Body composition analysis is a valuable tool for assessing and monitoring the nutritional status of children with spinal muscular atrophy (SMA). This study was designed to compare the consistency of bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), as the gold standard method for assessing body composition in clinical practice when treating children with type II and III SMA.From 2019 to 2021, we performed a retrospective analysis of body composition by DXA and BIA measurement methods in patients with type II and III SMA treated at a Chinese tertiary children's hospital. Fat mass (FM), muscle mass (MM), bone mineral content (BMC), and visceral fat area (VFA) were compared using paired sample t-tests. We calculated Lin's concordance correlation coefficient (CCC) and Spearman correlation coefficient to verify the correlation between DXA and BIA measurements. Bland–Altman analysis was used to assess the consistency of the two methods.P <0.001), whereas the average MM, BMC, and VFA measured by BIA are significantly higher (P < 0.001) in children with SMA. Overall, the difference between MM (Delta [BIA-DAX] = 1.6 kg) and FM (Delta [BIA-DAX] = −1.6 kg) measured by DXA and BIA was minor, whereas the difference of VFA (Delta [BIA-DAX] = −43.5 cm) was significantly large. Correlation analysis indicated a substantial correlation of MM  = 0.93–0.98], r = 0.967 [P < 0.0001]) and FM , and poor correlation of BMC  and VFA  measurements between the two methods. The Bland–Altman analysis suggests that the majority of participants were within LOA. In addition, differences in MM and VFA measurements between BIA and DAX increased according to patients' increasing height, whereas differences in FM and BMC did not differ with height.Fifty-seven children with type II and III SMA were recruited. Compared with body composition measured by DXA, the average FM measured by BIA is significantly lower (BIA overestimates MM and underestimates the FM, BMC, and VFA in children with SMA compared with DXA measurements. Overall, the non-invasive, easy-to-use, and repeatable BIA measurements were found to be in good agreement with DXA measurements, especially for FM and MM, which are essential parameters for the nutritional evaluation of children with SMA. Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disease mainly caused by the homozygous mutation or deletion of chromosome 5q in the survival motor neuron 1 gene. This mutation causes a deficiency in survival motor neuron proteins . SMA is The current updated consensus statement for the standard of care in SMA children highlighted multidisciplinary team (MDT) care is devoted to reducing complications and potentially improving patients' and caregivers' quality of life , 8, 9. FThere are numerous methods for determining body composition, ranging from simple indirect measures, such as calipers (skinfold thickness), to sophisticated and noninvasive instruments, such as computed tomography (CT), ultrasound, bioelectrical impedance analysis (BIA), and dual-energy X-ray absorptiometry (DXA) , 16. HofDespite the limitations and confounding influences of all body composition methods, DXA is frequently regarded as a reference method for evaluating BMC, FM, and MM , 22. HowPrevious studies have shown that BIA has a good consistency for measurements of fat mass in both healthy and obese people , 24. DifWe retrospectively collected data for children with type II and III SMA from the Children's Hospital of Zhejiang University School of Medicine between December 2019 and September 2021, following the inclusion criteria listed as follows: (1) Confirmed diagnosis of 5q SMA with type II or III by genetic testing, (2) Ages ranged from 3 years to 18 years, (3) Both DXA and BIA examinations were completed.All the patients who participated in our study did not take any disease-modifying treatment or take part in any experimental pharmacological trials at the time of recording. Patients who were diagnosed with type I SMA or with incorrect and inaccurate data were excluded before analysis.Medical details such as demographics, puberty status, disease characteristics, ambulation status, and body composition with DXA and BIA were collected using a full medical record chart review from the patient's visit to the outpatient SMA MDT clinic. Patients' motor function was evaluated based on Hammersmith Functional Motor Scale–Expanded (HFMSE) score (ranged from 0 to 66). This study was approved by the Ethics Committee of the Children's Hospital of Zhejiang University School of Medicine (No. 2019-IRB-171). Informed consent was obtained from the patients or their guardians before their enrollment in this study.2) was determined. The World Health Organization Anthro Plus software was used to calculate the height for age Z score (HAZ) and BMI Z score (BAZ).Body weight (BW), height, BMI, and body composition with BIA were measured as part of a nutritional assessment by trained dietitians during a standard evaluation. The height was replaced with arm span in non-ambulatory patients using a flexible non-stretchable tape. BW was specifically measured with the assistance of caregivers. The children and their caregivers were weighed together, and then separately, and the difference in the two measurements was used to calculate the subject's weight. BMI  and a Inbody 770 MF-BIA device  under the standardized conditions required in the morning. Quality control calibration procedures for DXA were performed on a spine phantom. The weight of each subject was determined using a calibrated scale. The children were instructed to maintain a supine position on DXA within the scanning table's borders, and each body scan took approximately 10 min. The subjects remained standing barefoot on the specific inspection platform while their middle fingers, thumbs, and ankles were clamped with corresponding detector electrodes for the InBody S10 BIA. For patients unable to stand independently, they needed to sit in a nonmetallic chair to receive measurements. MM, FM, BMC, and VFA measurements were obtained using both DXA and BIA. All procedures were performed within 1 h on the same day of the visit, during which the children did not consume any foods or fluids.t-test. The mean difference of 1.96 SD was taken as the indicator to calculate the 95% limits of agreement (LOA) for the difference in measurements of the same individual . The IBM SPSS software was used to conduct all statistical analyses (version 21.0). A A total of 57 children with SMA  were included in the analysis , with 27P <0.001) and higher average values of MM, BMC, and VFA (P < 0.001). On average, BIA underestimated the FM about 12.6% (Delta [BIA-DXA] = −1.6 kg) and overestimated MM by 11.9% (Delta [BIA-DXA] = 1.6 kg), BMC about 50% (Delta [BIA-DXA] = 0.5 kg), and VFA about 77.6% (Delta [BIA-DXA] = 43.5 cm). Overall, the difference between MM and FM measured by DXA and BIA was minor, whereas the difference between BMC and VFA was significantly large.All body composition measurements by DXA and BIA, including MM, FM, BMC, and VFA, were presented in P = 0.056, 0.622, and 0.850, respectively), but the VFA-related results were statistically significant (P < 0.05). The Bland–Altman analysis suggests the majority of participants were within LOA. Furthermore, the correlation analysis verified the positive correlation of MM , FM , BMC , and VFA  between the two methods  and FM CCC = 0.95 (95% CI = 0.92–0.97) measurements, and poor correlation of BMC CCC = 0.61 (95% CI = 0.42–0.75) and VFA CCC = 0.54 (95% CI = 0.33–0.70) measurements between DXA and BIA in children with SMA. Those correlations in whole participants were consistent with those in children with type III SMA. Moreover, patients with type II SMA had a lower CCC of body composition measurements than patients with type III. There were moderate correlations of MM and FM measurements, and poor correlation of BMC and VFA measurements between DXA and BIA in children with type II SMA. Pitman's tests showed that there is no statistical significance of proportional bias for MM, FM, and BMC  may lead to a greater overestimation of MM by BIA than by DXA . Second,There is no denying that DXA, as the gold-standard method for evaluating body composition, takes advantage of BMC and density measurements to obtain knowledge of bone health . NeverthTo the best of our knowledge, this is a study with the largest sample size that compared body composition measurements obtained using DXA and BIA in children with SMA, although previous studies in healthy children or children with other neuromuscular diseases have been conducted , 44. FurWith the widespread use of drugs and gene therapy, an increasing number of children with SMA will have a higher survival rate and quality of life. The ability to accurately estimate MM and FM is of vital importance for assessing the nutritional status of children with SMA. BIA is emerging as an available approach in routine clinical practice for evaluating and monitoring nutrition.To sum up, BIA overestimates MM and underestimates FM, BMC, and VFA in children with type II and type III SMA, compared with the gold standard DXA measurement. Apart from this, FM and MM measured by BIA and DXA in children with type II and III SMA are in good agreement, whereas BMC and VFA are not, and this consistency is more obvious in children with type III than that in children with type II. In a word, BIA is considered to be a non-invasive, easy-to-use, and repeatable measuring tool for monitoring FM and MM, which is expected to be widely used in children with SMA in clinical practice.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committees of the Children's Hospital and Zhejiang University School of Medicine (No. 2019-IRB-171). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.WW contributed to the data analysis and writing of the manuscript. YF, QL, FC, and YC contributed to the collection of the data. SM and MM contributed to the design of the experiment and revising of the manuscript. All authors critically revised the manuscript, and read and approved the final manuscript.This study was supported by the National Natural Science Foundation of China (Grant No. 82271735), Key R&D Program of Zhejiang Province (2022C03167), the Zhejiang Province Public Welfare Technology Application Research Project (LGC21H090001), the Key Technologies Research and Development Program of Zhejiang Province (2021C03099), and a project supported by Scientific Research Fund of Zhejiang University (XY2022045).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To overview the research actuality and offer the hotspots and cutting-edge issues in the field of Non-suicidal Self-injury (NSSI) by using bibliometric analysis.Publications related to NSSI from 2002 to 2022 were extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace V 6.1.R2 and VOSviewer 1.6.18 were used to visually analyzed institutions, countries, journals, authors, references, and keywords in research on NSSI.via CiteSpace and VOSviewer. The number of annual publications related to NSSI is fluctuating growth. The USA and Harvard University are the most productive country and institutions. In the case of journals and co-cited journals, Psychiatry Research are the most productive journal and also ranked highest among co-cited journals. Furthermore, Michael Kaess has published the most publications, and Matthew K. Nock is the most cited author. An article published by Swannell SV et al. shows the highest citation counts. After analysis, the most common keywords are harm, adolescents and prevalence. The gender difference, diagnosis, and dysregulation are frontier areas of NSSI research.A total of 799 studies about NSSI were analyzed This study analyzed the research of NSSI from multiple perspectives, and provides valuable information for researchers to capture the current status, hot spots, and frontier trends of NSSI. Non-suicidal Self-injury (NSSI) is described as deliberate, direct destruction or alteration of body tissue without conscious suicidal intent . This inMany patients with non-suicidal suicide don’t take it seriously without treatment and eventually evolve into other psychiatric disorders such as borderline personality disorders (BPD), anxiety disorders, and affective disorders . The theBibliometrics is a mathematical and statistical method that analyzes and observes research trends and is widely applied in many fields . It playThe retrieval data were extracted from the Science Citation Index-Expanded of Web of Science Core Collection (WoSCC). The WoSCC is the most frequently used and acceptable database for scientific or bibliometric studies. It contains nearly 9,000 of the world’s most prestigious high-impact journals and more than 12,000 academic conferences . For exaAll search datas retrieved WOSCC were converted to VOSviewer and CiteSpace to perform visual analysis.VOSviewer is a computer program developed by Van Eck and Waltman at Leiden University in the Netherlands in 2009 that is suitable for constructing complex networks using large-scale data . It provCiteSpace is created by Dr. Chaomei Chen  and his team in 2004 . Burst dThe number of publications per specific period reflects the development trend of research in this field ; 14. As n = 40) has the most publications, followed by Heidelberg University (n = 31) and Katholieke University Leuven (n = 27). The network map of institution cooperation was generated by CiteSpace , followed by China (n = 109) and Germany (n = 84). This phenomenon may be related to the population of the countries. The larger the population, the greater the number of psychological problems associated with NSSI, which can arouse the attention of clinical workers and researchers. The country cooperation map was conducted CiteSpace , followed by journal of affective disorders (n = 63) and frontiers in psychiatry (n = 57). The highest impact factor journal was the Psychiatry Research , and the average of 10 journals’ impact factor was approximately 6.44. The network visualization map of journal co-citation analysis was generated by VOSviewer  and Paul L. Plener (n = 23). Co-cited author analysis refers to when the literatures of two authors are simultaneously cited by a third author. The visualization density map of co-citation author’s analysis was generated by VOSviewer , followed by “adolescents” (n = 323), “prevalence” (n = 321), “non-suicidal self-injury” (n = 267) and “behavior” (n = 220). The network of co-occurrence keywords was generated by VOSviewer  had the highest number of citations. Significantly, Michael Kaess had the most significant number of published articles without ranking in top 10 cited journals, whereas Mattew K. Nock published articles far lwss than Michael Kaess could listed first in co-citation journals. Therefore, it can be seen that we should not only pay attention to the number of articles, but also the quality of articles, and improve the impact of the research in the future.Furthermore, according to the top 10 authors in n = 105, IF = 11.225) and there is only two journal’s impact factor exceeded 10.0, whereas there was a journal’s impact factor is 1.899 that JQR ranked Q4. It can be seen that publishing articles related to NSSI in high-IF journals are a huge challenge. As for co-cited journals, Psychiatry Research  is also the journal with the highest citations. It is helpful to find the core journals published in the research about NSSI by analyzing the distribution of literature sources. It follows that the cited publications are all from high-impact journals, showing it is attached great importance to research NSSI in the worldwide academic field. The most co-cited reference was retrospective analysis of the prevalence of NSSI in non-clinical samples by Sarah V Swannell et al. Study on the behavior character of NSSI and its harm to patients can help to find the corresponding treatment fundamentally .(2)Study on the prevalence and risk factors of NSSI can better understand the phenomenology of NSSI and solve the different problems of the population . And the(3)Adolescent is the main group of NSSI research. The main cause is that numerous research showed that the NSSI prevalence of adolescents is higher. Surely, it also involved patients in the population of children , adults,(4)NSSI related to depression closely. Lu Wang, et al.  concludevia CiteSpace: a meta-analysis (2018–2022), ecological momentary assessment (2020–2022), and diagnosis (2020–2022).Additionally, keywords with the strongest citation bursts can reflect emerging trends and research frontiers. In this study, three frontiers of related research were captured as follows 1.Gender difference: Gender difference in NSSI is not a static gap, but evolves across time, widening in mid-adolescence and disappearing by early adulthood . Moye Xi2.Diagnosis: Numerous research focus on studying diagnostic methods of the NSSI to promote to treat this disease. Maria Zetterqvist et al.  discusse3.Dysregulation: Desregulation are usually considered as emotion dysregulation in the field of NSSI research, which many researchers find it closely connects with NSSI. Hedvig Andersson, et al.  considerAs far as we know, this is the first study in the Web of Science database that using bibliometric analysis to visually analyze NSSI from institutions, countries, authors, journals, co-cited references. Based on comprehensive indexes like journals, authors, countries, and institutions, bibliometric can conduct an in-depth evaluation of the research trends and the focus of a certain field; the combination of the knowledge map and visualized analysis can quantitatively reflect research status and practical applications simultaneously, and demonstrate the distribution of collaborations among countries, regions, disciplines, etc., , 51, othFrom bibliometric analysis and visualization of NSSI, it is can be seen that related literature developed rapidly at present. The USA is the most productive country and Michael Kaess is the most prolific author in this field. Psychiatry Research has the largest publications about NSSI. The research of NSSI hotspots and frontiers has been mentioned above and it will be very helpful to the researchers in this field.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.WS and MH designed the research subject and critically revised the manuscript. YZ and GL conducted the literature retrieval and screening. XD, QZ, and SL provided the guidance in statistical analysis. XD, NZ, YZ, and GL wrote the manuscript. All authors read and approved the final manuscript.


Future studies should examine the causal relationship between stigma and anxiety, depression, and psychological distress and how to intervene to reduce stigma among women undergoing infertility treatment. Japan has the highest number of cases of infertility treatment in the world. Studies have indicated that women undergoing infertility treatment feel stigmatized and suffer from psychological symptoms such as anxiety and depression. However, in Japan, few studies have quantitatively examined the association between the stigma of infertility and psychological symptoms, and, to our knowledge, no study has examined its association using a scale with tested reliability and validity. This study aims to quantitatively examine the relationship between infertility stigma and anxiety, depression, and psychological distress among women undergoing infertility treatment, using a scale that has been validated for reliability and validity. The cross-sectional study was conducted in December 2021 through a web-based survey of 254 participants undergoing infertility treatment. A multiple regression analysis was performed to examine the relationship between stigma and anxiety, depression, and psychological distress. Stigma was a statistically significant predictor of anxiety, depression, and psychological distress (standardized β = 0.58,  Infertility is defined as the failure to conceive despite 12 months of unprotected intercourse . In 2017In general, patients who are struggling to conceive express feelings of depression and anxiety . The psyIn addition to these factors, studies have shown that the stigma of infertility has negative impacts on health, including psychological distress such as anxiety and depression, low quality of life, and social isolation ,28,29,30In Japan, a qualitative study indicated that the traditional beliefs that “married couples should have children” and the notion that “couples are fully fledged when they have a child” are deeply rooted in the country . In factIn other countries, the relationship between stigma related to infertility and psychological distress, such as anxiety and depression, has been quantitatively examined ,23,24,25Previous studies in other countries also demonstrated the need for psychological intervention and suggested viable specifics for such interventions by examining the association between stigma and psychological distress, as well as quality of life ,17,21,25To overcome these limitations and fill the knowledge gap, we recently developed a reliable and validated scale to measure infertility stigma in Japan . This stThe purpose of this cross-sectional study was to test the relationship between infertility stigma and anxiety, depression, and psychological distress, controlling for sociodemographic and infertility characteristics of women undergoing infertility treatment in Japan. This cross-sectional study was conducted in December 2021 as part of our previous study that examined the reliability and validity of the Japanese version of the Infertility Stigma Scale , using aThe participants were recruited from among those registered in the databases of a survey company in Japan. Women living in Japan aged 20–59 were selected from the database and were invited to participate in the survey. These women received an e-mail about the survey, and those who wished to respond could access the online survey company’s website. After logging in, potential participants in this study selected the survey for this study from the list of questionnaires and proceeded to the screening stage. A total of 100,208 individuals were sent the e-mail, of which 10,000 answered the screening questions online. Participants were selected for the screening survey on the basis of a set of inclusion and exclusion criteria. The inclusion criteria were those who answered the screening questions; were undergoing infertility treatment (excluding those undergoing infertility testing); did not have children; were native speakers of Japanese; were married (including common-law marriage); and gave consent to participate in this study. The exclusion criteria were individuals who answered affirmatively to screening questions asking if they had background or experience in healthcare, had a previous or current diagnosis of mental illness, or were experiencing secondary infertility. In total, 9734 participants were excluded from the screening questions.A total of 266 respondents who agreed to participate were asked to complete a web-based survey on a computer or smartphone. The survey closed after 266 people completed it. Following this, data for this survey were received from an online survey company, which did not include the names, addresses, or e-mail addresses of the participants. The last question of the survey asked participants to “Please choose ‘uncertain’ from the following options” to determine whether they had properly read and answered the questions. From the 266 participants, we excluded nine who chose other than “uncertain” as lax respondents. Three participants who did not meet the eligibility criteria were also excluded; two did not undergo infertility treatment at the time of the survey, and one was pregnant. Finally, the data from 254 individuals were included in the analysis. The sample size for the multiple regression analysis should be approximately 20 times as large as the number of independent variables ,42. ThusThis study was approved by the ethics committee of the University of Tokyo . Informed consent was obtained from all the participants in accordance with the Declaration of Helsinki. In this study, the independent variables were infertility stigma (one variable), sociodemographic factors (six variables), and infertility characteristics (four variables). The dependent variables were anxiety, depression, and psychological distress (one variable for each). Infertility stigma in this study refers to an individual’s perception of stigma and self-stigma, such as feelings of shame and guilt. Anxiety and depression in this study refer to symptoms of anxiety and depression, and psychological distress is the aggregate of anxiety and depression symptoms. The following measures were administered in the survey.The participants were asked about their sociodemographic information, including age, duration of marriage, education, annual household income, occupation, and whether the couple lived with their parents or not. The participants were also asked about their infertility characteristics, including duration of infertility, duration of infertility treatment, causes of infertility, and treatment for infertility.The Infertility Stigma Scale was developed by Fu et al. in 2015 to assess individuals’ perception of stigma (perceived stigma) and feelings of loss of self-esteem, shame, and guilt (self-stigma) . The scaThe Hospital Anxiety and Depression Scale (HADS) was developed by Zigmond and Snaith in 1983 to assess anxiety and depressive symptomatology . HADS ist-test and a one-way ANOVA. After confirming that the distribution of residuals obtained in the model estimated using the Q-Q plot was close to a normal distribution, a multiple regression analysis was performed. We employed a multiple regression analysis using stigma, sociodemographic information, and infertility characteristics as independent variables and anxiety, depression, and psychological distress as dependent variables. Sociodemographic information and infertility characteristics were determined based on previous studies [p < 0.05.Descriptive statistics were applied to determine the sociodemographic information and infertility characteristics of the participants. Categorical variables were summarized as percentages, and continuous variables were presented as means ± SD. We then examined the associations between sociodemographic information, infertility characteristics, stigma, anxiety, depression, and psychological distress by performing a two-sample  studies ,20,21,53The sociodemographic information and infertile characteristics of the participants are shown in p = 0.014) and the duration of infertility treatment (p = 0.002). However, stigma was not statistically significantly associated with education level (p = 0.059).As shown in p = 0.014). Anxiety also had a statistically significant association with the level of education (p = 0.046). Anxiety was highest among participants with junior college or technical college graduate degrees and lowest among participants with graduate school degrees. Depression was not statistically significantly associated with sociodemographic information or infertility characteristics, while psychological distress was statistically significantly associated with living with parents (p = 0.007).The mean values of participants’ scores on the HADS were as follows: 7.9 (SD = 4.3) for anxiety, 8.1 (SD = 3.7) for depression, and 16.0 (SD = 6.9) for psychological distress . As for the prevalence and severity of anxiety and depression, 51.1% were anxious , and 54.0% were depressed . Anxiety was statistically significantly associated with living with parents (p < 0.001) and living with parents  statistically significantly predicted anxiety. The independent variables accounted for 36% of the variance of the dependent variable (adjusted R2 = 0.36).As shown in p < 0.001) and the duration of infertility  statistically significantly predicted depression. The independent variables accounted for 24% of the variance of the dependent variable (adjusted R2 = 0.24).As for depression, stigma  and living with parents  statistically significantly predicted the psychological distress. The independent variables accounted for 39% of the variance in the independent variable (adjusted R2 = 0.39).Concerning psychological distress, stigma , which is higher than that in the Turkish and Chinese studies ,20,53,54.9, whichA univariate analysis showed a significant relationship between the degree of stigma and the duration of infertility and infertility treatment. These results are consistent with those of Chinese studies on stigma and quality of life among women undergoing IVF . This maThe mean values of the participants’ scores of the anxiety, depression, and psychological distress in this study were 7.9 (SD = 4.3), 8.1 (SD = 3.7), and 16.0 (SD = 6.9), respectively. Compared to previous Japanese studies using the HADS , these mean values were all higher . During Univariate and multiple regression analyses showed a significant relationship between living with parents and anxiety. Living with their parents may increase women’s anxiety by making them feel more pressured to have children. Moreover, the pressure may be accentuated with more people staying at home because of the COVID-19 pandemic. A univariate analysis showed no significant association between duration of infertility and depression, but a multiple regression analysis showed that duration of infertility was significantly associated with lower levels of depression. Participants in this study were not selected by strict random sampling from the population. Therefore, it is possible that a sampling bias may have occurred during the inclusion of participants and consequently affected the results of the analysis.Multiple regression analysis in the present study found that stigma was a significant predictor of anxiety, depression, and psychological distress. These results are consistent with those of previous studies ,22,23,25The model of stigma-induced identity threat may explain why stigma is a predictor of anxiety and depression . Women’sTo reduce the self-stigma of women undergoing infertility treatment and the public stigma against infertile women, the following initiatives may be necessary. First, narrative enhancement and cognitive therapy interventions may be needed. These include experimental learning, positive change in experience of self, acquiring cognitive skills, enhanced hope, and coping and emotional change ,64. SimiThe present study has several limitations. First, this study was conducted during the COVID-19 pandemic. Therefore, it was not possible to recruit participants from hospitals and clinics. Because the survey was conducted via the Internet, participants diagnosed with mental illnesses could not be included because of difficulties in providing post-survey psychological care. Since many studies on infertility stigma also remove those diagnosed with mental illness as an exclusion criterion ,21,22,25The present study found that stigma is a strong predictor of anxiety, depression, and psychological distress. Infertile women may be regarded as deviants from the normal pathway of marriage and childbearing, with no escape from such stigmatization. Infertile women may also perceive stigma, appraise their identity as being threatened, fail to cope, and may be anxious and depressed. The results indicate that efforts to reduce infertility stigma such as psychoeducation and cognitive therapy intervention could be essential for reducing anxiety, depression, and psychological distress in women undergoing infertility treatment. Future research should examine the process of generating stress responses due to infertility stigma, and based on this, interventions to alleviate stress-related symptoms such as anxiety and depression are needed.


Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery.Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated.Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively.Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance. Bladder cancer is one of the most common malignant tumors of the urinary system , 2. HighAt present, the risk tables developed by the European Organization for Research and Treatment of Cancer (EORTC) and the Club Urológico Español de Tratamiento Oncológico (CUETO) are widely used in clinical practice , but theIn recent years, radiomics has shown great potential for disease diagnosis and prediction with the continuous development of imaging technology –13. RadiBased on these findings, we can hypothesize that 1) small changes in tissues can be discriminated based on the radiomics features extracted from multiphase CT images, which may predict the recurrence of bladder cancer, and that 2) radiomics combined with clinical pathological information, such as age, gender, tumor grade, tumor staging, number of tumors, and surgical method, may improve the efficacy of bladder cancer recurrence prediction.This retrospective study was approved by the Institutional Review Committee. A retrospective search was performed in the pathology database and radiology database to identify patients with bladder cancer at the First Affiliated Hospital of Soochow University between January 2016 and December 2019. The criteria for inclusion are as follows: (1) complete pathological data are provided; (2) the images of CT urography (CTU) used to evaluate the lesion are complete; (3) CT scan is performed within 30 days before surgery. The exclusion criteria are as follows: (1) CT images are unqualified; (2) lesions of which the boundaries are difficult to define, such as only the lesion considered as abnormal enhancement, without intracavitary lump or bladder wall thickening; (3) clinicopathological data are missing or incomplete Figure 1The clinical information of the patients of the First Affiliated Hospital of Soochow University from January 2016 to December 2019, such as age, gender, histopathological grade, tumor size in bladder cavity, number of tumors, and surgical method, have been collected.All patients were followed up once within the 3–5 months after surgery, then every 6 months for the next 2 years, and annually thereafter. Follow-up content includes the cystoscopy and radiological examination. Recurrence occurs when a bladder tumor reappears in the bladder, prostate glands, urethra, pelvis, or ileum after surgery. The patient’s relapse-free survival (RFS) is recorded from the previous surgery until the date of the confirmed tumor recurrence. During the 2-year follow-up, bladder cancer recurrence confirmed by cystoscope and pathology examination was defined as recurrence group, and the rest were no recurrence groups.via the elbow vein at an injection rate of 3 ml/s. After the locating image was collected at the supine position, plain scanning and three-phase dynamic enhanced scanning were performed for the patient. The dynamic enhanced scanning of the corticomedullary phase, nephrographic phase, and excretory phase was performed after 30, 60, and 150 s, respectively, after the contrast agent was injected. All images were treated with the standard reconstitution method. The interlamellar spacing and layer thickness were both 5–6 mm. In this study, the plain scan, corticomedullary phase, and nephrographic phase will be used for subsequent studies.A Siemens CT scanner (Somatom Definition Flash), Toshiba CT scanner (Aquilion ONE), and Philips CT scanner (Philips Ingenuity) were used. The scanning parameters are as follows: tube voltage 100–120 kVp, automatic tube current in mA/s, FOV 500 mm, and a scanning layer thickness of 5–6 mm. For enhanced scanning, the contrast agent iohexol (350 mg I/ml) is injected http://label.deepwise.com). In the process of ROI delineation in the focal area, automatic delineation was performed based on the partitioning algorithm of the level set at first, and then the radiologist manually corrected the wrong partitioning. If multiple lesions were encountered, only the largest lesions were delineated because the pathology results show the largest tumor within the bladder  completed the image reading of plain scan, corticomedullary phase, and nephrographic phase together without knowing the patient’s condition. In case of disagreement, it was resolved through negotiation. The 3D tumor lesion ROI was semiautomatically delineated with a Deepwise scientific research platform  features of tumor texture, 160 gray-level size zone matrix (GLSZM), 160 gray-level run length matrix (GLRLM) features, 140 gray-level dependence matrix (GLDM) features, and 50 features of neighboring gray tone difference matrix (NGTDM) features. All extracted radiomics features are subjected to Z-score normalization, and the formula for feature normalization is z = (x-mean)/std. Among all the features in each model of the plain scan, corticomedullary phase, and nephrographic phase, the lowest P value was filtered out with the F test for follow-up analysis after calculating the linearly dependent coefficient.The radiomics feature subset was optimized based on 10 random splits of 7:3; the three basic models plain scan, corticomedullary phase, and nephrographic phase as well as two combination models were established with the logistic regression algorithm. The application performance indicators include AUC, accuracy, sensitivity, and specificity to evaluate the predictive performance of its radiomics model. The specific method was used as follows: divide the data into 10 parts randomly, with 7 of them used as the training set in turn, and the remaining 3 as the validation set. The corresponding model prediction probability can be obtained for each test, and the mean of the 10 prediction results is used for the estimate of accuracy; a total of 10 times of 7:3 random split verifications were performed, and the mean value of each efficiency index prediction was calculated as the final evaluation result of model efficiency. The AUC values of the five models were compared with Delong’s test, and the radiomics model with higher performance was selected for subsequent analysis.First, the optimal radiomics features screened by the radiomics model with higher performance were put into a logistic model to obtain the regression coefficients, and then the Rad-score of each patient can be calculated with the linear combination of these features and their corresponding regression coefficients. Finally, the clinical risk factors and Rad-score were screened through Cox univariate analysis, the variables with significant statistical differences were retained (P < 0.05), and Cox multivariate analysis was further performed to determine the independent risk factors (P < 0.05). Then, the radiomics-clinical model for predicting recurrence of bladder cancer was built with these independent risk factors. The performance was evaluated with the calibration curve and AUC of the training group and the validation group. Finally, the clinical benefit of the radiomics-clinical model was further validated using decision curve analysis.Statistical analysis was performed using SPSS 25.0 and R 4.1.0 software. The counting data were compared with the chi-square test. For measurement data, the Kolmogorov–Smirnov test was performed to test whether it is normal distribution at first. If the normal distribution is conformed, an independent sample t-test was conducted for comparison, which was expressed as the mean ± standard deviation. If the normal distribution is not conformed, the Mann–Whitney U test will be used and expressed as mean . When P < 0.05, the difference is statistically significant.A total of 183 patients with bladder cancer who met the inclusion and exclusion criteria were included in this study, including 160 men and 23 women; the mean age was (67.41 ± 11.44) years.During the follow-up on December 31, 2021, there were 76 patients with recurrence (Recurrence group) and 107 patients without recurrence (No recurrence group). The median RFS for all recurrent patients was 10.5 months, with a range of 1–24 months, while the median RFS for all non-recurrent patients was 33 months, with a range of 24–67 months. The clinical data of patients of the recurrence group and no recurrence group are shown in The average ICC was 0.897 for the plain scan model features, 0.928 for the corticomedullary phase model features, and 0.860 for the nephrographic phase model features, which showed good consistency among the observers.Based on the original images and the filtered images, 614 features were removed and a total of 296 features were retained in the 910 features of each model of plain scan, corticomedullary phase, and nephrographic phase after the linearly dependent coefficient was calculated. The 16 features with the lowest P-values were filtered out using an F-test for the follow-up analysis.After the radiomics feature screening was completed, the radiomics feature subset was optimized based on 10 times of 7:3 random splits, and the three basic models plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were established with the logistic regression algorithm. The AUC of the plain scan, corticomedullary phase, nephrographic phase, and the corticomedullary phase + nephrographic phase and plain scan + corticomedullary stage + nephrographic stage models for predicting recurrence of bladder cancer within 2 years after surgery were 0.594, 0.678, 0.716, 0.721, and 0.749, respectively Figure 3Fourteen optimal radiomics features were retained in the combination model of plain scan + corticomedullary phase + nephrographic phase constructed using the logistic regression algorithm Figure 4After selecting 14 optimal radiomics features, the logistic model was used to linearly combine these features into one feature, a rad score formula was established, and the Rad-score of each patient was calculated. Rad-score and clinicopathologic risk factors were analyzed by using Cox univariate and multivariate proportional hazard models, as shown in High recurrence rate is a significant epidemiological feature of bladder cancer . CystoscWith the continuous development of image technology, imaging diagnosis has played an increasingly important role in the early diagnosis of diseases, curative effect monitoring, and prognosis evaluation. In recent years, MRI has been recognized as an important tool for assessing bladder cancer , becauseUp to now, the application value of CT radiomics technology in the diagnosis and treatment of bladder cancer in relevant studies has been reported. Regarding the prediction of bladder cancer grade, Zhang et al.  includedRelatively more studies were performed on CT radiomics in the aspects of bladder cancer grade, staging, invasion, and efficacy, but few related studies on predicting recurrence risk of bladder cancer based on CT radiomics were reported. For example, in the preliminary study conducted by Zhang et al.  based onNevertheless, the abovementioned literatures are all based on nephrographic phase CT images for bladder cancer research, and few studies are conducted based on multiphase CT images; whether the performance of prediction model can be improved is still unknown.Therefore, in this study, the logistic regression algorithm was used to construct three basic models plain scan, corticomedullary phase, and nephrographic phase of the single-phase CT image, as well as two combination models of multiphase CT images, and evaluate the performance of the training group and validation group with the best performance of the combination model of plain scan + corticomedullary phase + nephrographic phase. The AUC is 0.811 for the training group and 0.749 for the validation group, indicating that multiphase CT images are feasible in predicting the recurrence of bladder cancer within 2 years after surgery. However, the efficacy of the combination model was still lower than 0.75, indicating that the radiomics model alone has limited application value in predicting the recurrence of bladder cancer within 2 years after surgery.Therefore, this study also combined the clinicopathologic risk factors to construct a radiomics-clinical model to explore whether combining clinicopathologic risk factors could increase the efficacy in the prediction of the 2-year recurrence of bladder cancer. Some recent studies have shown , 30 thatHowever, our study has the following limitations. First, since this study is retrospective, there may be inherent bias in the limited patient cohort from a single institution. Second, the sample size is relatively small, and the data from multiple centers are required to externally validate the overall performance of the model. Third, small follow-up duration is another limitation, since bladder cancer could be considered as a chronic disease; the follow-up should be extended. More importantly, due to the incomplete clinical database, this study did not include other potentially useful predictive factors, such as HER2 , Ki67 2, and othIn conclusion, our results preliminarily demonstrate that radiomics features extracted from multiphase CT images, combined with important clinicopathologic risk factors, can predict the 2-year postoperative recurrence of bladder cancer. It still deserves optimization and further in-depth analysis with longer follow-up and wider sample size, which may be helpful for the early identification of patients with high recurrence risk, and for assistance and guidance of the formulation of individualized diagnosis and treatment plans in the future.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.JQ and SG collected and analyzed the data. JQ drafted the manuscript. ZL reviewed the pathological findings and provided the clinical data and followed up the patients. LY and SH revised the manuscript for important intellectual content. JY, HD and HS designed and supervised the study. All authors contributed to the article and approved the submitted version.This work was supported by the Jiangsu Medical Association Roentgen Imaging Research Special Fund Project: SYH-3201150-0017(2021012).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Overall, 190 patients with oral mucosal lesions  and 49 patients with normal oral mucosa (NOM) were enrolled between January 2019 and March 2021. The FI of the images was analysed using image analysis software. After establishing regions of interest for SCC, non-SCC, and NOM, the average FI, standard deviation (SD), and CV were compared. There was a significant difference in the average FI for all pairs of comparisons. The SD was not significantly different between the SCC and NOM groups (p = 0.07). The CV differed significantly for NOM (p < 0.001) and non-SCC groups (p < 0.001) relative to the SCC group but was not different between NOM and non-SCC groups (p = 0.15). Univariate analysis of SCC and non-SCC groups showed significant differences for all factors, except age. However, multivariate analysis showed a significant intergroup difference only in the CV (p = 0.038). Therefore, analysing the CV in FV images of OML may be useful for the diagnosis of oral cancer.We evaluated whether fluorescence intensity (FI) and its coefficient of variation (CV) can be used to diagnose squamous cell carcinoma (SCC) through IllumiScan Oral cancer is relatively easy to diagnose because it is more easily visualised and palpated than other organs. However, differentiating oral cancer from a variety of mucosal lesions, such as leukoplakia, erythroplakia, lichen planus, and refractory stomatitis, is difficult. Diagnoses of oral squamous cell carcinoma (SCC) and oral potentially malignant disorder (OPMD) are based on a combination of biological staining with iodine  and toluHowever, this method is unreliable because it does not access deep cells . It has Since the 1980s, FV devices have been used to detect oral cancer . The fluIn cancer tissues, energy metabolism switches from oxidative phosphorylation to aerobic energy production , decreas®  was the first commercial auto-focus imaging device approved for oral use in 2008 [®    is an FV, Japan) . This de, Japan) . Further® and VELscope®, have been reported to have high sensitivity but low specificity [However, the auxiliary tools for OML detection, such as IllumiScancificity . This is® images between SCC and non-SCC tissues.Furthermore, keratinised areas have enhanced fluorescence intensity (FI). Keratinisation is often present on cancer surfaces, including that in SCC, and in epithelial dysplasia. This causes an improved FI and obscures the FVL of these lesions , making The advantage of autofluorescence devices is that they enable the direct visualisation of tissue fluorescence; thus, there are many reports of visual recording and subjective judgment ,22. For ® images to diagnose SCC.In this study, we attempted to measure the luminance value of the fluorescence of OML, quantify it, and analyse it. The luminance value of healthy mucosa in the same area as the lesion was measured and compared with that of the lesion. Furthermore, we evaluated whether FI and its variation, i.e., the coefficient of variation (CV), could be used when analysing IllumiScanOf the patients who visited the Department of Oral Oncology at Hiroshima University Hospital from January 2019 to March 2021, 239 patients were included in the study. Of the 239 cases, 190 cases of OMLs were identified during conventional oral examination under white light and were found to have features compatible with the clinical suspicion of OPMD or early-stage SCC. Inclusion criteria were patients who gave informed consent for imaging, were imaged prior to biopsy, and had a definitive diagnosis confirmed by biopsy. Patients with lesions of T3 or higher as per the International Union Against Cancer (UICC) 8th edition TMN malignancy classification and patients without adequate images due to aperture defects were excluded from this study to reduce the burden on patients [® in a dark room. The brightness of the irradiation light was maintained and did not vary between cases. The distance between the IllumiScan® lens and lesion was approximately 30 mm.The lesions were recorded using a digital camera  under white light and were then photographed using IllumiScan® images were transferred to a computer hard drive and analysed using ImageJ version 1.53 . Each image was divided into RGB components , and a black-and-white image .® 16 software . The Dunn’s test was used for non-parametric comparisons between all pairs of NOM, SCC, and non-SCC groups, with regard to luminance, SD, and CV. Fisher’s exact probability test was used to compare sex differences. Multivariate analysis was performed using nominal logistic analysis. A p-value < 0.05 was considered statistically significant. The area under the curve (AUC), threshold, sensitivity, and specificity were calculated for average FI, SD, and CV, using receiver operating characteristic (ROC) curve analysis. The cut-off values were defined based on the Youden index.Statistical analysis was performed using JMPThe clinicopathologic characteristics of the patients are shown in p < 0.001). The FI was significantly lower in the SCC group than in the non-SCC group (p = 0.013). The SD in the non-SCC group was significantly lower than in the NOM and SCC groups (p < 0.001), but the SD in the SCC and NOM groups were similar (p = 0.071). CV was significantly higher for the SCC group than for the other two groups (p < 0.001) but similar between the non-SCC and NOM groups (p = 0.15).The relationships between NOM, SCC, and non-SCC for average FI, SD, and CV are shown in p = 0.038, Odds ratio = 13, 95% CI: 1.09–154.7) was independently significant . The AUC of the SD was 0.63. Assuming an SD cut-off of 11.2, the sensitivity and specificity were 83.1% and 65.6%, respectively (p < 0.0001). The AUC of the CV was 0.84. Assuming a CV cut-off of 1.70, the sensitivity and specificity were 72.9% and 78.3%, respectively (p < 0.0001).The ROC curves for differentiating SCC from non-SCC and NOM are shown in ® to observe OMLs and investigated image evaluation methods for the detection of oral cancer. While the SD was not significantly different between the SCC and NOM groups, the CV of SCC differed significantly from that of NOM (p < 0.0001) and non-SCC (p < 0.0001) but not between NOM and non-SCC (p = 0.15). In multivariate analysis, only the CV (p = 0.038) was a significant factor in distinguishing SCC. Therefore, the analysis of the CV in FV images of OML obtained with an FV device may facilitate oral cancer diagnosis.We obtained FV images with IllumiScan® induces autofluorescence of the NOM and allows image-based detection of the attenuated fluorescence of cancerous tissue and epithelial dysplasia. The imaging is non-invasive and repeatable. It is considered easy to operate and does not require specialised training. However, the use of the device by unskilled users can lead to high false-positive results because of misinterpretation. Furthermore, it has been reported that the device should not be used at the screening stage as false positives may lead to unnecessary referrals and biopsies [Tissue autofluorescence is used for diagnosis as a screening for precancerous lesions in the lungs, cervix, skin, etc. . In thisbiopsies ,28. Howebiopsies . Some rebiopsies .Therefore, this technique may be useful as a clinical diagnostic aid to detect SCC and other lesions that are often missed and could guide biopsy. Further, it could be used not only for cancer screening but also for long-term follow-up of precancerous lesions, preoperative determination of the resection area , and posMore than 15 years have passed since the introduction of FV devices, and they are now approved and marketed as medical devices. It has been reported that the oral mucosal observation system is an excellent tool for detecting OMLs ,33 and c® did not improve, so, it was suggested that the 2-week follow-up should not be performed for high-risk lesions [Molecules that can generate autofluorescence with a certain light include tryptophan, porphyrins, collagen cross-linkers, elastin, NADH, and FAD. Conversely, they also lose fluorescence and appear darker. Fluorescence loss may occur because of increased metabolic activity of the epithelium, disruption of collagen cross-links, increased tissue blood volume, and the presence of pigment. Haemoglobin, pigmentation, and metal tattoos are observed as FVL because they absorb light. Benign lymphoid tissue aggregates due to the prominent vascular melanin seen in mild trauma and inflammation and the absence of collagen and leukocytes can be observed as FVL due to the lack of autofluorescence . In this lesions . InflammBecause FI depends on various mucosal conditions, it has been reported that FV systems have high sensitivity but low specificity . This is®  noted that the VELscopesy sites . Additiosy sites . The comsy sites . HoweverThere are large individual differences in FI values, even in the NOM. It has been reported that the FI of SCC in keratinised mucosa is significantly lower than that in non-keratinised mucosa . AlthougThe NOM group used in the present study was significantly younger than the comparison group because of the cooperation of younger volunteers; however, no comparison of FI values or CV of NOM by age was made, which is also a limitation of this paper. The site of imaging, age, and individual differences in the FI values of healthy mucosa will be investigated in the future.® and measured their FI values. Our data indicated that by analysing the FI and its CV obtained with the IllumiScan®, a differential diagnosis of SCC can be made among OMLs.In this study, we photographed OMLs and NOM using IllumiScan


To build machine learning models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the patient's vital signs.This retrospective observational cohort study enrolled septic patients from five emergency departments (ED) in Taiwan. We adopted seven variables, i.e., age, sex, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature.Among all 353,253 visits, after excluding 159,607 visits (45%), the study group consisted of 193,646 ED visits. With a leading time of 6 h, the convolutional neural networks (CNNs), long short-term memory (LSTM), and random forest (RF) had accuracy rates of 0.905, 0.817, and 0.835, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.840, 0.761, and 0.770, respectively. With a leading time of 48 h, the CNN, LSTM, and RF achieved accuracy rates of 0.828, 0759, and 0.805, respectively, and an AUC of 0.811, 0.734, and 0.776, respectively.By analyzing dynamic vital sign data, machine learning models can predict mortality in septic patients within 6 to 48 h of admission. The performance of the testing models is more accurate if the lead time is closer to the event. It can be life-threatening if not recognized and treated promptly . DespiteIn recent decades, medical artificial intelligence (AI) has been used to achieve clinical diagnoses and suggest treatments. A few examples where AI has shown promise for clinical diagnoses include diabetic retinopathy screening , skin leMachine learning methods can predict in-hospital mortality in sepsis patients in an intensive care unit (ICU) . At the However, most previous prediction models for mortality require a large number of variables, including the underlying disease, laboratory data, and clinical parameters. The aim of our study was to build ML models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the vital sign.This is a retrospective observational cohort study conducted from January 1, 2006 to December 31, 2017. The study was approved by the IRB Review Board of the Chang Gung Medical Foundation  in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. Informed consent was not required owing to the retrospective nature of the study.We used data provided by the Chang Gung Medical Center, including five EDs that belonged to a single healthcare system and were geographically dispersed nationwide in Taiwan. Sepsis patients were extracted from the electronic database records of the Chang Gung Medical Center under the following conditions: 1) The age of the patient was over 17 years, (2) blood culture was obtained, and (3) antibiotics were prescribed in medical order. Sepsis patients were defined according to the Third International Consensus Definition of Sepsis (Sepsis-3) definition, that was an acute change in Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more consequent to the infection  the length of the hospital stay was >3 days (2) the patients recorded less than three times when they stay in hospital, and (3) patients with incorrect data and format. The selection process and sample numbers were listed in the The outcome was divided into two results: positive instances in which patients died in the hospital and negative instances in which patients survived. After cleaning problematic data such as those containing less than one record for every variable, and those having an error in terms of format, the number of positive instances was 19,434, and the other negative instances numbered 194,646. To construct a mortality prediction tool, we adopted five vital signs: systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), respiratory rate (RR), and body temperature (BP). Patient age and sex were also included. Vital signs were selected as objective predictor variables because they are routinely and frequently collected, regardless of the clinical situation, and the values are rarely affected by the examiner.There were two different types of outcomes in this research. For these two outcomes, we extracted negative instances from all subjects who survived and positive instances from all subjects who died in the hospital. The data for up to 6–48 h prior to death were extracted as a positive instance, and the data for up to 6–48 h prior to survival or discharge were extracted as a negative instance, as shown in We mainly focused on four different lead times  prior to the results, and we used machine learning and developed four models to predict the results k hours in advance.The subsequent cleaning process ensured that the electronic data were ready for analysis and did not contain any errors. First, we removed problematic records. Second, to resolve the problems of missing data, the measured value of the vital sign variable was forward-filled following an initial measurement until the next available measurement. The choice was based on the clinical insight that measurements are taken more frequently during times of hemodynamic instability and less frequently when the patient appears stable. Third, the records were converted into z-scores using the computed means and standard deviations. These vital sign variables were normalized to  for comparison. Fourth, the vital sign normalized values are divided into 255, according to the degree of the divided results, converted into grayscale, and transformed into an image for each patient.To build appropriate models and develop an early warning system (EWS), we set the training, validation, and testing sets to a ratio of 6:2:2. The early warning system models developed using convolutional neural networks (CNN) was labeled as EWS_C, using long short-term memory (LSTM) was labeled as EWS_L, and using random forest (RF) was labeled as EWS_R, respectively.CNNs are the most mature tools in graphical process in machine learning. It is a class of deep feed-forward artificial neural networks, and a CNN architecture is formed by a stack of distinct layers that transform the input volume into an output volume through a differentiable function . A few dLSTM is effective for capturing the underlying temporal structures in time-series data. It consists of the following three gates: forget, input, and output gates. These three gates interact to control the flow of information. LSTM builds memory by feeding the previous hidden state as additional input in the subsequent step. This makes the model particularly suitable for modeling dynamics in vital sign data, which has a strong statistical dependency between medical events over the time intervals. LSTM enables the network to maintain the previous information of the hidden states as internal memory . The netRF is an efficient, multi-class approach that is able to handle large attribute spaces, and has been widely used in several domains including real-time face recognition and bioinformatics . RF is aThe flow of the applied research method is described in the previous section. For the reliability and stability of our models in this research, our research adopted two validation methods. The first validation, we use k-fold cross-validation methods considered our sample sizes, we adopted k-fold = 5 for model tuning and yield a satisfying generalization performance. In k-fold cross-validation, we randomly spilt the training dataset in k folds without replacement, where k-1 folds are used for the models training and one folds is used for testing. This procedure is repeated k times and we obtain k models and performance estimates. Then, we calculate the average and 95% confidence interval performance of the models based on the different, independent folds to obtain a performance estimate that is less sensitive to the sub-partitioning of the training data. We listed the cross-validation results of EWS_C, EWS_L, and EWS_R in the The second validation part was reserved the data of 2017 as extra validation part. The EWS_C, EWS_L, and EWR_R were validated the data of 2017, and the results were listed in the validation part in the To accurately build the early warning system model, we compared it with other standard machine learning algorithms, i.e., CNN, LSTM, and RF. We call these models EWS_C, EWS_L, and EWS_R, respectively. The model performance was assessed based on discrimination using the precision, recall, accuracy, receiver operating characteristic (ROC) curve, and derived area under the ROC curve (AUC).The symbols in this research included 28,530 positive instances and 194,646 negative instances. We also excluded (1) records from <72 h after admission to the ED, (2) cases with fewer than three records, and (3) incorrect data or an improper format. To summarize, k = 6, 12, 24, and 48 h in the numbers of excluded and included samples are listed in We extracted the vital signs of the patients in k  prior to the time of death as a positive instance. By contrast, we extracted the vital signs of the patient k (h) prior to the time of survival as a negative instance. The smaller the lead time before the result is, the larger the number of patient cases. For example, when k = 6 h, there are 19,434 positive instances and 194,646 negative instances; in other cases, when k = 48 h, there are 17,123 positive instances and 125,102 negative instances.We compared the models among these three methods: CNNs, LSTMs, and RF. To distinguish which model is more accurate and reliable and help doctors make decisions, we also compared four different lead time models, i.e., 6, 12, 24, and 48 h models.In the first part, we used the CNN-based algorithm under different lead-time models for EWS_C. With EWS_C, the precision, sensitivity, and accuracy values of the training and validation models are summarized in According to the validation models for EWS_C, the records show that the precision among these four different lead time models is above 0.85. The sensitivity was above 0.75 for EWS_C6 and EWS_C12, and the sensitivities for EWS_C24 and EWS_C48 were above 0.7. In addition, the accuracy for EWS_C was >0.8. In In our study, machine learning models were used to predict the mortality in septic patients 48 h prior to death. The AUC of the testing models for a 48-h lead time is 0.83, 0.74, and 0.77 with the EWS_C, EWS_L, and EWS_R models, respectively. In general, the performance of the testing models is more accurate if the lead time is closer to the event. The AUC of the testing models under a 6-h lead time could achieve values of 0.84, 0.75, and 0.78 for EWS_C, EWS_L, and EWS_R, respectively. For all lead times, we found that the AUC of EWS_C had the best model performance among the ML models, with an AUC within the range of 0.82–0.85.A wide array of rule-based scoring systems was developed to assess the severity of illness and risk stratification. Examples frequently used as severity assessment tools in the ICU are the simplified acute physiology score (SAPS) II , acute pThese EWSs were created mostly based on physiological measurements and clinical observations, including vital signs, level of consciousness, laboratory data, and other metrics, depending on the selected modification tool. Thus, most of these scores are complex or disease-specific, leading to a poor early recognition of septic patients at risk of deterioration. Moreover, the differences between the observed and expected mortality may also be caused by inadequate diagnostic data, unreliable Glasgow coma scale (GCS) score assessment, regional differences, and changes in the effectiveness of therapy over time , 34.With the progress and development of big data techniques, machine learning methods have attracted research attention in the past decade. Zhang et al. found that the least absolute shrinkage and selection operator technique achieves a good discrimination and calibration for mortality prediction in patients with severe sepsis . Using oSeveral studies highlight the value of dynamic vital sign changes for building predictive models. A pilot study used physiomarkers to generate 52 highly ranked features and build an eXtreme Gradient Boost classifier that could predict post-liver transplant patients 12 h before developing sepsis . AnotherHowever, using only physiological data, previous studies mostly focused on predict sepsis event. By contrast, our study focused on predict mortality. We included only seven input parameters in our study, including age, sex, and vital signs , available from the moment of triage to any time during hospitalization. Using data available in the ED in real time, artificial intelligence can accurately predict mortality in septic patients 6–48 h prior to clinical recognition.The proposed method has several advantages. First, vital signs had clear-cut values and were obtained through machine measurements, which reduced the expert judgment and limited variations in the healthcare providers. In addition, models that require hundreds of variables may lead to difficulty in encoding the databases and may have more missing values or data errors. Instead, we attempted to develop an uncomplicated model that requires simple input parameters that are routinely collected during daily practice. A simplified tool would be more easily implemented in resource-limited ED settings. Furthermore, the data used in our model were widely available in clinical practice. Lukaszewski et al. reported that neural networks can correctly predict patient outcomes of overt sepsis prior to clinical diagnosis with high sensitivity and selectivity  . BecauseOur study has several limitations. First, this was a retrospective study conducted in Taiwan. The sample was homogeneous and may have been subject to local practices, limiting its generalizability to other ethnicities. Second, we did not compare all available ML models and scoring systems, or their variations. There are hundreds of different ML models and variations; therefore, a comprehensive study is unfeasible. Application of the developed ML model to other datasets or populations requires a further clinical evaluation.This study contributes to clinical areas using machine learning in-hospital mortality prediction models for sepsis patients in the ED. By analyzing dynamic vital sign data, machine learning models can predict mortality in septic patients within 6–48 h of admission. The CNN achieves the best model performance in comparison to the LSTM and RF approaches. In general, the performance of the testing models is more accurate if the lead time is closer to the event.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Chang Gung Medical Foundation. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.CK and C-MS conceived the study and assumed responsibility for the paper as a whole. I-MC and C-HL managed the data and including quality control. C-TK, F-CC, and C-CC provided statistical advice on the study design and analyzed the data. C-YC and C-TK chaired the data oversight committee. C-YC drafted the manuscript and all of the authors contributed substantially to its revision. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Carpal tunnel syndrome is a neuropathic disease. It is one of the most frequent musculoskeletal pathologies affecting the upper limbs. One of most frequently used non-surgical treatments is corticosteorids. There are several alternatives for corticosteroids administration. One of them is phonophoresis, this being an effective and painless method of treatment. A systematic review and meta-analysis have been conducted over the use of phonophoresis with corticosteroids for the treatment of carpal tunnel syndrome compared to other non-surgical treatment methods. Keywords from Medical Subjects Headings (MeSH) were used in the following databases: Wos, Scopus, CINHAL, SciELO and PeDro. A total of 222 potentially relevant articles were retrieved. Eleven articles analysing the efficacy of phonophoresis with corticosteroids in reducing pain symptoms in individuals with carpal tunnel syndrome were included, 10 of which were used to conduct the meta-analysis. A conclusion could not be reached as to the application of phonophoresis with corticosteroids being better than other treatment methods, except for the perception of pain and an improved motor and sensory nerve conduction in cases of mild to moderate carpal tunnel syndrome. Carpal tunnel syndrome (CTS) is a neuropathic disease that occurs at the wrist area when the median nerve is affected. It is one of the most frequent musculoskeletal pathologies affecting the upper limb . It featIn spite of the systematic reviews that exist on this subject ,13,14,15A systematic review and meta-analysis was conducted and recorded in PROSPERO (CRD42022338100) using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines . Search Keywords from Medical Subjects Headings (MeSH) were used in the following databases: Web of Science (WoS), Expertly curated abstract & citation database (Scopus), PubMed database, Cumulative Index to Nursing & Allied Health Literature (CINHAL), Scientific Electronic Library Online (SciELO) and Physiotherapy Evidence Database (PEDro). The following terms combined with Boolean operators were used: “Phonophoresis”, “Sonophoresis”, “carpal tunnel syndrome”, “Median Neuropathy” and “Treatment(s)”. This search was restricted to controlled clinical trials. Filters were not applied when searching .The eligibility criteria were established based on the PICO  model: (Two reviewers (F.J.M.V. and G.G.M.) conducted the studies selection process, eliminated duplicates, and reviewed and systematically abstracted data. A third reviewer (V.P.C.) helped to reach an agreement in case of dispute. For this, the Mendeley reference manager version 1.19.8.0 was used.Data abstracted from each of the studies included were: (i) authors and publication date; (ii) subjects’ related data ; (iii) data related to assessment ; (iv) data related to the intervention with phonophoresis ; (v) parameters of techniques used, duration and frequency of the intervention; (vi) achieved outcomes.For methodological assessment purposes, the PEDro  scale wa2 value was above 50% the random effect model was used. The effect size was obtained through the Z value in the Test for overall effect and its p value . Sensitivity analysis has been conducted for all studies included in all the variables subject to analysis. To verify the homogeneity of studies, the value for p of Heterogeneity  was used. The risk of bias was estimated through the Begg and Egger tests, with the epidemiological data analysis programme (EPIDAT) [The IT tool Review Manager (RevMan) was used to conduct the meta-analysis. Version 5.4. The Cochrane Collaboration, 2020 [(EPIDAT) , in addiA total of 222 potentially relevant articles were retrieved . Eleven The total sample included 434 subjects. The average was 39.5 subjects (a bracket between 31 and 54 subjects). Only four studies ,23,24,25In relation to the comparison of different alternative therapies, treatment based on phonophoresis with corticosteroids was compared to local corticosteroid injection ,24,25,29Concerning the tools used for diagnosis purposes, all of the studies included the Boston Carpal Tunnel Questionnaire (BCTQ) scale  which coThe assessment of different diagnosis tools was conducted before and after the intervention. In some studies a subsequent evaluation after four weeks ,31, two Steroideal active ingredients enhancing phonophoresis efficacy compared to other treatment methods were: betamethasone valerate 0.1% ,29 compaThe treatment was applied for four weeks ,32, or d2 in all of the studies except in two of them in which it reached 1.5 W/cm2 [2 [With regard to the parameters used for the application of phonophoresis with corticosteroids, most of the studies ,27,31,32.5 W/cm2 ,29 and aW/cm2 [2 . The timW/cm2 [2 ,26,30,31In relation to quality from a methodological perspective, the average resulting from all the studies is considered as good (7/10) .Twelve meta-analyses grouped by variable were made .These results show that no statistically significant effect arises from phonophoresis in any of the analysed variables. In the case of variables such as Pain , LMMotor  and LMSensor , there is a positive trend for phonophoresis with certain values remarkably close to significance.The sensitivity analysis for all the studies in every analyzed variable does not significantly modify the outcomes, therefore, our analysis is solid. As for LMSensor, when removing the study by Aygül, R. et al. I, the outcome changes in favour of phonophoresis. However, it should be noted that this outcome was already remarkably close to significance .Studies analysed for the variables pain, BCTQ Sensorial, BCTQ Total, LMMotor, LMSensor, Grip strength and CNVS were considered homogeneous. The rest of the studies for the remaining variables were heterogeneous .The prevalence of women observed during this review, as well as the subjects’ range age, is in line with the epidemiological studies carried out in the past ,37,38,39Regarding the bilateral occurrence of the pathology, the average ratio resulting from this review is slightly lower than that stated in other searches ,39. ThisAlthough all of the studies included in this review state the efficacy of phonophoresis when applied with corticosteroids in carpal tunnel syndrome, a consensus has not been reached as to its therapeutical preeminence compared to other types of treatment.Therefore, when comparing the application of phonophoresis with corticosteroids to other methods, this is not decisive concerning an improvement of the studied variables. When compared to injections, it is observed that electrophysiologic parameters and pain improve with injections, but this is not the case when applying phonophoresis. This is in contrast with Soyupek F et al. , who obsWhen comparing this application to iontophoresis, it is observed that phonophoresis provides higher benefits , and morClinical symptoms and functionality improve both after the application of corticosteroids via phonophoresis and the application of phonophoresis without any drug.Concerning evaluation tools, much heterogeneity exists. The reason may be found in the lack of a standardised protocol for this purpose ,41. ThreAs for the active ingredients, there is also a great heterogeneity. Anti-inflammatory and analgesic actions of dexamethasone valerate have been already described in other studies ,59. WhenBakhtiary AH et al. ,31 descr2), which is in line with the outcomes observed during a systematic review conducted by Huisstede BM et al. [With regard to the parameters used in phonophoresis, our outcomes do not allow us recommend the use of 1 MHz frequency instead of 3 MHz . This maM et al. .The meta-analysis proves that, although no statistically significant relation is established between the use of phonophoresis compared to other kinds of drug administration, there is indeed a significant trend in the case of pain, LMMotor and LMSensor. This suggests that using phonophoresis may be beneficial in the case of patients suffering from pain or for whom nerve conduction, both motor and sensory, is altered.No other systematic reviews or meta-analysis have been found for comparing our outcomes.A systematic review and a meta-analysis have been conducted by following a specific methodology. Sensitivity analysis shows stability for the final measure obtained. All the studies found have been reviewed without using any filters. Clinical trials have been used showing a medium-high methodological quality.The literal translation of studies drafted in Turkish or Persian could indicate a general or biased overview of the global construction the authors wished to convey.A differentiation in syndrome severity could not be realised, as certain authors did not state it ,28,29,30For the analysis of some variables, the number of studies were scarce. Although variables could be categorised into groups, it was observed that some of them were measured on different nerves, which could have an impact on extremely specific assessments. We recommend the use of updated clinical practice guidelines as far as the electrophysiologic studies is concerned , and a lThe application of corticosteroids by phonophoresis do not seem better that other treatments according with the results, except for the perception of pain and an improved motor and sensory nerve conduction in case of mild to moderate carpal tunnel syndrome.Concerning the evaluation tools, for the active ingredients and phonophoresis parameters, much heterogeneity does exist. For future studies we recommend these variables to be homogenised in order to achieve more conclusive outcomes and parameters to conform to the treatment objectives.


TAT), and 4-hydroxyphenyl-pyruvate dioxygenas (HPPD) genes, respectively. There are still unclarified aspects in their clinical presentations, mutational spectrum, and genotype–phenotype correlation.Different types of non-hepatorenal tyrosinemia are among the rare forms of tyrosinemia. Tyrosinemia type II and III are autosomal recessive disorders caused by pathogenic variants in the tyrosine aminotransferase . The most common variant is p.Arg57Ter, causing a splicing defect, and resulting in premature termination of translation, which was found in 10 patients from 3 families. In HPPD gene, eleven variants in 16 patients have been reported including 7 missense variants, 2 nonsense variants, 1 splice defect, and 1 frameshift variant so far. All variants are unique, except for p.Tyr160Cys, which is a missense variant found in two different patients. Regarding genotype–phenotype correlations, in 90% of tyrosinemia type II patients, positive clinical and biochemical correlations with a detected variant are observed. In HPPD gene, due to the small number of patients, it is not possible to make a definite conclusion.This is the first large review of variants in TAT and HPPD, highlighting the wide spectrum of disease-causing mutations. Such information is beneficial for the establishment of a privileged mutation screening process in a specific region or ethnic group.The online version contains supplementary material available at 10.1186/s13023-022-02579-0. Catabolism of amino acids supplies necessary nitrogen for the synthesis of crucial compounds like neurotransmitters, hormones, and energy for the cell. The liver and kidneys are the main tissues for this enzymatic pathway that its involvement depends on the nature of the amino acid. Tyrosine is a non-essential amino acid, derived directly from diet or tissue proteins or the hydroxylation of phenylalanine, which is an essential amino acid. It is a precursor in the synthesis of catecholamines, thyroid hormones, and melanin . Defect The two least common deficiencies of tyrosine metabolism pathway are non-hepatorenal tyrosinemia, which are called tyrosinemia type II and III. Tyrosinemia type II OMIM 276600) or oculocutaneous tyrosinemia occurs secondary to a deficiency of the cytoplasmic enzyme, tyrosine aminotransferase (TAT: EC 2.6.1.5) 76600 or .Tyrosinemia type III (OMIM 276710) is the rarest type of deficiency in the tyrosine metabolism pathway due to a lack of enzyme 4-hydroxyphenylpyruvate dioxygenase (HPPD). The human 4-HPPD gene is located at 12q24-qter and contains 14 exons, which encode a protein containing 392 amino acids. The 4-HPPD enzyme is expressed mainly in the liver and kidney. This disorder is autosomal recessive [TAT and HPPD genes and their corresponding manifestations.Regarding recent developments in the management of these patients, we think that it is crucial for clinicians and healthcare providers to be informed about metabolic diseases, which would be potentially beneficial for current and future mutation-targeted therapeutic options. There are some reports about Tyrosinemia type II and III, however, no clear analysis and comparison between mutations (genotype), and their phenotype has been reported so far. The objective of this review was to identify and summarize the reported mutations in the TAT from 42 families (74 patients) have been described to cause tyrosinemia type II . No information about the epidemiology and molecular defects in tyrosinemia type III patients from North and Central America, Africa, Australia, and the Oceania continent are available.The correlation between genotype and phenotype is described as the possibility of an association between a specific mutation and/or class of mutation with a special clinical abnormality. Therefore, finding genotype–phenotype correlations in inherited metabolic disorders as rare diseases are difficult and complicated by several factors, such as the absence of large cohorts of patients for analysis, the large phenotypic heterogeneity associated with the same mutation, and a high proportion of private mutations . The greData on clinical and biochemical results of tyrosinemia type II were available for 70 patients. We have identified among them, only 7 patients had elevated levels of tyrosine along with detected pathogenic variants, however, clinical manifestations were asymptomatic  for future carrier and prenatal screening. It would be useful for clinicians to focus on specific variants of definite regions, which facilitates the targeted detection of disease. Although not all cases of non-hepatorenal tyrosinemia are described in the literature, establishing a genotype–phenotype correlation is difficult. Although the pathophysiology of non-hepatorenal tyrosinemias is not completely explained, it seems that multicenter collection of data and further studies on these patients are necessary to understand the consequences of these deficiencies, the mechanisms of injuries, and the long-term outcome of these patients.Additional file 1. Table 1. Summary of reported patients with tyrosinemia type. Table 2. Summary of reported patients with tyrosinemia type III.