8.15 %) had glaucoma in the total population. Additional core information is presented in With respect to glaucoma, the data was analyzed for differences in lifespan and cause of death in different categories in PES-negative persons versus POAG, PES3.1Total, PESAlone, and PEG individually, differences were seen. The cardiovascular death group showed reduced lifespan in the POAG patients  acute myocardial infarction and b) other cardiovascular disease, cerebrovascular disease, and neoplasms. Due to low numbers, other conditions  have not been analyzed. No change in lifespan was observed in any of the categories when testing OAG (i.e. POAG and PEG together) against the rest of the population within each single category . HoweverTotal showed mean differences of 4.99 years  and there was minimal dropout. Thus, these observations are not restricted to a limited group with early death, but rather represents the actual lifespan in the total population. Furthermore, apart from presence or absence of PES, the same definition of glaucoma has been used for both PEG and POAG , which cIn a previous study we showed that lifespan is not statistically different in persons with or without PES in the same cohort . This isThe relationship between OAG and mortality is still a matter of controversy. Increased mortality due to cardiovascular disease has been reported in OAG patients . However4.1When dividing OAG into the subgroups POAG and PEG , a consi4.2Total group versus the control in cardiovascular disease  lifespan of PEG patients is normal in the neoplasia category, 2) POAG patients have reduced lifespan due to neoplasia, 3) in addition, there is a significant lifespan reduction in POAG patients due to cardiovascular disease. The last phenomenon is shown in When comparing the lifespan of POAG and PEG groups, remarkable differences are observed in all categories . The mosA shortcoming of our study is the fact that the ocular status was established in 1985–86, whereas the death diagnoses were recorded during the following 30 years. As the prevalence of POAG, PES, and PEG all increase with age, their numbers are underestimated when compared to rates at death. However, as they all are underestimated, this bias is at least minimized when mutual comparisons are performed. It should also be noted that, unlike with the neoplasm group that was confirmed with autopsy or biopsy, there is some uncertainty linked to the other death diagnoses as these were given by a medical doctor on site.In conclusion, patients who have PEG had normal lifespans, whilst patients with POAG had a reduced lifespan. A statistically significant lifespan difference was observed between POAG and PEG in the neoplasm death group, with POAG patients’ lifespan being shorter by 4.99 years. A possible explanation could be that neoplasia reduces lifespan in general, except for patients with PES perhaps suggesting a protective element. In addition, POAG patients showed reduced lifespan in the cardiovascular category. Consequently, POAG and PEG, the two OAG subgroups, are very different disease entities both from an ocular and a systemic point of view.Jon Klokk Slettedal and Amund Ringvold: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Leiv Sandvik: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.Norwegian Society of the Blind and Partially Sighted and the Raagholt Research Foundation.This work was supported by The Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Moxibustion is a common intervention of Chinese medicine (CM). Systematic reviews (SRs) on moxibustion are increasing. Although the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement provides guidelines for SRs, the quality of moxibustion-related SRs is still not satisfactory. In particular, descriptions of the interventions and the rationale for using moxibustion are insufficient. To address these inadequacies, the working group developed this PRISMA extension for reporting SRs of moxibustion (PRISMA-M 2020).A group of CM clinical professionals, methodologists of SRs, reporting guideline developers, and journal editors developed this PRISMA-M 2020 through a comprehensive process that includes registration, literature review, consensus meetings, Delphi exercises for soliciting comments, and revision, resulting in this final draft.Seven of the 27 PRISMA checklist items, namely title (1), rationale (3), eligibility criteria (6), data item (11), additional analyses (16), study characteristics (18), and additional analysis (23), were extended, with specific reference to the application of moxibustion. Illustrative examples and explanations for each item are provided.The PRISMA-M 2020 will help improve the reporting quality of SRs with moxibustion.http://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-systematic-reviews/#65.We have registered it on the EQUATOR  network, particularly under the item of PRISMA-TCM: The online version contains supplementary material available at 10.1186/s13643-020-01502-7. Moxibustion is a traditional therapeutic technique in which hot or burning moxa is applied on acupoints or meridians . The earWith more and more randomized controlled trials (RCTs) and systematic reviews (SRs) of moxibustion published every year, the importance of improving the reporting quality of moxibustion studies has been highlighted by both researchers and users of moxibustion evidence –11. In 2Recently, the quality of reporting of SRs with various CM interventions has raised concern, and relevant guidelines have been developed, such as PRISMA for acupuncture  and PRISGiven this situation, we suspected that the practices of moxibustion were not being adequately reported, despite PRISMA guidelines. Aiming to determine if this were true, we conducted a preliminary literature review of 97 moxibustion SRs from 2011 to 2019. In the results, 84.5% (82/97) SRs studied CM-based moxibustion interventions and published the results in Chinese journals. For the reporting of intervention technique, 69.1% (67/97) SRs did not provide the specific type of moxibustion; 39.2% (38/97) lacked details regarding the materials, procedure, and technique used for moxibustion; 67.0% (65/97) did not fully report the selection of acupoints for moxibustion; and 28.9% (28/97) did not provide the number or duration of treatment sessions . Given sThe PRISMA-M 2020 is based on the PRISMA statement. The methodology framework recommended by the EQUATOR Network was used in developing it , in the This PRISMA-M was included in the registered item of “PRISMA-TCM” on the EQUATOR Network, as moxibustion is a common and important type of traditional Chinese medicine .Authors XZ, S-YL, Q-YA, and RT conducted a literature review to assess the reporting quality of moxibustion SRs and continued to update the included articles until 31 December 2019  reviewed all potential items and formulated a reporting checklist on 15 June 2017.A total of eleven professionals, including five senior TCM practitioners, three evidence-based medicine and clinical trial methodology experts, two reporting guideline developers, and one epidemiologist, were invited to attend a face-to-face meeting in Lanzhou, China, on 19 July 2017. During the meeting, the aim and scope of the guideline with a drafted checklist were presented to the participants, followed by a discussion and revision of each proposed item. Finally, a checklist questionnaire was formed for a Delphi survey for further solicitation of comments.Three rounds of an e-mail-based Delphi survey were conducted from November 2017 to April 2018. Thirty-five experts with rich experience in moxibustion practice, performing SRs, and developing reporting guidelines were invited. The invitation letters, which included an introduction to the study, workflow of Delphi surveys, and relevant questionnaire, were sent out; positive responses confirmed their participation. Each of the 35 individuals completed at least two rounds of the Delphi survey. The questionnaires of each round were sent to the participants from a specific email address, which was managed by one member (RT) of the working group. Anonymity and confidentiality of responses were ensured.N5 + 0.75 × N4 + 0.5 × N3 + 0.25 × N2)/(N5 + N4 + N3 + N2 + N1), where Ni represents the number of respondents who chose specific i in the scale of “1 to 5.” Items with a score greater than or equal to 75%, namely reach a consensus, were included [During the Delphi process, participants were asked to rate each item on a scale of 1 (not important) to 5 (very important), to suggest new items, and to provide comments; any items that did not reach consensus  and any new items were circulated in subsequent rounds. Following each round, the score for each item was calculated with the formula 100% × (1 × included . This caincluded . The anaExplanation and elaboration (E&E) documents were prepared for each included item. Examples of good reporting were identified and edited for inclusion . Explanation documents were also developed to provide the rationale and describe the characteristics of moxibustion SRs.The manuscript of this guideline was drafted by authors ZX-B and XZ. The working group members reviewed and provided revisions. The draft was also presented by team leaders in academic workshops and conferences to solicit broad comments for improvement . The man2. There is no modification of the PRISMA flow diagram.PRISMA-M 2020 expands sections of PRISMA to ensure that the unique characteristics of moxibustion are adequately reported in SRs, so as to ensure that the data and results are accurate and complete, and that the study is reproducible. Also, the key concept of CM pattern Table  is highlThe aim of PRISMA-M 2020 is to optimize the reporting of SRs focusing on moxibustion interventions for specific conditions and/or patterns (if any). We consider most of the PRISMA items relevant in reporting SRs assessing the benefits and harms of specific moxibustion treatments. Further, we emphasized the concept of CM pattern (if applicable) since the moxibustion prescription in CM clinical practice can be determined on pattern identification. However, we recognize that authors who address the research question without pattern contents and/or no pattern-related indicators in the primary studies may not need to focus on pattern-related extension items for the SRs. To maximize the clarity of this checklist, examples and explanations are provided for each item. For certain thematic concepts , a comprehensive description is presented in Table PRISMA item: Identify the report as a systematic review, meta-analysis, or both.Extension: 1a. Statement of the specific type of moxibustion treatment, such as direct moxibustion or heat-sensitive moxibustion. 1b. Statement of whether the review targets the (1) Western medicine–defined disease(s), or (2) Western medicine–defined disease(s) with specific CM pattern(s), or (3) CM pattern(s), if applicable.Explanation. For SRs, a self-explanatory title including the PICOS  can make vital information easily accessible to readers. If the review investigates the effect of a specific type of moxibustion on a particular condition, it should be stated in the title. There are different types of moxibustion regarding the materials used and the details of procedures, such as moxa cone moxibustion, warming needle moxibustion, natural moxibustion, and heat-sensitive moxibustion [ibustion . Authorsibustion , 35. If Whether the moxibustion targets a Western medicine–defined disease and/or specific pattern(s) should be clarified in the title. Readers can then easily understand which conditions are studied in the SR, and the results will be more easily applied in clinical practice. In terms of reporting the pattern names, authors are encouraged to provide the specific name if one type of pattern that will be targeted, such as “primary dysmenorrhea with cold coagulation and blood stasis syndrome”  and “proPRISMA item: Describe the rationale for the review in the context of what is already known.Extension: Describe the rationale for what is already known about moxibustion utilized for the target disease and/or CM pattern (if any). If applicable, relevant theory of CM should be included.Explanation. CM has long been thought to be a kind of personalized medicine, and its unique characteristic is pattern differentiation [ntiation , 31. In ntiation . For exantiation . TherefoPRISMA item: Specify study characteristics  and report characteristics  used as criteria for eligibility, giving rationale.Extension: 6a. Describe the diagnostic criteria of the target condition in Western medicine and/or CM pattern (if any). All criteria utilized should be universally recognized, or reference(s) where detailed explanation can be found should be given. 6b. Specify the types of moxibustion to be included, such as moxa burner moxibustion, natural moxibustion, or heat-sensitive moxibustion. 6c. State whether CM-related outcome(s) were included, such as the change of degree and scope of symptoms and signs related to CM pattern, or validated pattern survey, if applicable.Explanation. Diagnostic criteria of disease(s)/pattern(s) are important to clarify the scope of target populations in the review. How the disease and/or pattern (if any) was diagnosed and what criteria were used for identifying participants should be comprehensively described. In addition, because moxibustion can be used both to treat disease and to improve health, the condition diagnosed should be clearly identified as either a disease or simply a less-than-optimum state of health. Generally, authors should choose nationally or internationally recognized diagnostic criteria and then include the eligible primary studies. However, there may be different diagnostic criteria for one pattern as established by different original trials [l trials . Thus, sThe effects of different types of moxibustion can differ. Because of this, authors should pre-define the inclusion criteria for the studied intervention. If a specific type of moxibustion will be performed, it is suggested that authors should report eligibility criteria with standard terms and definitions (if necessary), materials, procedures, and techniques. If a broad type of moxibustion will be included, authors should pre-list its scope in as much detail as possible. Additionally, moxibustion is sometimes combined with other therapies, such as acupuncture, herbs, or cupping. Thus, where applicable, authors of SRs should pre-define the selection scope and criteria for complex interventions using information from original trials.For clinical trials testing the efficacy of moxibustion, the outcomes can usually be categorized into Western medicine–specific outcomes and CM-specific outcomes. Therefore, if an SR will include CM-related outcome(s) as eligibility criteria, the requirements for measurements and time points should be specified, because one pattern-outcome may have been assessed with different measurements in different studies .PRISMA item: List and define all variables for which data were sought  and any assumptions and simplifications made.Extension: 11a. List and define the data of CM pattern(s) in detail, considering the inclusion and exclusion criteria, if applicable. 11b. List and define the data of moxibustion interventions and controls , give details referring to STRICTOM and TIDieR. 11c. List and define the data of CM pattern outcome(s), considering the methods and timepoints, if applicable.Explanation. If the CM patterns were involved in selecting participants, information on how the pattern was diagnosed and what criteria were used for inclusion and exclusion should be reported in detail. If applicable, other information, such as numbers in different patterns groups, should be included in the data. If pattern-related outcomes are assessed, it is recommended to use symptoms and signs that can be measured objectively, such as in terms of occurrence , by a rating scale , or an assessment questionnaire  [ survey) , 44. In For moxibustion, the effect is associated with several important details, such as the materials and techniques used, the acupoints and meridians selected, and the number, frequency, and duration of treatment; all of these factors can vary between diseases and/or patterns. If sham moxibustion is included, descriptions on how to assess whether “sham” was achieved should be particularly reported . To ensuPRISMA item: Describe methods of additional analyses , if done, indicating which were pre-specified.Extension: Describe methods of subgroup analyses in terms of different types of included moxibustion interventions and/or included CM pattern participants (if applicable), if done, indicating which were pre-specified.Explanation. Whether subgroup analysis should be conducted generally depends on the review objective and the heterogeneity of interventions and participants. Due to the characteristics of moxibustion interventions and CM patterns (if any), we suggest that authors consider at least the following factors in subgroup analyses: (1) different types and/or amounts of moxibustion, (2) different treatment frequency and/or courses, (3) different phases of diseases and/or patterns (if any), and (4) different control groups. If one specific type of moxibustion is studied in an SR, more details of the factors  should be considered in heterogeneity analyses. Although the information may be inadequately reported in original studies, it is important for reviewers to inform readers whether the subgroup analyses were pre-specified and the rationale.PRISMA item: For each study, present characteristics for which data were extracted  and provide the citation.Extension: 18a. Present characteristics for the data of participants, which include CM pattern(s), considering (1) diagnostic criteria; (2) baseline data, if applicable. 18b. Present characteristics for the data of moxibustion intervention(s) and controls  for each study referring to STRICTOM and TIDieR. 18c. Present characteristics for the data of outcomes which include CM pattern(s), considering (1) name and measuring methods; (2) measuring timepoints and length of follow-up, if applicable.Explanation. Results should be presented referring to the pre-designed data items . If any data item is insufficiently reported and cannot be obtained by contacting authors of the included trials, then it should be described as “not reported” in the review. For moxibustion SRs, information about the CM pattern (if any) studied and about interventions should be completely provided in the results, as this is the basis for readers’ assessment of the validity of data synthesis in the SRs [ the SRs . It is hPRISMA item: Give results of additional analyses, if done .Extension: Give results of subgroup analyses based on the different types of moxibustioninterventions and participants with CM patterns (if any), if done.Explanation. If subgroup analyses are done, authors should give the results, even those without statistical significance, and they should state whether the items analyzed were pre-specified (see Items 16). Subgroup analyses are valuable because the different types of moxibustion and patterns (if any) are primary sources of heterogeneity across included studies [ studies ; howeverSRs of moxibustion are essential for evaluating the efficacy and safety of this unique from of medical intervention; they are expected to be accurate and reliable. With the aim of improving their reporting quality, this PRISMA for moxibustion extension was specifically developed for SRs studying CM-based moxibustion, taking into account the pattern concept on the basis of clinical practice. Other moxibustion types of traditional medicine , or non-pharmacological interventions that include moxibustion, could also use this as a basis for reporting. Generally, we expect that the main users of this guideline will be authors of SRs on moxibustion, journal editors, peer reviewers, methodologists, and clinical moxibustion practitioners.As with other extensions, the PRISMA-M 2020 checklist should be used together with the original PRISMA checklist. To facilitate this use, Table For better dissemination of PRISMA-M 2020, we will take the following specific steps: Firstly, we will continue to share it with clinical practitioners, researchers, peer reviewers, and journal editors through international conferences and seminars. Secondly, as we have publicly registered this guideline on the EQUATOR Network, all relevant results and publications will be updated in real time. Thirdly, we will contact relevant journals for endorsement as journals play an important role in the implementation of reporting guidelines. Finally, we will monitor the application and evaluate the effect of PRISMA-M 2020 continuously and, when necessary, update it according to users’ feedback and the latest evidence.Although guidelines do help improve the quality of reporting, there are some limitations to this extension. Firstly, although this checklist was completed through extensive solicitation of comments from CM clinicians, methodologists, journal editors, and epidemiologists, more than half of the experts were CM professionals, which may not be international enough for other traditional medicines. In future iterations of this guideline, we will optimize it by soliciting and incorporating comments from a broader group of experts from CAM societies. Secondly, in the development of PRISMA-M 2020, we did not include a large-scale user-based survey to test the practicality of each item. As the value of a guideline ultimately depends on its use, we will collect broad feedback from potential users  and update it accordingly. Despite these limitations, the PRISMA-M 2020 guideline has value as the first consensus-based reporting recommendations for SRs on moxibustion. We hope that these recommendations will promote better reporting and influence the methodology design of SRs on moxibustion.PRISMA-M 2020 was developed to help authors improve the reporting quality of SRs studying moxibustion interventions. The checklist can also be used to evaluate the current condition of reporting and to help journals identify moxibustion SRs of higher quality. Together, we believe this guideline will be a useful tool to promote the transparent reporting of SRs with moxibustion, thus to achieve its better use in clinical practice.Additional file 1. S1. Search strategy and flow chart of the literature review. S2. Available published examples of reporting moxibustion SRs.


Background: Colorectal cancer (CRC) is the third commonly diagnosed cancer worldwide. Recurrence of CRC (Re) and onset of a second primary malignancy (SPM) are important indicators in treating CRC, but it is often difficult to predict the onset of a SPM. Therefore, we used mechanical learning to identify risk factors that affect Re and SPM.Patient and Methods: CRC patients with cancer registry database at three medical centers were identified. All patients were classified based on Re or no recurrence (NRe) as well as SPM or no SPM (NSPM). Two classifiers, namely A Library for Support Vector Machines (LIBSVM) and Reduced Error Pruning Tree (REPTree), were applied to analyze the relationship between clinical features and Re and/or SPM category by constructing optimized models.Results: When Re and SPM were evaluated separately, the accuracy of LIBSVM was 0.878 and that of REPTree was 0.622. When Re and SPM were evaluated in combination, the precision of models for SPM+Re, NSPM+Re, SPM+NRe, and NSPM+NRe was 0.878, 0.662, 0.774, and 0.778, respectively.Conclusions: Machine learning can be used to rank factors affecting tumor Re and SPM. In clinical practice, routine checkups are necessary to ensure early detection of new tumors. The success of prediction and early detection may be enhanced in the future by applying “big data” analysis methods such as machine learning. Colorectal cancer (CRC) is the third most common cancer in the world and the fourth most common cause of death overall Recurrence and second primary malignancies (SPMs) affect the survival of CRC patients. Numerous studies have documented increases in the incidence and recurrence of CRC as well as increases in second primary cancers, including lung, head, neck, and gastric cancer t-test, have been used, but this requires a rigorous research design and clear, explicit hypotheses. In addition, the calculations cannot be modified when parameters change without redesign.Early detection of tumor recurrence and SPMs is essential to improving health outcomes of cancer patients. To detect SPMs, valid and reliable prediction tools are necessary. Traditional statistical methods, such as the chi-square test, multiple linear regression test, ANOVA, and n-dimensional space for a given set of biological samples using different techniques and algorithms In recent years, machine learning (ML) has emerged as an alternative to express parameters in disease treatment and outcome. ML is a process where an acceptable generalization is obtained by searching through an Our study used data accessed from the Cancer Registry in three medical centers. Individuals with ICD-09 codes 153~154 who were diagnosed with CRC between 2004 and 2012 and were 18 years or older at the time of diagnosis were selected for inclusion in the study. We used the cancer sequence number and the recurrence status of primary CRC as the target classification. Patients with CRC alone were selected first and identified using sequence number 1. Patients with an SPM in addition to primary CRC were identified using sequence number 2 and included in the analysis. All CRC patients were classified between 2004 and 2012 and the study is conducted as a time to event analysis.A total of 4299 patients with primary CRC were enrolled. As shown in Figure Risk factors previously reported to be associated with CRC tumor recurrence and SPM include tumor size, morphology, differentiation Support Vector Machines (SVMs) are supervised learning algorithms that can be used in binary classification problems and have been applied in many fields. SVMs maps feature vectors to a high-dimensional feature space, which classifies samples by searching for an optimal hyperplane and can divide the samples into different spatial areas. We uses LIBSVM (A Library for Support Vector Machines)Reduced Error Pruning Tree (REPTree) uses regression tree logic and creates multiple trees in different iterations. After this process, the optimal (or representative) decision from all generated trees were identified. The mean squared error of the prediction was used to prune the tree. REPTree offers a fast decision tree learning method and builds a decision/regression tree based on information gain or by minimizing the variance. The information gain was used as the splitting criterion and the reduce-error pruning method was also applied to reduce the size of the decision trees. Values were sorted once for numeric attributes, and then, to improve accuracy, all subtrees were visited in a bottom-up manner until no subtrees were replaced with leaves.xk, where k = 1,…,m, if the number of positive and negative instances are n+ and n-, respectively, the F-score of the ith feature is defined as:We applied the feature selection tool developed by the LIBSVM (A Library for Support Vector Machines), , are the average of the ith feature of the whole, positive, and negative data sets, respectively;  is the ith feature of the kth positive instance; and is the ith feature of the kth negative instance. The larger the F-score, the more likely this feature is discriminative. This score was used as a feature selection criterion.where After selecting patients for inclusion and dividing them into the four classification groups, the significant ranking of the 20 features was analyzed using the LIBSVM feature selection mode of the training dataset. Then, based on the performance of the predictive model, we selected candidate features that could assist in classification of recurrence and SPM. The LIBSVM algorithm was used as the initial classifier for model construction. Although it is easy to achieve the best accuracy using LIBSVM, some SVM algorithms are complicated and difficult to understand. Therefore, Reduced Error Pruning Tree (REPTree) was applied to provide possible rules for auxiliary analysis. In the study, we choose The predictive ability of each system was evaluated for accuracy (Acc), sensitivity (Sn), specificity (Sp), and the Matthews correlation coefficient (MCC), which were defined as follows:Where TP, FP, FN, and TN represent true and false positives and negatives. Acc evaluates the prediction accuracy of positive and negative data, whereas Sn and Sp evaluate the accuracy of the prediction of positive and negative data, respectively. The values of these parameters range from 0 to 1. MCC is suitable for assessing the dataset for imbalance between positive and negative data and ranges from -1 to 1. The model performance is better when MCC is closer 1 and vice versa.In this study, we identified 1989 out of 4299 total cases that had recurrent CRC (the Re group) and 2310 cases with no recurrence (the NRe group). Model evaluation data are shown in Table In addition, we identified 541 cases with SPM and 3758 cases without SPM (NSPM). Because using two datasets with drastically different sizes (SPM/NSPM ratio = 1:6.95) can affect data quality and hinder models training process, we constructed models using three different ratios  and compared their accuracy. Our results are shown in Table To generate models using ratios of 1:1, the SPM patients were combined with 541 randomly selected patients from NSPM to form training dataset. Under the condition, the MCC of the LIBSVM model was 0.324 and the Sp and Sn were both greater than 0.6. The LIBSVM_F model, employing only the top eight features , had the equivalent performance outcome as the model using all 20 features.Applying the REPTree algorithm with the 1:1 ratio gave an MCC of 0.282. When an optimized model (REPTree_OP) was constructed using only the top three features , the MCC increased to 0.294 and the Sn increased to 0.706. In the REPTree_OP decision tree in Figure Lastly, we considered the two results of recurrent CRC and SPM together using the four conditions SPM+Re, SPM+NRe, NSPM+Re, and NSPM+NRe. Data are presented in Table or surgical margins ≥2 and patient age ≥83 can be classified into SPM+Re  and combined these with the 208 SPM+Re (positive) samples into the training dataset. When only the top four factors  were included in the analysis (LIBSVM_F), the MCC was 0.466 and the Acc was 0.732. In the REPTree model, the MCC, Acc, and Sn were 0.448, 0.722, and 0.774, respectively. This was the same as the SVM model applying only the three features of surgical margins, organizational patterns, and patient age. The decision tree shows that rules of surgical margins ≥2, patient age <83, and organizational patterns <2 e Figure .We randomly selected a total of 1781 samples from three remaining classes as negative data and combined them with the 1781 NSPM+Re (positive) samples into the training dataset. When four major factors  were selected for analysis, the MCC of the LIBSVM_F model was 0.676 and the Acc was 0.836. Using the same four factors in the REPTree_F model gave similar values for MCC and Acc. Both models also achieved a sensitivity above 0.88. The rules of the decision tree for REPTree_F are shown in Figure The 333 samples from SPM+NRe class (positive data) and 111 samples randomly selected from each the other three classes (negative data) were combined into the training dataset. In the LIBSVM_F model using the top three features , the MCC was 0.494 and the Sn was 0.679. For the REPTree model, the MCC was 0.446 and the Sn was 0.757. When surgical margins, pStage, tumor size, behavior code, patient age, and smoking are used in the REPTree model after parameter optimization (REPTree_OP), the MCC was 0.504 and the Sn was 0.823 . Compared to the SVM model with three features, the REPTree_OP model was similar in MCC and had a higher Sn (by 0.144), but had a lower Sp (by 0.135). The rules of the decision tree for REPTree_OP are shown in Figure The 1977 samples (positive data) and were combined with 1977 randomly selected samples from other three classes as negative data for the training data. The top four features were pStage, surgical margins, differentiation, and tumor size. The LIBSVM model using all features had an MCC of 0.592. Using only pStage and surgical margins in the model improved performance of MCC (0.613), Sn 0.862), and Sp (0.746). In REPTree, the MCC increased from 0.612 before parameter optimization to 0.630 after parameter optimization using pStage, surgical margins, differentiation, and areca. The performance of REPTree was better than that of SVM. The rules of the decision tree for REPTree_OP are shown in Figure 62, and SOur study explored risk factors for predicting CRC recurrence and SPM and discovered that the four most important factors were pStage, surgical margins, smoking, and drinking. However, sensitivity (Sn) decreased slightly when drinking was removed from the analysis and decreased further when smoking was removed. These findings suggest that both drinking and smoking have an effect on recurrence and SPM. On the other hand, both surgical margins and pStage were significant factors using both SVM and REPTree models for classification.In classification for SPM, when eight features  were selected to construct the REPTree model, the MCC  outperformed the REPTree model without feature selection (MCC = 0.282, Table For all four groups in the combined analysis of recurrence and SPM, pathologic stage and surgical margins were included in all models. This highlights the clinical importance of these two factors. On the other hand, primary site was the last feature to be included in all models, indicating that it has little clinical or reference value Table .Although our results show that selected features can be weighted using mechanical learning to find predicting factors for recurrence and SPM, there were some limitations to the study. Some site-specific factors, such as tumor markers and tumor regression grade, were not included as risk factors. Including these and other factors in machine learning programs may improve the prediction and early detection of recurrence and SPM.There are some limitations in the study. Risk factors such as family history of breast cancer, Hereditary Nonpolyposis Colorectal Cancer cannot be available. This may affect the result of SPM. The will be enrolled in further study.With mechanical learning programs, we have developed a feasible and a robust method to identify factors that are important for predicting recurrence of colorectal cancer and SPM. The four most important factors are pStage, surgical margin, smoking, and drinking. Mechanical learning can be used as an effective medical decision-making tool to improve prognostic and diagnostic accuracy in clinical settings. We strongly recommend that clinicians consider using mechanical learning in diagnosing and treating cancer patients to provide high-quality care.


Rapid on-site evaluation (ROSE) is a kind of rapid evaluation of specimen satisfaction, preliminary diagnosis and priority strategy, the diagnostic accuracy of ROSE in the field of pulmonary intervention shows wide variation. The aim of the study was to further clarify the accuracy and diagnostic efficacy of ROSE in interventional pulmonology.This review summarizes and meta-analyzes studies of ROSE in interventional pulmonology, the ROSE diagnoses would be compared with the final pathologic diagnoses. The following electronic databases have been searched: PubMed, Cochrane Library, Embase, Web of science, CNKI, and WANFANG DATA. The methodologic quality of studies has been assessed using the Quality of Diagnostic Studies (QUADAS-2) instrument. This review is conducted using standard methods for systematic reviews of diagnostic accuracy studies. STATA SE 12.0 is used for data synthesis and analysis.This review evaluates the accuracy and diagnostic efficacy of ROSE in interventional pulmonology, and the process factors that may influence the ROSE diagnosis are analyzed, such as Smear method, profession of smear technician, staining method, Profession of stain technician, Profession of reading slides, invasive procedure, Anesthesia method and etc.This review will stimulate proper evaluation of ROSE and provide assistance for clinical practice. In theory, the use of ROSE can improve the diagnostic efficiency of interventional respiratory disease, reduce the operation time and the number of biopsy samples, and then reduce the occurrence of complications in interventional operation. However, ROSE has not been widely used in practice. Some observational Researches have shown that ROSE increases the yield.In order to further clarify the accuracy and diagnostic efficacy of ROSE in interventional pulmonology, we conducted a systematic review and meta-analysis to compare ROSE with the final pathological results, extracted, and analyzed the factors that may affect the results of ROSE during the procedure.2http://www.crd.york.ac.uk/PROSPERO), registration number: No.CRD42020145807. The procedure of this protocol is based on PRISMA-DTA guidelines.This study has been registered in PROSPERO  were searched by combine keyword searches and thesaurus terms or subject headings as following: (lung OR pulmonary) AND  AND .The retrieval strategy for PubMed is as follows: ((((lung[Title/Abstract]) OR pulmonary[Title/Abstract])) AND  OR sensibility[Title/Abstract]) OR specificity[Title/Abstract]) OR negative predictive value[Title/Abstract]) OR positive predictive value[Title/Abstract])) AND (((ROSE[Title/Abstract]) OR rapid on-site evaluation[Title/Abstract]) OR rapid onsite cytological evaluation[Title/Abstract]).2.2Studies meeting the following criteria were included:1.The type of researches retrieved were not limited except case-control studies, case reports, abstracts, reviews, editorials, reviews, etc.2.The subjects were required to sample the lesions through diagnostic interventional procedures without restrictions on auxiliary sampling equipment or technology in the process, such as radial endobronchial ultrasound (R-EBUS), fine-needle aspiration (FNA), transbronchial biopsy (TBB), Endobronchial ultrasound with a guide sheath (EBUS-GS), EBUS-TBNA, TBNA and etc. And ROSE should be used as an auxiliary intervention.3.The results of each literature study can be listed in the form of 2 × 2 table, which shows the number of people who are divided into positive and negative according to ROSE diagnosis and final pathological diagnosis.If the data cannot be extracted completely, the literature will not be included in the study. The selection process was showed in Figure 2.3Two researchers used Endnote literature management software to screen the included research according to the title and abstract content of the study. After screening, the literature was evaluated according to the inclusion criteria, we selected studies that met the requirements, and extracted and analyzed the data. The problems in the process of literature screening need to be discussed and solved by the 2 researchers. The following information was retrieved:1.The basic characteristics of the literature: the first author, the country, the type of literature, the time of publication, the blind method, the gold standard, the number of research cases.2.Main end point: the number of people who were divided into positive and negative according to ROSE diagnosis and final pathological diagnosis were extracted in the form of 2 × 2 table.3.The factors that might have an impact on the main end point during the procedure: smear method, profession of smear technician, staining method, profession of stain technician, Profession of reading slides, invasive procedure, anesthesia method, sampling time (minutes), quantities of samples.The 2 researchers eventually resolved any differences in the data extraction process through discussions.2.4 The instrument consists of 4 components: Patient Selection, Index text (s), Reference standard, Flow and timing. Two researchers will independently evaluate each study for risk of bias and applicability. “L”, “H”, and “U” have been used as a code for the evaluations of the above bias risks.“L” indicates a low risk of bias, “H” indicates a high risk of bias, and “U” indicates the risk of bias is unclear. Disagreements resolved by discussion between all the researchers. When necessary, the study authors have been contacted to inquire some missing information. Trials of high risk of bias will be considered when conducting sensitive analysis.Quality was evaluated using the Quality of Diagnostic Studies (QUADAS-2) instrument.2.5I2 statistic. We considered an I2 value greater than 50% indicative of substantial heterogeneity. If we detect heterogeneity, Meta-regression analyses may be done to investigate the causes of heterogeneity, the sensitivity analyses would be conducted based on the study quality. In addition, Publication bias was analyzed through Deeks funnel plot test in midas. The whole statistical analyses were performed by Stata SE 12.0 , P values <.05 will be considered to be statistically significant.For this diagnostic study, the estimates such as sensitivities, specificities were pooled by using a random-effects model. Heterogeneity between the studies was assessed using both the Q test and the 3At present, ROSE technique has been widely used in the field of respiratory intervention, such as the pathological diagnosis of hilar and mediastinal lesions, peripheral pulmonary lesions, and Pleural lesions, but its clinical value and significance are not clear and controversial. In 2015, Cancer letters reported that 236 patients with lung cancer complicated with hilar and mediastinal lymph nodes were divided into 2 groups: ROSE group  and non-ROSE group . EBUS-TBNA was performed, and the diagnostic efficacy of the 2 groups was compared with the undiagnosed rate. The results showed that the undiagnosed rates of cytology and histology in the ROSE group were lower than those in the non-ROSE group, and the undiagnosed rates were 8.7% vs 14.6%, P = .038; 0.9% vs 4.4%, P = .018. This study suggests that the use of ROSE technology in EBUS-TBNA can reduce the rate of invalid samples and help to obtain positive diagnosis. Rokadia performed TBNA + C-ROSE examination on 625 patients with mediastinal enlarged lymph nodes with an average diameter of (14.4 ± 7.9) mm. It was found that the coincidence rate between ROSE diagnosis of hilar mediastinal benign granulomatous lesions and the final pathology was 81.6%. This study shows that not only malignant diseases, ROSE technology can also help hilar mediastinal benign lesions for on-site rapid diagnosis.For instance, EBUS-TBNA can be used to sample hilar and mediastinal lesions that cannot be found under common bronchoscope, which has unique advantages in the diagnosis of hilar and mediastinal diseases and lymph node staging in patients with lung cancer. A large number of literatures have shown that ROSE technology combined with EBUS-TBNA or conventional transbronchial needle aspiration (C-TBNA) can reduce unnecessary puncture, reduce operation-related complications, guide the process of on-site operation, and improve the positive rate of diagnosis. Collins study found that combined TBNA and ROSE sampling reduced 33% of unnecessary punctures and 30% of unnecessary smears, due to the use of ROSE technology, 68% of the patients were successful with a single puncture of TBNA. Samples were collected under the guidance of R-EBUS. It was found that the time spent in C-ROSE group was lower than that in non-ROSE group (19 ± 8) minutes vs (31 ± 11) minutes, P < .0001). The positive rate of diagnosis in ROSE group was 97%, and there was no obvious complication of operation, so it was considered that ROSE + R-EBUS could help to improve the diagnosis rate of peripheral lung cancer, shorten the operation time, and reduce the risk of complications. In Chen study, R-EBUS guided transbronchial brushing or lung biopsies were performed in 815 patients with peripheral pulmonary lesions, of which 279 patients were randomly selected and received C-ROSE. The results of the study found that the diagnosis rate of peripheral pulmonary lesions in ROSE group was higher than non-C-ROSE group(88.9% vs 74.5% P < .05), suggesting that C-ROSE is helpful to improve the diagnostic efficiency of peripheral pulmonary diseases, the report also found that for some lesions in the tip of the right lung, the left tongue segment and other difficult parts, the diagnosis rate of ROSE group and non-ROSE group was 77.4% and 56.8%, there was still statistical difference. In pleural lesions, ROSE may also be beneficial. A prospective study showed that ROSE during medical thoracoscopy was found to have high accuracy for predicting malignancy. ROSE can provide the thoracoscopist with an on-site preliminary diagnosis, especially in cases with inconclusive macroscopic appearance. The above studies show that ROSE technique can help to obtain accurate and reasonable samples, improve the diagnostic efficiency of hilar and mediastinal lesions, peripheral pulmonary lesions and Pleural lesions, and reduce the risk of complications.As for peripheral pulmonary lesions, a prospective cohort study was conducted by Stein fort DP. Another study also found that ROSE helped to ensure the validity and adequacy of samples, and can provide optimal samples for flow cytometry, immunostaining, and molecular pathology, but it had no significant effect on the diagnosis rate. According to the study of Madan, in the 2 EBUS-TBNA groups, the use of ROSE had no significant effect on the diagnosis rate. In view of the large difference of the diagnostic value of ROSE, this review further clarify the accuracy and diagnostic efficacy of ROSE in interventional pulmonology. Besides, ROSE is a simple but complex program, the specific operation steps can be divided into 3 steps: production, dyeing, reading, and interpretation. Since the purpose of ROSE is to guide the intervention process in real time, its technical core lies in 2 points: Firstly, to improve the speed of film production and staining as much as possible; secondly, to accurately interpret the cytological results. So we extract and analyze the ROSE process information that might have an impact on the main outcome: such as smear method, profession of smear technician, staining method, profession of stain technician, invasive procedure, Anesthesia method and et al. We hope this review will stimulate proper evaluation of ROSE and provide assistance for clinical practice.However, there are different views indicating that ROSE does not help to improve the positive diagnosis rate. For example, a retrospective study of Liu found that ROSE was helpful in guiding the operation of EBUS-TBNA, but did not improve the rate of pathological diagnosis of TBNA.44.1This review is ongoing, the study has begun from August 2019 and it is expected to end in July 2020.4.2This study is sponsored by the science and technology department of Sichuan province (grant no: 2019YFS0084), the funder do not take part in the study design, data collection and analysis, or the preparation of the manuscript. The funder has provided only financial support for the study.4.3This review does not require ethical approval because the included studies are published data and do not involve the patients privacy. The results of this review will be reported in accordance with the PRISMA-DTA extension statement and disseminated to a peer-reviewed journal.Conceptualization: Xiangwen weng, Lijia Zhi, Xing An.Methodology: Xiangwen weng, Lijia Zhi, Xing An.Software: Meixin Xu, Hua Zhang.Supervision: Kunlan Long, Peiyang Gao.Writing – original draft: Xiangwen weng.Writing – review & editing: Xiangwen weng, Lijia Zhi.


Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment. Osteosarcoma (OS) is the most common primary bone tumor in children, adolescents and young adults, representing approximately 3.5% of all childhood cancers and 56% of malignant bone tumors in children. Its incidence rate ranges between 1 and 5 cases per million people and it is usually diagnosed in patients who are 10 to 19 years old. OS follows a bimodal distribution, with an initial peak in late adolescence and young adulthood and a second peak in old age [The management of patients diagnosed with OS has not changed in recent decades. Current systemic OS first-line therapy includes cycles of cisplatin, doxorubicin, and high-dose methotrexate (MAP). Second-line therapy can integrate some tyrosine kinase inhibitors such as sorafenib and everolimus, plus antineoplastic agents like etoposide, topotecan and cyclophosphamide . NeoadjuThe prognosis of this disease is highly variable, possibly due to its high rate of tumor mutations, which leads to widespread dysregulation in cell signaling pathways and genomic instability . PatientDespite current chemotherapy regimens being the most effective strategy for OS treatment, patients’ sensitivity to these agents regarding the toxic side-effects and antitumor effects varies considerably ,11. SeveThe development and validation of novel therapeutic compounds is a time-consuming and labor-intensive process. Drug repositioning, which explores potential novel uses for known molecules based on prediction algorithms has become an effective and innovative approach ,16,17. OIn this sense, the application of these models is of high interest to researchers specializing in cancer. Several studies have focused on the description of new therapeutic agents, especially for treating carcinomas ,30,31,3250, GI50, and C50 assays  was performed on 277 compounds described for HOS, 124 for MG63, 173 for U2OS and 341 for SAOS2 A and we Since inactive compounds are described in greater numbers than active ones in all cell lines, we evaluated the chemical diversity in the inactive series by applying a hierarchical clustering. Thus, we calculated the degree of similarity in the inactive compounds to balance the data through stratified random sampling instead of a solely random partitioning. As a result, hierarchical representations were generated in which the clusters at each level of the hierarchy were created by merging clusters at the next level down . In our Therefore, we separated 24 inactive compounds for all cell lines (24 in each) reported in the DrugBank, as mentioned above, and then 7, 14, 19 and 29 compounds from each list. The balanced datasets resulted in 246 molecules for HOS, 86 for MG63, 130 for SAOS2, and 288 for U2OS, with a ratio of 1:1 between active and inactive compounds. The prediction algorithms used were: support vector machine (SVM), random forest (RF), neural networks (NN), decision tree (DTREE), k-Nearest Neighbors (KNN), and a scalable end-to-end tree boosting system (XGBoost) . As seen in SAOS2 modeling resulted in only one algorithm with AC greater than 0.8, namely KNN (0.833). Lastly, the prediction models for U2OS with ACs > 0.8 were SVM, RF, NN, and KNN , all of whose SN and SP rates were higher than 0.8. XGBoost and DTREE were not taken into account for the assembly.It is interesting to note that the feature selection by genetic algorithm (GA) reduced from 500 to 19–101, depending on each dataset. This strategy allowed us to generate the models described above and obtain the desired performance measurements for the final model . The AC, SN and specificity (SP) evaluated the performance of a model based on its training and data described as external, but these metrics do not always describe a desirable recovery rate at the time of performing screening for drug repositioning . TherefoWe used all base-models with an AC greater than 0.8 in previously described external validation, and tested all possible combinations. Based on the VS results, the best multi-objective model was made up of the desirability values of the algorithms HOS-SVM, HOS-RF, MG63-SVM, SAOS2-KNN, U2OS-NN, and U2OS-KNN.As seen in When analyzing the active retrieved fractions of all the models B, one noThe screening weighted four principal drug classes in the first 1% of the screened list: anti-infectives for systemic use ; antineoplastic/immunomodulating agents ; dermatological/immunosuppressant ; and antiparasitics . The first two groups represent more than 85% of all repositioned drugs A. All thThe action mechanism of antineoplastic and immunomodulating agent mainly inhibits the mTOR pathway and microtubules polymerization. Among these, temsirolimus, paclitaxel, sirolimus (rapamicine), everolimus, cabazitaxel and docetaxel are ranked at the top. On the other hand, broad-spectrum antibacterials described as drugs that bind to the 30S/50s subunit of bacterial ribosome, HIV-1 protease inhibitors and antimycobacterials, which inhibit DNA-dependent RNA bacterial polymerase, were weighted in the screening. We also found two molecules used for the treatment of HIV, described as inhibitors of HIV-1 protease (tipranavir and fosamprenavir) B. It is Several reports have shown that multi-objective models have a better prediction rate during screening time since they approach the problem with a particular perspective from a set of potentially desirable solutions ,21,38,39Drug repositioning is an effective strategy for finding novel drug-disease relationships for existing molecules. The development of these strategies has gained considerable interest in recent years compared with de novo drug discovery pipeline, which demands more research time and experimental hours in the case of new drug development, and requires a greater financial investment. On the other hand, the use of already proven drugs is a highly efficient, low-cost and low-risk strategy since screening is carried out on molecules that have passed all clinical safety tests at Phase I, Phase II, and Phase III ,41. We uhttps://clinicaltrials.gov/)  are enrolled in clinical trials for cancer patients (reviewed at ls.gov/) : temsiroCancer cells are characterized by unregulated proliferation, which leads to cellular undifferentiation and disruption on the function of tissues. Cell proliferation can be caused by a checkpoint failure in cell cycle or a disruption in the cell death pathway. In this sense, any agent that affects the metabolism of cancer cells by reducing or inhibiting cell proliferation and promotes apoptosis is a potential target for cancer treatment . SeveralIt is interesting to note that 54.5% of the total predicted compounds (12 out of 22) are classified as anti-infectives for systemic use. More specifically, taking into account the Anatomical Therapeutic Chemical (ATC) classification system, our protocol weighted several macrolides , tetracyclines , protease inhibitors (tipranavir and fosamprenavir) and antimycobacterial (rifampicin and rifapentine) as possible anti-OS agents B. On theThese results may be promising for future preclinical and clinical studies. The lack of therapeutic options for OS should be the basis of searches for new agents as potential treatments. The discovery of molecular targets in OS will be part of the development of new molecules that could give these patients more options.50 , GI50 (percentage of cell grow inhibition at a fixed concentration), and EC50 , and from these scores, we defined a class for each compound.Prediction models were developed from compounds described in the Chemical database (Version 25) of the European Molecular Biology Laboratory (ChEMBL) ,63 with Compounds with standard values > 10 µM were classified as inactive (0), and those with values < 10 µM as active (1). In those drugs where two or more assays are reported, and the standard values classify these compounds in different classes, the final criteria were assigned by most of the set of classes obtained. If more than 75% of the tests obtained classify a compound in the same class, this drug was included in the study, otherwise it was rejected. On the other hand, compounds that did not show information about their biological activity, inconclusive data about their activity, or incomplete information regarding ChEMBL ID or canonical SMILES were removed from the analysis.We used the ChemAxon’s JChem for Excel (18.8.0.253)  softwareTwo-dimensional molecular descriptors were computed with ISIDA Fragmentor 2017 ,68. The The computed descriptors for each dataset were first filtered to remove those present in less than 1% of the compounds. Next, the Minimal Redundancy Maximal Relevance (mRMR) algorithm  was emplIn each cell line under study, the amount of active and inactive compounds varied considerably. This unbalance in the dataset is not desirable for modeling. In order to balance the classes, the following procedure was executed in all cell lines. (1) Using the previously computed molecular descriptors, we carried out a hierarchical clustering. We applied the interval measure, the Euclidean distance and Ward’s method for clustering, both in active and inactive compounds for each cell line. The IBM SPSS Statistics software v.25  was employed to generate dendrograms and define all the clusters within the data. (2) Once the number of clusters had been defined, we continued with a random stratify extraction of the same amount of compound in both classes. This procedure had previously been used by other authors in order to obtain a balanced data representative of the chemical diversity space . The traTo obtain each model, we applied genetic algorithms as feature selection by considering an initial population of 50 chromosomes and 30 generations. For validation of the fitness function in GA, we performed a cross-validation strategy using the average balanced classification rate (BRC) across 100 random splits (bootstrap sampling). This means that in each generation, 100 models were evaluated and the average AC was extracted. The models used together with genetic algorithm were: the support vector machine, random forest, neural networks, decision tree, k-Nearest Neighbors, and a scalable end-to-end tree boosting system . The SVMk = 1, …, 4). For each of the cell lines, several possible models are available. The resulting prediction of each model for a given compound resulted in a score linked to the class membership- either a prediction score for an active class, and/or score for an inactive class. In all cases, we established the score for which the compound was active against the cell line. However, the calculated class membership for some machine learning algorithm occurred in the range of positives and negatives, where active compounds showed positive values and vice versa. For all the cases, the geometrical mean of all scores of the compound to be active in a particular cell line  was used as a desirability score for each model . As first- and second-line therapy drugs, we included [For VS, we developed a dataset with those antitumor compounds used in the current management of osteosarcoma, not included in either the training or external sets for any cell line and with compounds validated in clinical studies for OS, published on the US government’s Clinical Trials website , and common ChEMBL compounds for the four cell lines that showed no biological activity . Additionally, we generated Decoy molecules based on the selected active compounds by employing the DUD-E server 5 . We incoThe performance of our models within this VS scenario was evaluated by computing the AUAC, BEDROC and EF ,82:(6)AUn represents the number of active compounds, N the total number of compounds in the dataset, ix the relative ranking of active compound i in the ranked list, χ the fraction of data for which EF will be computed, aR the rate of active compounds in the dataset (n/N), and α is the α parameter which ensures that active compounds ranked at the beginning of the ordered list result in higher weights than those at the tail. The α parameter is computed using the following equation:z represents the fraction of the ranked list at which enrichment is important and θ is the expected contribution of the enrichment at this z% fraction to the overall enrichment.In the above equations, In conclusion, this study presents a multi-objective prediction algorithm developed from compounds described with biological activity for the osteosarcoma cell lines HOS, MG63, SAOS2 and U2OS. The performance of this multi-objective model considerably improves the recognition rate in a virtual screening scenario, developed on drugs used as first- and second-line treatment for OS. Specifically, a high level of performance was observed for the recognition of molecules with biological activity within 1%. Using this ML algorithm on 2218 compounds described in the DrugBank, we found several antineoplastic agents currently being studied in clinical trials for the treatment of OS. Interestingly, Cabazitaxel is a compound with chemotherapeutic activity that is being studied in several clinical trials for different types of carcinomas and not in sarcomas, therefore it can be taken into account for clinical validations in patients with OS. Furthermore, several broad-spectrum antibiotics, for instance clarithromycin, erythromycin and doxycycline, were top-ranked drugs in our screening. These compounds have already been studied in various types of carcinomas, so they comprise an interesting group of drugs for developing therapeutic validation studies in bone cancers. One of the main limitations was the lack of experimental validation of the drugs proposed for repositioning because this was an initial study. Although it is true that several compounds have already been studied in clinical trials, the validation process of their biological activities on bone tumor cells is an indispensable step in order to proceed with a validation strategy in patients, hence this will be the next procedure in further studies.


Aspergillus infections, particularly in patients with acute respiratory distress syndrome (ARDS), other fungal infections could occur, probably more easily, due to the immunological dysregulation and the critical condition of these patients. Probiotic preparations of Saccharomyces are broadly used for the prevention of antibiotic-associated complications, especially in the intensive care units (ICU). On the other hand, Saccharomyces organisms are reported as agents of invasive infection in immunocompromised or critically ill patients. We report two cases of bloodstream infection by Saccharomyces in two patients hospitalised in the ICU, due to severe COVID-19, after Saccharomyces supplementation.Co-infections have an unknown impact on the morbidity and mortality of the new clinical syndrome called coronavirus disease 2019 (COVID-19). The syndrome is caused by the new pandemic coronavirus SARS-CoV-2 and it is probably connected with severe traces in the elements of the immune system. Apart from possible  The new pandemic caused by the coronavirus SARS-CoV-2 has evolved as a major health threat and has been connected to a big number of deaths worldwide, while the future spread of the disease is more or less unknown.While it is already known that patients with co-morbidities and underlying diseases present poorer clinical outcomes , the freAspergillus, have already been reported [There are common risk factors, such as hospitalisation in the intensive care unit (ICU), chronic respiratory diseases, corticosteroid therapy or intubation and mechanical ventilation  that coureported ,6,7,8.Saccharomyces cerevisiae has been a well-known and emerging agent of invasive fungal infection since the 1990s in immunocompromised or critically ill patients [patients  and it ipatients , it can Saccharomyces cerevisiae supplementation because of diarrhea, and subsequently developed a Saccharomyces cerevisiae bloodstream infection. Informed consent was acquired from both patients with opt out possibility.We report two cases of critically ill patients who had to be hospitalised in the ICU due to COVID-19, received They concern two male patients, 76 and 73 years old, with no other underlying diseases apart from regulated arterial hypertension (both of them) and diabetes (the second). The first one was also an ex-smoker.Both patients presented fever, dyspnea and hypoxia, while the second also had a nonproductive cough. They were admitted for hospitalisation by different departments of internal medicine. In two days’ time, due to the worsening of their clinical condition and the concomitant development of ARDS, they had to be intubated and subsequently transferred to the ICU.Meanwhile, the molecular testing for SARS-CoV-2 was found positive in both patients’ upper and lower respiratory specimens by the use of real time RT-PCR methods . The first patient was found positive again ten and fifteen days later, while he became negative almost twenty-five days later. The second patient was positive when retested on the fifteenth day and negative twenty days later.Staphylococcus hominis and Acinetobacter baumannii first and Staphylococcus epidermidis and Acinetobacter baumannii second. Moreover, Acinetobacter baumannii and Pseudomonas aeruginosa were isolated from the cultures of their bronchial secretions.During their stay at the ICU they had several successive positive blood cultures for Staphylococcus hominis and, at day 26, for Acinetobacter baumannii, which continued even up to day 45. Meanwhile, at day 11, he presented a positive bronchial culture for Pseudomonas aeruginosa and, from day 26 and thereafter, for Acinetobacter baumannii.The first patient, from the day of his admission in the ICU and for ten days thereafter, had a positive blood culture for Acinetobacter baumannii and at day 15 by Staphylococcus epidermidis. The presence of Acinetobacter baumannii relapsed at day 18, while all blood cultures became negative at day 30. He also had positive bronchial cultures for Acinetobacter baumannii between days 10 and 15 and again from day 18 to day 30.The second patient had a positive blood culture at day 10 by For all the aforementioned blood and bronchial infections, the two patients received empirical, as well as documented (by culture and sensitivity testing), treatment with several antibiotics, such as piperacillin–tazobactam, moxifloxacin, linezolid, azithromycin, meropenem, colistin, daptomycin and tigecycline. The sequence of the relevant antimicrobials is seen in Moreover, the two patients developed a gradual worsening of their renal function and had to undergo several sessions of haemodialysis. The decrease in the kidney capacity could be attributed to both the accumulative nephrotoxicity of several of the antimicrobials, as well as to the COVID-19 itself .Saccharomyces cerevisiae (boulardii)) at 250 to 500 mg/day.Both patients developed diarrhea and were prophylactically treated with Ultra-Levure , suffered a bloodstream infection by revisiae . The samSaccharomyces cerevisiae.After the incubation of the blood cultures, positive direct microscopy and inoculation on Sabouraud dextrose agar with chloramphenicol 0.05%  and malt extract agar  at 30 °C and 35 °C, the use of germ tube testing and CHROMagar Candida  and biochemical testing by API ID 32C , the two strains were phenotypically identified as Further on, both identifications were molecularly confirmed by the amplification and sequencing of the internal transcribed spacer 1 (ITS1) region of the fungal ribosomal DNA (Gen Bank Accession Numbers: MT527544 and MT522376). Both sequences presented a 100% alignment between each other, as well as with the sequence of the strain used in the specific preparation of Ultra-Levure, providing arguments for the genetic relatedness and similarity of all three of them.Saccahromyces and, mainly, boulardii on RPMI agar [A sensitivity testing was attempted by ATB™ Fungus 3  for amphotericin B, flucytosine, fluconazole, itraconazole and voriconazole and MIC (minimum inhibitory concentration) Test Strips  for posaconazole and anidulafungin, although there are possible difficulties concerning the growth of PMI agar . The groSaccharomyces, the above results indicate a probable in vitro sensitivity to flucytosine, amphotericin-B, fluconazole, voriconazole, posaconazole and anidulafungin.The MICs for the first isolate were 4 μg/mL for flucytosine, 1.0 μg/mL for amphotericin-B, 0.5 μg/mL for itraconazole, 4.0 μg/mL for fluconazole, 0.125 μg/mL for voriconazole, 0.032 μg/mL for posaconazole and 0.047 μg/mL for anidulafungin, while, for the second one, they were 4 μg/mL for flucytosine, 0.5 μg/mL for amphotericin-B, 0.5 μg/mL for itraconazole, 4.0 μg/mL for fluconazole, 0.125 μg/mL for voriconazole, 0.064 μg/mL for posaconazole and 0.002 μg/mL for anidulafungin. Although there are not defined clinical breakpoints for In both patients, no fungal presence was found in any of the bronchial cultures.Gradually, both patients showed clinical improvement and were transferred again to the department of internal medicine of the hospital. The first patient, after almost eighty days of hospitalisation, was discharged from the hospital, while the second had to be transferred to a regional teaching hospital due to complications with his tracheostomy.Both described patients were hospitalised in a small and relatively new intensive care unit with four beds. Due to the outbreak of COVID-19, two more beds, exclusively for these patients, were added. These beds were separated from the rest of the ICU and from one another, while separate nursing staff were provided for each COVID-19 patient. Moreover, all necessary, very strict measures were taken in order to maintain “sterile” conditions between the patients.Saccharomyces had occurred for at least the last four years in this ICU, despite the fact that it was a long lasting and common practice to use preparations of the fungus for the prophylaxis of patients under antibiotics and concomitant diarrhea. During the last four years, almost eighty patients per year  were to in this specific ICU and at least half of them were under prophylactic preparations of Saccharomyces. This was the first time that such a fungaemia occurred and only during the hospitalisation of these first two COVID patients.According to the experience of the medical staff of the unit and the well-recorded data, no case of fungaemia due to CD8 cells have a major role in the clearing of the virus from the pulmonary tissue. However, these cells can be relatively dysfunctional due to the highly produced epithelial cytokines and the impairment of their function could affect the function of dendritic cells and macrophages [Apart from the devastating consequences of SARS-CoV-2 on the respiratory function, there are also pronounced effects on the absolute numbers of lymphocytes, leading to lymphopenia and an increase in several cytokines and inflammatory markers ,14,15. Lrophages ,17,18. Arophages .Saccharomyces, may pose a high risk to patients suffering from immune deficiencies due to malignancies or immunosuppressive treatment. Moreover, oral mucositis or ulcers may lead to yeast translocation into the bloodstream of such patients [Saccharomyces cerevisiae is supposed to be digestive [The administration of probiotic preparations containing live yeasts, like patients ,21. Centpatients ,23, althigestive . Furtherigestive . Yeasts igestive .Saccharomyces boulardii, which is used in commercial probiotic preparations is considered to be an invalid taxon and either a subtype or a variety of Saccharomyces cerevisiae [S. boulardii, after probiotic use, is more often seen in critically ill patients in the ICU rather than in typical immunodeficient patients [revisiae ,26,27, irevisiae . Fungaempatients ,20,28. HSaccharomyces cerevisiae/boulardii were used for many years as protective agents in this specific ICU and even during the same period of hospitalisation of other patients, only the two specific patients with COVID-19 presented a bloodstream infection. The fact that they were completely separated from one another and the other patients of the ICU and were treated by separate nursing staff, with all the recommended precautions for the avoidance of SARS-CoV-2 transmission, extremely reduces the chances of being contaminated by manipulations or acquisition from other patients or personnel and makes a connection to the probiotic preparations more possible.In the described cases, it is interesting that, although the probiotic preparations of In addition, the fact that both fungaemias occurred four to six days after the initiation of Ultra-Levure makes even more possible the connection of the infection to the use of the specific preparation and the concomitant fungal translocation from the gastrointestinal tract to the bloodstream.d-glucan, is correlated to Sepsis-Related Organ Failure Assessment score (SOFA score) and the gravity of the disease in terms of septic shock [Yeast overgrowth and gastrointestinal (GI) leakage, caused by either direct or indirect gastrointestinal injury, could be important pathogenic factors for invasive mycoses. Among other factors, intestinal surgery, haemodialysis, intensive chemotherapy and sepsis could play important roles in the aforementioned GI leakage. Moreover, there are indications that the occurrence of fungal translocation through mucosal barrier damage, as indirectly calculated by the measurement of serum (1→3) β-ic shock ,30.S. boulardii from some S. cerevisiae strains [It is reported that the sequencing of the ITS region of the fungal ribosomal DNA cannot possibly discriminate  strains ,31. HoweSaccharomyces cerevisae, indicates the need for cautious use of the relevant probiotic preparations in COVID-19 patients.Although further data and observations are needed, the occurrence of the described cases of two patients suffering from severe COVID-19, with long periods of hospitalisation in the ICU and concomitant bloodstream infection by


Repeated invasive intracranial pressure (ICP) monitoring is desirable because many neurosurgical pathologies are associated with elevated ICP. On the other hand, it could become a risk for children to repeat sedation, anesthesia, or radiation exposure. As a non-invasive method, measurements of optic nerve sheath diameter (ONSD) has been revealed to accurately predict increased ICP. However, no studies have indicated a relationship among age, brain, and ventricular parameters in normal children, nor a prediction of increased ICP with artificial intelligence.2 = 0.60). This study revealed a linear correlation of mONSD measured on CT with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. Additionally, the multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. The results of the group comparison between control and suspected increased ICP revealed a statistical significance in mONSD and the width of the ventricles. The study indicated that supervised machine learning application could be applied to predict suspected increased ICP in children, with an accuracy of 94% for training, 91% for test.This study enrolled 400 normal children for control and 75 children with signs of increased ICP between 2009 and 2019. Measurements of the parameters including ONSD on CT were obtained. A supervised machine learning was applied to predict suspected increased ICP based on CT measurements. A linear correlation was shown between ln(age) and mean ONSD (mONSD) in normal children, revealing mONSD = 0.36ln(age)+2.26 (RThis study clarified three issues regarding ONSD and ICP. Mean ONSD measured on CT was correlated with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. The multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. Mean ONSD and the width of ventricles were statistically significant in children with signs of elevated ICP. Finally, the study showed that machine learning could be used to predict children with suspected increased ICP. In the clinical settings of pediatric neurosurgery, many pathologies are associated with increased intracranial pressure (ICP). For the initial diagnosis and follow-up evaluations of many underlying neurosurgical conditions, repeated invasive ICP measurements are desirable; however, it could become a risk for children to repeat sedation, anesthesia, or radiation exposure. Therefore, a reliable and useful clinical technique to non-invasively assess the ICP status in children is admirable , 2.Recent studies have indicated the efficacy of various non-invasive methods for predicting elevated ICP –5. In thArtificial intelligence (AI) uses computer systems to simulate cognitive abilities to achieve goals. Machine learning classification is one of the domains of AI that enables an algorithm or classifier to learn patterns in large, complex datasets and produce useful predictive outputs. The number of published machine learning studies in neurosurgery is increasing exponentially –14. SomeThe purpose of this study was to identify three issues regarding ONSD and suspected elevated ICP. First, to determine the relationship between ONSD and age, the brain width, or the ventricular width by CT in normal children. Secondly, we performed an analysis to detect differences between normal children and children with clinical signs of elevated ICP. Finally, the feasibility of machine learning to predict children with suspected elevated ICP was examined.This is a retrospective study of a prospectively collected database on children visited or admitted to our hospital between 2009 and 2019. The inclusion criteria are as follows; the control group (CTR) included 400 children who visited our hospital because of minor head injury or other minor events, revealed no intracranial or skull abnormal lesion on CT of the head. The exclusion criteria included children with previously diagnosed intracranial lesions. The suspected increased intracranial pressure (siICP) group enrolled 75 patients who presented with signs of elevated ICP, such as disturbance of consciousness, confusion, irritability, increased sleepiness, shrill or high-pitched cry, headache, nausea/vomiting, bulging of anterior fontanelle, and convulsion. A board-certified pediatric neurosurgeon (TM) and board-certified neurosurgeons confirmed children with at least 2 signs of increased ICP as an siICP.2, BB2, and DD2, ieMeasurements of parameters, including ONSD, were performed under a rigorous protocol established by our team to minimize interobserver variability. Our protocol includes several points: using the same application software on our PACS system, selecting CT images with maximum optic nerve visibility, using the same magnification and the same density, and measuring three times to minimize interobserver variability. The ONSD was measured 3 mm behind the posterior aspect of the globe on axial sequences  using thFor univariate analysis, Fisher’s exact test was used to determine significance, which was defined as p < 0.05. This study was satisfied when the statistical power is 0.8 or higher, corresponding to an 80% chance of detecting a real effect of the expected size. Also, multiple regression analysis and Spearman’s rank correlation analysis were applied to investigate the relationship between CT parameters.A supervised machine learning in which the program was written by python was applied to predict suspected increased ICP based on ln(age) and the measurements on CT. The logistic analytical method was used in the algorithm to classify the children into intact and suspected elevated ICP. In detail, 80 percent of the total 475 cases were used for training, 20% for test. A combination of the values among ln(age), mONSD, A, B, D, AA, BB, DD, or EE was used for maximum accuracy with as few values as possible. The precision of the algorithm was evaluated by calculating accuracy.This study complies with the standards of the Declaration of Helsinki and the current ethical guidelines. The study also was approved by the institutional ethics board and by the IRB of Matsudo City General Hospital. Verbal consent was obtained from the caregivers for using the data.After converting the day of life to the logarithmic expression, ie ln(age), -x).Supervised machine learning was applied to classify the children to predict suspected raised ICP as shown in This study identified three issues regarding ONSD and suspected elevated ICP. First of all, the study revealed a relationship among ONSD measured on CT and ln(age), parameters of the brain, or the ventricles in 400 normal children. Secondly, measurement of the parameters including ONSD on CT showed that ONSD and the width of ventricles were statistically significant in children with signs of elevated ICP compared to control children. Finally, the study showed that machine learning could be used to predict children with suspected increased ICP.2 and F value. The multiple regression equation obtained could explain 67% of mONSD. The analysis also revealed the correlation coefficient of ln(age), B, and EE were significant at the level of p < 0.01, indicating those 3 elements significantly contribute to the equation. The mONSD in the control children could be speculated mathematically with age, minimum bicaudate nuclei distance, and the square root of 3 brain widths. Based on the univariate and multivariate analyses, mONSD was speculated to correlate strongly to ln(age) and the brain width, and fairly to the ventricular width. To the best of our knowledge, this is the first report which revealed a relationship between ONSD and the CT parameters including the width of the brain or the ventricles.The results of the multivariate analysis revealed the relationship between each parameter, as shown in Although a slightly lower coefficient of determination was observed in females than in males , the F vAs shown in Several studies have found a correlation between ONSD on ultrasound , 16, 17,Based on the results mentioned above, we tried to use supervised machine learning to classify the children into 2 groups, intact and siICP, using ln(age) and CT parameters.-x). To the best of our knowledge, our study presents the first to predict suspected increased ICP in children using an AI, especially supervised machine learning. When inserting ln(age), mONSD, D, and EE obtained from CT of the head in the child whom we would like to know whether he or she may have suspected increased ICP into the equation, it automatically reveals a probability of suspected elevated ICP with high accuracy. This study indicated that the supervised machine learning could be used to predict suspected increased ICP in children. Although machine learning-based systems are powerful technologies as mentioned above, they should not replace the clinical judgment of physicians and medical teams [The values of ln(age), mONSD, A, B, D, AA, BB, DD, and EE were combined to obtain maximum accuracy with as few values as possible. Among these values, the analysis revealed only 4 values, ln(age), mONSD, D, and EE, were the ones for obtaining the maximum accuracy. It revealed an accuracy of 94% for training, 91% for test. The logistic equation obtained was x = -0.89ln(age) + 3.50mONSD + 0.37D − 0.08EE—0.69, probability y = 1/ and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. Additionally, the multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. Second of all, measurement of the parameters including ONSD on CT showed that ONSD and the width of the ventricles were statistically significant in children with signs of elevated ICP as compared with control children. Finally, the study showed that machine learning could be used to predict children with suspected increased ICP.S1 Table(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file.S3 Table(TIF)Click here for additional data file.S4 Table(TIF)Click here for additional data file.S5 Table(TIF)Click here for additional data file.