TTN) mutations, whether the TTN gene mutation may be a predictor, and its impact on mortality and survival in these patients. A total of 585 samples from patients with ovarian serous cystadenocarcinoma were collected from The Cancer Genome Atlas and PanCancer Atlas through the cBioPortal for analysis of social, genetic, and histopathological factors. Logistic regression was used to investigate whether TTN mutation could be a predictor, and the Kaplan–Meier method was applied to analyze survival time. TTN mutation frequency did not differ between age at diagnosis, tumor stage, and race, and was related to increased Buffa hypoxia score (p = 0.004), mutation count (p < 0.0001), Winter hypoxia Score (p = 0.030), nonsynonymous tumor mutation burden (TMB) (p < 0.0001), and reduced microsatellite instability sensor score (p = 0.010). The number of mutations (p < 0.0001) and winter hypoxia score (p = 0.008) were positively associated with TTN mutations, and nonsynonymous TMB (p < 0.0001) proved to be a predictor. Mutated TTN affects the score of genetic variables involved in cancer cell metabolism in ovarian cystadenocarcinoma.Several factors may increase the risk of development of ovarian cancer. In this study, we investigated the relationship between social, genetic, and histopathologic factors in women with ovarian serous cystadenocarcinoma and titin ( Ovarian cancer is the sixth most common and deadliest malignancy in women . The incRegarding mortality, a study based on the Global Cancer Observatory (GLOBOCAN) database showed that mortality rates vary among different countries due to differences in the Human Development Index (HDI) . AccordiHistologically, ovarian carcinoma is divided into five types: serous, endometrioid, mucinous, clear cells, and squamous cells, which are further divided in subtypes ,5. AmongBRCA1/2), tumor protein p53 (TP53), epidermal growth factor receptor (EGFR), and AKT serine/threonine kinase 2 (AKT-2) [Regarding genetic factors, although certain gene mutations, such as breast cancer gene 1 and 2 ( (AKT-2) , are inv (AKT-2) .TTN) represents a tumor mutation burden (TMB) associated with various cancers, including ovarian cancer [TTN gene acts in chromosome condensation and segregation (https://genome-euro.ucsc.edu/ accessed on 23 December 2022). Therefore, the presence of a mutation can compromise gene function at this stage of the cell cycle and favor the emergence of neoplasms, such as ovarian cancer. Analysis of the frequency of this mutation in the Catalogue Of Somatic Mutations In Cancer (COSMIC) database (http://www.sanger.ac.uk/cosmic accessed on 23 December 2022) [TTN mutation and OSC may enable the discovery of a new biomarker for the screening and management of the disease in clinical practice.A recent study showed that a spontaneous mutation of the titin gene (n cancer . The TTNer 2022)  revealedTTN-related hypoxia scores can provide information regarding important characteristics of the tumor and establish relationships with tumor severity and progression in affected individuals [TTN mutation may provide evidence for a new prognostic and predictive biomarker for the risk and aggressiveness of ovarian cancer in women with the mutated gene.In addition, analysis of mutation types, the presence of microsatellite instability (MSI), and ividuals ,12. TherTTN gene mutation with social, genetic, and histopathological factors in women diagnosed with OSC, to verify whether this mutation can be a predictor and if it impacts the mortality and survival of these individuals.In this study, an analysis of OSC samples from The Cancer Genome Atlas (TCGA) was performed to identify the relationship of TTN mutations. Furthermore, it is hypothesized that TTN mutation may be related to a higher risk of OSC, increased mortality, and reduced survival in women diagnosed with OSC.We hypothesized that social, genetic, and histopathological variables are related to the presence of Thus, the results of this study may contribute to early disease screening and the development of personalized treatments for patients with OSC and provide a better understanding of the molecular mechanisms of the disease.The open-access database used in this research is maintained by the Memorial Sloan Kettering Cancer Center (MSK). These individuals were not identified and contained anonymous data; therefore, according to the National Health Council (CNS) Resolution No. 510, 7 April 2016, evaluation by the research ethics committee was not required .Observational study with ecological design having descriptive and inferential bioinformatics analysis.The study included 585 women diagnosed with OSC and the relevant data were collected in a public database from TCGA, PanCancer Atlas through the cBioPortal . SecondaAs inclusion criteria, we selected genomic samples with OSC, ICD-10: C56.9 , C48.2 , and C48.1 , using the filter to select samples with “mutation” Molecular Profile for TTN gene mutation types (ENST00000591111.5 transcript). As exclusion criteria, samples with incomplete information were not considered for the analysis.https://www.cbioportal.org accessed on 23 December 2022), maintained by the Center for Molecular Oncology at MSK [Data were collected using the database publicly available on the cBioPortal platform, an open source and open access resource for the interactive exploration of multidimensional cancer genomics datasets , which it defined as free for steps data OSC  [The data access policy regarding the availability of data for publication was consulted on the MSK Data Catalog platform (r Atlas) .TTN gene, a prior consultation was performed in the OncoKB portal. OncoKB is a knowledge base platform in precision oncology, which was developed at the MSK and is composed of biological and clinical information regarding genomic alterations in cancer (https://www.oncokb.org/cancerGenes accessed on 23 December 2022) [TTN has not been included in the list of cancer genes, highlighting the importance of studies assessing the potential of TTN as a cancer gene.For the er 2022) . The conData on 1838 clinical samples of serous ovarian cancer were available on the platform. For this study, only data referring to OSC derived from TCGA were selected via the cBioPortal.n = 475) and Genomic Profile for TTN mutation (n = 110) were used.We used a dataset containing clinical and summarized data from a sample of 585 serous ovarian cystadenocarcinomas of 585 patients from the PanCancer Atlas initiative, which comprehensively addresses cancer issues. To obtain these samples, the filters Mutation , time of overall survival (months), and disease-free survival (months).For analysis, we considered the following social variables: diagnosis age and race/skin color; genetic factors such as http://www.mutationaligner.org accessed on 23 December 2022) was used [The frequency by mutation type for the TTN gene, specifically for the ENST00000591111 transcript in OSC samples, was checked using cBioPortal. To explore the hotspots of somatic mutations in the protein domains, the MutationAligner platform (was used .2) or Mann–Whitney test was used.Descriptive analysis was expressed as frequency (absolute and relative), central tendency (median), and dispersion (interquartile range). To verify normality, the data were submitted to Kolmogorov–Smirnov test. To determine whether there were statistically significant differences between the frequency and median, the chi-square (χTTN) gene mutations could be a predictor. Initially, we performed univariate analysis considering a p-value < 0.20. To verify multicollinearity, the variance inflation factor (VIF) was calculated, and variables that presented a VIF value > 10 were removed from the final model. Statistical significance was set at p < 0.05. Odds ratios (OR) with 95% confidence intervals (95% CI) were used to quantify the degree of association.Binary logistic regression analysis was used to determine whether the Titin , Breslow (intermediary), and Tarone–Ware  tests were used to identify the occurrence of a statistically significant difference in different periods.For statistical analyses, we used the Statistical Package for the Social Sciences (SPSS) Version 26.0 .TTN gene and 110 with the mutation) were analyzed to assess the impact of TTN mutation on social variables, grid histopathology, and genetic factors.In this study, a sample of 585 individuals (475 without the mutation for the TTN gene and 110 with the mutation) were analyzed to assess the impact of TTN mutation on social variables, grid histopathology, and genetic factors.A sample of 585 individuals (475 without the mutation for the TTN gene mutation. The Buffa hypoxia score (p = 0.004), mutation count (p < 0.0001), Winter hypoxia score (p = 0.030), and nonsynonymous TMB (p < 0.0001) were higher in samples with mutation. Additionally, the MSI sensor score (p = 0.010) was lower in individuals with mutation.TTN gene showed that there was no significant statistical difference in relation to age at diagnosis (p = 0.419) and for race/skin color (p = 0.152), grade histologic neoplasm (p = 0.420), death (p = 0.937), and survival time (p = 0.082). However, one-tail analysis showed a lower survival time in the group with mutation (p = 0.041) (No TTN gene mutation: 34 (39) days; Mutation in TTN gene: 31 (35) days).As shown in p = 0.003), MSI sensor score , mutation count , and Winter hypoxia score  were associated positively with TTN mutation. The other variables were not different in relation to TTN mutations.In the univariate analysis presented in p < 0.0001) was found to be a predictor.In this sequence, multicollinearity of the variables was analyzed. The mutation count was removed for VIF values > 10. In the multivariate analysis, nonsynonymous TMB , followed by in-frame mutations (1 sample), truncations (8 samples), and multiple mutations (12 samples)  A. In tersamples) B.For missense mutations, the observed hotspots were fibronectin-type III (fn3) domain and the I-set immunoglobulin domain. Data from the InterPro description on the MultationAligner platform showed that fn3 domain has several important functions, including cell differentiation and migration, tumor metastasis, and cell adhesion activity. I-set domains are found in cell adhesion molecules, such as vascular, intercellular, and angiogenic molecules.OSC is the most common malignant ovarian tumor, accounting for approximately 50% of all cases. It occurs bilaterally in 30–50% of patients, affects women close to 60 years of age, is generally diagnosed late, and results in high lethality. In the context of late diagnosis and high lethality , studiesTTN mutations. Social factors, such as age at diagnosis and race did not affect the frequency of TTN gene mutations. Previous studies have found that mutation in this gene is observed in other types of tumors, but not in the variables of social factors in this case, which represent trends between populations. [TTN mutation and social, clinical, and genetic factors in ovarian cancer remains unexplored. Therefore, in this study, considering the database used and the Tissue Source Site, it is probable that the individuals are present in a certain geographical area that does not have large heterogeneity in social and demographic aspects [In the present study, social factors were not found to be associated with lations. ,20. To d aspects .TTN in ovarian cystadenocarcinoma. In this type of mutation, there is a variation in the DNA nucleotide in the coding region of the protein, which results in an alteration of the amino acid, and consequently of the codon, thereby influencing the structure, function, and level of the protein [TTN mutation in OSC may help characterize this histological subtype using a genetic marker.The frequency of TTN_ENST00000591111 based on the type of mutation, primarily missense mutation, is relevant in serous carcinoma, indicating that it may be a promising marker for detecting the type of missense mutation related to  protein . Therefo protein , as well protein . The preTTN mutations. This is likely due to the fact that the TTN ENST00000591111.5 transcript is a key component of striated muscles, with low levels of reads per kilobase of transcription per million reads mapped in ovarian tissue (https://genome-euro.ucsc.edu/ accessed on 23 December 2022). This may explain the lack of a histological relationship with TTN.Histopathological factors were not associated with TTN mutations to cancer cell activity and prognosis of oncogenic mutations in ovarian cancer. An analysis of the genomic sequencing profile of primary epithelial ovarian cancer samples showed that somatic mutations in multiple genes including TTN can be a biomarker for early diagnosis [Additionally, few studies have linked iagnosis .BRCA1/2 are two of the most well-known genes associated with the disease, and mutations in these genes increase the risk of developing both ovarian and breast cancer [TP53 is another commonly mutated gene in ovarian cancer, and its mutations are often associated with poor prognosis and resistance to chemotherapy [AKT-2 gene as a potential contributor to ovarian cancer development and progression [BRCA1 and BRCA2 genes have been successfully targeted with poly (ADP-ribose) polymerase (PARP) inhibitors, leading to significant improvements in response rates and overall survival in patients with BRCA-mutated ovarian cancers [BRCA1/2, TP53, and AKT-2 are among the most relevant gene factors associated with ovarian cancer, and their study is critical for improving our understanding and management of this disease.Ovarian cancer is a complex and heterogeneous disease with multiple genetic and environmental factors contributing to its development. Several genes have been identified as important players in the onset and progression of ovarian cancer. t cancer . TP53 isotherapy . Additiogression . These g cancers . However cancers . DespiteTTN is associated with high TMB [TTN mutation in OSC can also be used as a marker that should be further investigated in preclinical studies to test the in vitro activity of immunotherapy in a selected population with likely high TMB. However, the OncoKB portal showed no results regarding the TTN gene [However, a recent study indicated that high TMB . Tumors high TMB . In clinhigh TMB ,33. TherTTN gene , emphasiTTN mutation in OSC can also be used as a marker that should be further investigated in preclinical studies to test the in vitro activity of immunotherapy in a population with likely high TMB selected as a biomarker.Therefore, considering previous findings and the results of the present study, it is possible that the presence of a TTN gene mutation and OSC. The mutation count was higher in women with ovarian cystadenocarcinoma and was positively associated with TTN gene mutation. The TTN gene is susceptible to cancer protein kinase, encodes the major polypeptide expressed in oncogenesis, and is found in many functional cell types [TTN ENST00000591111.5 transcript plays an important role in chromosomal condensation and segregation during mitosis [TTN gene may affect cell cycle and contribute to tumor occurrence and progression. In addition, we found that missense point mutations in the TTN transcript for the fn3 and I-set domains can affect cellular processes such as angiogenesis.In this study, we found that genetic factors are associated with ll types . In part mitosis . TherefoTTN gene mutation. When a tumor develops, the homeostasis of tissue oxygenation is altered because of the increased demand for oxygen by the cancer tissue, leading to the rapid and erratic formation of new blood vessels and increased demand with regard to the available supply of oxygen [TTN mutation and hypoxia score in ovarian cancer has not yet been researched.When we examined the relationship between Winter and Buffa hypoxia scores, we found an increase in hypoxia with f oxygen . This isf oxygen . AlthougTTN-related hypoxia score indicated that dysregulation of these parameters can offer valuable information about tumor characteristics and its relationship with disease progression and severity in affected individuals [Molecular analysis of mutation types, MSI status, and ividuals . Tumor bividuals , therebyTTN gene as the score increased. It is anticipated that MSI  will be increasingly employed in cancer diagnostics because it is crucial for understanding cancer progression, extracting information about familial risk, and developing treatment methods [The MSI sensor score was lower in individuals with mutations but was positively correlated with a mutant  methods .TTN gene mutation, and the levels of nonsynonymous TMB were greater and more positively correlated. TMB is a biomarker for immuno-oncology that has a high rate of immunotherapy response and is used to quantify the burden of neoantigen tumors by examining both the quantity and quality of mutations [TTN mutation serves as a risk factor for ovarian cancer [When women with ovarian cystadenocarcinoma had the utations ,40. Althutations ,42,43. On cancer .Sociodemographic variables, such as family history, demography, and low/high income, were not analyzed because of database limitations. In addition, given the different natural histories and clinical backgrounds of the five ovarian cancer histotypes, the results of this study are only applicable to the analyzed subtypes. Nevertheless, our study limitation concerns the ecological design, where association between clusters does not necessarily indicate that it occurs at the individual level, when investigating samples from secondary databases . HoweverTTN mutations. However, we identified genetic factors associated with TTN mutations in women diagnosed with OSC. The Buffa hypoxia score, mutation count, Winter hypoxia score, and nonsynonymous TMB were higher and positively associated with TTN gene mutation in women with ovarian cystadenocarcinoma. MSI sensor scores were lower in those with mutation but were positively associated with mutated TTN gene. The survival time was lower in women with TTN mutation. Nonsynonymous TMB proved to be a predictor of interaction with other variables. In addition, missense point mutations in the TTN transcript for the fn3 and I-set domains may affect cellular processes such as angiogenesis. In summary, these findings indicate that TTN mutations and TMB may act as target genetic markers to assist in the detection of OSC and in the clinical management of the disease.Social and histopathological factors were not associated with


To investigate morphological and microcirculation changes of optic nerve head (ONH) in simple high myopia (SHM) and pathologic myopia(PM) to evaluate and identify ONH changes in the development of PM.A cross-sectional clinical study was used. Medical records from 193 right eyes of 193 patients with high myopia (HM) were included. Using the Topocon swept source optical coherence tomograph (SS-OCT) and fundus camera to detect the parameters, we have assessed the relative position and size of ONH, tilt and rotation of ONH, angle α , size and type of peripapillary atrophy (PPA), the thickness of peripapillary retinal nerve fiber layer (PRNFL), peripapillary choriodal thickness (PCT) and peripapillary scleral thickness (PST), and peripapillary vessel density (PVD). In addition, subjects were grouped as SHM and PM according to retinopathy, and the above parameters were compared between the two groups.Patients were divided into the SHM group (138 eyes) and the PM group (55 eyes). Paramters like older age, higher diopter and longer axial length (AL) of the PM were compared to SHM . There were no differences in the smallest diameter and area of ONH, rotation angle and ratio, or PST . The angle α in PM was smaller than that in SHM . The disc-fovea distance (DFD), the largest diameter, tilt index and ratio, PPA area and radian in PM were larger than in SHM . The superior and inferior PRNFLs in PM were smaller than in SHM . The temporoinferior PRNFL was the opposite . The average PCT in PM (93.82 ± 29.96 μm) was smaller than in SHM (108.75 ± 30.70 μm) (P<0.05). The PVD in each direction of PM was smaller than that in SHM .Compared with SHM, the PM patients were older, with higher diopter. Their AL and DFD were longer, the angle α was smaller, the tilt index was more extensive, the PPA area and radian were larger, PCT was generally thinner, and PVD was lower. When the PPA area was bigger than the ONH area, this already indicated the presence of PM. Based on these results, we suggest ophthalmologists and myopia patients pay more attention to ONH’s morphology and microcirculation changes as there is a possibility that microcirculatory changes precede morphologic changes. Pathologic myopia(PM) is a fundamental cause of irreversible low vision and blindness, leading to irreparable visual impairment. It has a population incidence of 0.9%~3.1% among Asian countries .Therefore, we hypothesize that the on-time evaluation and identification of early ONH changes in HM will be conducive to the early diagnosis and treatment of PM. Furthermore, before fundus lesions occurred, patients were reasonably treated to reduce vision-threatening complications.One hundred ninety-three patients were enrolled in the retrospective study. They were diagnosed with myopia from May 2020 to January 2021 at Changsha Aier Eye Hospital, Central South University, Changsha, China. The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Changsha Aier Eye Hospital, Central South University, Changsha, China. In addition, comprehensive examinations including slit-lamp biomicroscopy, mydriatic indirect ophthalmoscopy, intraocular pressure (IOP), axial length (AL) , ultra-widefield fundus imaging , swept-source optical coherence tomography (SS-OCT) , visual acuity and refractive error were conducted for all participants.The inclusion criteria were: (1) the diopter > -6.00 DS and the AL > 26 mm; (2) IOP ≤ 21 mmHg; (3) SS-OCT acquisition signal ≥ 50. In addition, patients were excluded from the study if they had hypertension, diabetes, systemic connective tissue disorder, any other ophthalmic disease , special abnormal ONH , history of ophthalmic trauma and surgery. One hundred ninty-three patients were enrolled in the retrospective study.According to Meta-PM , myopia We used the Optos to perform digital photography, and the images were exported into the Image-pro Plus (version 6.0) to measure the morphology of ONH. The size, disc-fovea distance (DFD), angle α, tilt, rotation, peripapillary atrophy (PPA) area, and PPA radian were measured from these photographs by the aforementioned retinal ophthalmologists Fig. . The areWe have applied the SS-OCT , where the system automatically measures the peripapillary retinal nerve fibre layer (PRNFL) thickness, peripapillary choroidal thickness (PCT), peripapillary scleral thickness (PST), and peripapillary vessel density (PVD) . We divided the ONH into 12 directions , Before the SS-OCT image acquisition, we input the examinee’s information  to adjust the magnification factor. The average value was used in the final analysis.2 test. P-values of < 0.05 were considered to be statistically significant.The SPSS22.0 software was used for the statistical analysis of all gathered data. Wherever the data had the normal distribution, it was presented as mean ± standard deviation (X ± SD). The LSD test was applied for the different analyses of the data collected for the two groups of patients. Wherever the data could not meet the normal distribution, it was represented as median , and the non-parametric test (Mann-Whitney (U)) was used for further analysis. The count data were expressed as a percentage (%) and were analyzed with the χA total of 193 patients were enrolled in the retrospective study. The right eyes of the patients that met the inclusion and exclusion criteria were analyzed. Among them, 138 eyes were SHM, and 55 eyes were PM. As shown in Table We have compared the PRNFL parameters among the PM and SHM groups, and the results are displayed in Table The comparison among the PCT parameters between the patients’ groups is displayed in Table The comparison between the PVD parameters between the two groups is shown in Table The comparison of morphology and microcirculation of the eyes of the patients’ in the compared groups are shown in Fig. At present, the clinical definition of PM is not clear. Therefore, it is necessary to discover the unique features of PM by measuring parameters that describe the morphology and microcirculation changes of the optic nerve head between SHM and PM. Hence, the primary purpose of this study is to compare the difference in ONH between SHM and PM to find the measurable indicators of morphological and microcirculatory parameters related to ONH.Previous studies have shown that axial myopia mainly depends on the posterior eyeball enlargement, and the closer the posterior pole is, the pathology is more prominent . To asseNormal ONH is round or oval with an average diameter of 1.5 mm. ONH is the access of the optic nerve and retinal vessels in the eye, consisting of nerves, vessels and bindweb. The morphology changes of ONH are often manifested as tilt and PPA in HM. Results show no significant difference between the area and SD of ONH in the SHM group and PM group, while LD and tilt index of ONH were significantly different. In contrast, the tilt ratio increased in PM. The severe tilt and distorted deformation adversely impacted the optic nerve, resulting in vision decline . This inPPA is attributed to photoreceptor reduction, RPE and choroid capillary loss, Bruch membrane displacement or defects. As a characteristic change of myopia, the expansion of PPA indicates the progression of myopia. During 20 years of prospective studies on fundus in children with HM, Yokoi  found thONH is seamlessly connected to the RNFL, choroid, and sclera, which plays a crucial role in morphology changes. PRNFL mainly shows changes in thickness and distribution in HM. Our results showed that the average thickness of PRNFL in the PM group was lower than that in the SHM group, significantly inferior and superior; However, the TI-PRNFL of the PM group was slightly higher than that of the SHM group. Analogously, while studying the changes in displacement and morphology of ONH, Tan  found thThe choroid, as vascular tissue, lies between the scleral and retina. Compared with ordinary people, in anatomy and OCT, the choroid was atrophic and thin in HM . MoreoveAs the outermost layer of the eyeball, the sclera plays an essential role in stabilizing intraocular pressure, protecting the intraocular structure and maintaining normal morphology. The morphology and structure of the sclera were shown to change constantly, together with its biochemical and biomechanical properties, along with AL, including the phenotype of the scleral fibroblasts and the composition of the extracellular matrix . WhetherWe further speculate that the choroidal changes may precede the RNFL and sclera by affecting the blood supply of the two tissues, stimulating myopia toward HM, and ultimately resulting in PM. These initial insights may provide clues and a basis for further investigation of histology in HM.In our study, the PVD was lower in the PM group compared with the SHM. Similarly, another study showed that HM had less peripapillary retinal blood flow index and vessel density (VD) than emmetropia, suggesting decreased peripapillary microcirculation perfusion . We alsoMeanwhile, the Zinn-Haller provided the main blood supply to the lamina cribiosa of ONH. Ishida  found thWe acknowledge several limitations associated with our study. First, this is a retrospective study. Hence, the longitudinal data were unavailable. Thus, it might not reflect the true representative of the population. Furthermore, due to the requirements of SS-OCT scan quality, most patients with PM were excluded from the study, allowing a selection bias. Last, the magnification effect of images might affect the results of OCT measurements (e.g. vessel density and area). Still, the current formula for amplification correction was not uniform, and re-measurements were separated from the OCT measure system, which might bring new deviations. However, the range of 4.5 × 4.5 mm scans used in this study was small enough that the effect of the amplification effect was not significant.PM is closely linked to the reduction of choroidal perfusion and structural changes of ONH. Compared with SHM, the PM patients were older, with higher diopter. As a result, their AL and DFD were longer, the angle α was smaller, the tilt index was more extensive, the PPA area and radian were larger, PCT was generally thinner, and PVD was lower. Unfortunately, it was impossible to regain normal vision once PM-related complications occurred. Therefore, we suggest ophthalmologists and myopia patients pay more attention to ONH’s morphology and microcirculation changes as there is a possibility that microcirculatory changes precede morphologic changes. However, this needs to be determined by further studies.


A meta-analysis of randomized controlled trials.Our meta-analysis was conducted to investigate whether interspinous spacer (IS) results in better performance for patients with lumbar spinal stenosis (LSS) when compared with decompressive surgery (DS).DS and IS are common surgeries for the treatment of LSS. However, controversy remains as to whether the IS is superior to DS.We comprehensively searched PubMed, EMBASE, and Cochrane Central Register of Controlled Trials for prospective randomized controlled trials that compared IS versus DS for LSS. The retrieved results were last updated on July 30, 2023.P = .003), lower dural violation rate (P = .002), better Zurich Claudication Questionnaire Physical function score (P = .03), and smaller foraminal height decrease (P = .004), but inferior to DS considering the higher rate of reoperation (P < .0001). There was no significant difference between the 2 groups regarding hospital stay (P = .26), blood loss (P = .23), spinous process fracture (P = .09), disc height decrease (P = .87), VAS leg pain score (P = .43), VAS back pain score (P = .26), Oswestry Disability Index score (P = .08), and Zurich Claudication Questionnaire symptom severity (P = .50).Eight studies involving 852 individuals were included in the meta-analysis. The pooled data indicated that IS was superior to DS considering shorter operation time (In summary, we considered that IS had similar effects with DS in hospital stay, blood loss, spinous process fracture, disc height decrease, VAS score, Oswestry Disability Index score, and Zurich Claudication Questionnaire Symptom severity, and was better in some indices such as operation time, dural violation, Zurich Claudication Questionnaire Physical function, and foraminal height decrease than DS. However, due to the higher rate of reoperation in the IS group, we considered that both IS and DS were acceptable strategies for treating LSS. As a novel technique, further well-designed studies with longer-term follow-up are needed to evaluate the effectiveness and safety of IS. Therefore, a minimally invasive and low-risk procedure called interspinous spacer (IS) or interspinous process device was invented to implant a spacer between the 2 adjacent spinal processes with lesions to prevent excessive dorsiflexion and to relieve symptoms induced by stenosis, such as Aperius, Coflex, DIAM, Wallis and X-stop, which could effectively unload the diseased lumbar motion segment and maintain mobility and structural elements.,7 However, previous studies,9 indicated that controversy remains about whether IS could produce better or worse outcomes than DS, and lacks strong evidence. Given the newly emerging evidence, we performed a meta-analysis to compare the effectiveness and safety of IS versus DS for lumbar spinal stenosis (LSS), and only randomized controlled trials (RCTs) were included in our meta-analysis to achieve high quality and credible results.Lumbar spinal stenosis (LSS) is a common disease due to a degenerative decrease in the lumbar spinal complex.To make an exhaustive search of all relevant literature, 2 independent reviewers conducted a PRISMA  compliant search of PubMed, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) for RCTs. The following search terms were used for the initial literature search: “ AND (DS OR decompression) AND Clinical Trial [ptyp].” We also reviewed the reference lists of retrieved studies and recent reviews. The retrieved results were last updated on July 30, 2023.The PICOS  strategy was utilized to guide the study selection process. We included studies that were eligible for the following criteria: (1) patients with symptomatic LSS; (2) the articles that provide IS versus DS as intervention treatments; (3) outcomes including at least one of the following data: surgical parameters , and clinical indexes; (4) RCTs were eligible. The exclusion criteria were as follows: (1) the researches were case reports, reviews, or observational studies. (2) The outcomes were descriptive or graphic with no numerical values; (3) the same data had been published repeatedly. Two reviewers independently selected the potentially qualified trials according to the inclusion and exclusion criteria. Any disagreement was resolved by discussion and conformity was reached.Two independent reviewers extracted the data from eligible studies. If there were any disagreements, all of the authors discussed them until a consensus was achieved. The indispensable data extracted from eligible studies included published year, sample size, mean age, gender distribution, body mass index, number of levels, country, sample type, duration of follow-up, number of centers, and outcomes. The outcomes in this analysis included surgical parameters , and clinical indexes . The contents are 7 parts: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment data (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other bias. Each item was recorded as “high risk of bias,” “low risk of bias,” and “unclear risk of bias.” Any disagreement was discussed and resolved with a third independent author.The risks of bias in the RCTs were assessed independently by 2 of the authors by using the Cochrane Collaboration Risk of Bias Tool.I-square test were used to assess the statistical heterogeneity. It demonstrated significant heterogeneity when a P-value of the chi-square test was <0.10 or I2 values exceeded 50%. A random-effects model was used when significant heterogeneity was observed among the included studies. Otherwise, a fixed-effects model was used for no significant heterogeneity. The possibility of publishing bias was not researched because of the limited number of included studies. This meta-analysis was performed by RevMan 5.3 software . The statistically significant level was set at P < .05.The weighted mean difference or standardized mean difference (SMD) and their 95% confidence interval (CI) were calculated for the continuous data, and the odds ratio (OR) and its 95% CI were calculated for the dichotomous data. The chi-square test and –19 for analysis .Relevant data for operation time was obtained from 4 studies,18 with 197 patients in the IS group and 196 patients in the DS group. A random-effects model was used. The pooled estimate of effect size for blood loss in Figure P = .23).Relevant data for blood loss was obtained from 2 studies,15 with 120 patients in the IS group and 120 patients in the DS group. A random-effects model was used. The pooled estimate of effect size for the hospital stay in Figure P = .26).Relevant data for hospital stay was obtained from 2 studies,15,18,19 with 226 patients in the IS group and 232 patients in the DS group. A fixed-effects model was used. The pooling of data in Figure P = .002).Relevant data for dural violation was obtained from 4 studies,13,19 with 151 patients in the IS group and 155 patients in the DS group. A fixed-effects model was used. The pooled estimate of effect size for spinous process fracture in Figure P = .09).Relevant data for spinous process fracture was obtained from 3 studies,13,15,17 with 252 patients in the IS group and 251 patients in the DS group. A fixed-effects model was used. The pooling of data in Figure P < .0001).Relevant data for reoperation was obtained from 4 studies,13,17 and VAS bp was obtained from 4 studies.–14,17 A random-effects model was used. The pooled estimate of effect size for VAS lp  and VAS bp  in Figure Relevant data for VAS lp was obtained from 3 studies,15,18 with 185 patients in the IS group and 186 patients in the DS group. A random-effects model was used. The pooled estimate of effect size for ODI in Figure P = .08).Relevant data for ODI was obtained from 3 studies,17 with 122 patients in the IS group and 122 patients in the DS group. A random-effects model was used. The pooled estimate of effect size for ZCQP in Figure P = .03), and no significant difference for ZCQS between the 2 groups .Relevant data for ZCQP and ZCQS was obtained from 2 studies,18 with 121 patients in the IS group and 121 patients in the DS group. A random-effects model was used. The pooled estimate of effect size for FH in Figure P = .004), and no significant difference for DH decrease between the 2 groups .Relevant data for FH and DH was obtained from 2 studies (P = .0004). ODI score in the IS group was significantly less than that in the DS group when omitting the study by Schmidt et al (P = .0006). The results of sensitivity analysis of other outcomes were not materially different compared with those of the original analysis. We analyzed that different versions of the rating scale and different IS types may yield different results in assessing disease symptoms or patient quality of life. The duration of follow-up may also impact study results, as disease symptoms or treatment effects can change over time.Sensitivity analysis investigated the influence of a single study on the overall outcome estimate by omitting 1 study in each turn. VAS bp score in the IS group was significantly less than that in the DS group when omitting the study by Stromqvist et al,9,20,21 had reported that IS showed non-inferior efficacy and reliability for the treatment of LSS compared with DS. Non-RCT studies were included in the previous analyses, and other clinical outcomes and complications  of IS were not mentioned in these analyses. That is why we included only RCTs in our meta-analysis to determine whether IS was superior to DS for LSS.The IS is a new treatment concept for LSS, which enables the treatment of spinal pain and instability without fusing the involved spinal segment. However, a few systematic reviews and meta-analyses–24 Desai et al indicated that dural violation during DS for LSS did not impact long-term outcomes in patients, although it considerably increased operation duration, blood loss, and hospital stay. In comparison to the DS group, the pooled data in our research revealed that IS can greatly lower the likelihood of dural violation. We summarized studies assessing the safety of IS and DS for LSS using multiple metrics. IS was found to have a significant advantage in terms of operative time, but did not differ significantly from DS in terms of bleeding and length of hospital stay. Dural rupture is a common complication of decompression surgery but has a limited impact on long-term outcomes, whereas IS reduces the risk of dural rupture. These findings have important implications for guiding treatment decisions.We used operation time, blood loss, hospital stay, dural violation, spinous process fracture, and reoperation rate to describe the safety of IS and DS in treating LSS. The results of our meta-analysis demonstrated that IS had a significantly shorter operation time compared with the DS group. However, the pooled data showed that there were no significant differences between the 2 groups in blood loss and hospital stay. Dural violation is a familiar encounter during DS for LSS (6.5–9%). demonstrated that IS could lead to stress redistribution at the pars interarticularis, which could cause a potential spinous process fracture. David et al also indicated that these fractures were initially minimal or nondisplaced and the metallic wings of the devices often obscured fractures, and the unrecognized spinous process fracture may be responsible for a significant number of patients who experience reoperation after IS surgery.The pooled data indicated that IS group had a higher rate of reoperation  than the DS group, and no significant difference in the rate of spinous process fracture between both groups. Hao et al identified a significant correlation between FH and DH. A decrease in FH may cause compression to the nerve root that crosses the foramen, and the increased DH resulted in greater FH and area, which can improve symptoms of nerve root pain. In our study, despite the fact that there were no significant variations in DH reduction between the 2 groups, patients treated with IS had a lower FH reduction than those treated with DS, which relied on the functional design of the IS. The results of the study showed that the effectiveness of IS and DS in the treatment of LSS was similar in most of the assessed metrics. However, for the effects on ZCQP score and FH, IS showed some advantages, which may be related to the functional design of IS. These results provide important information for clinicians and patients regarding treatment options for LSS, especially for those concerned with symptom relief and functional improvement.VAS lp, VAS bp, ODI, ZCQP, and ZCQS, FH, and DH were used to describe the effects of IS and DS in treating LSS. In this analysis, we found that compared with DS, except for the less ZCQP score in IS group, the VAS score (both for leg and back), ODI, and ZCQS in the IS group were not significantly different. Therefore, we considered that the effect of IS was similar to that of the DS. Yusof et al,21 were included in our study. However, there are also several limitations in our study. First, the language of the included studies was limited to English, and studies in other languages were not considered for inclusion in this analysis. Second, the IS devices used in these studies were different types, which are potential sources of clinical heterogeneity. The use of IS in patients with severe LSS requires more caution, future studies should focus on indications for surgery in patients, complications, as well as the type and degree of spinal stenosis.There are several strengths in our study. First, only RCTs were included in our analysis to ensure high quality and credible results. Second, some clinical outcomes and complications that were not accessible in prior studiesIn summary, we considered that IS had similar effects with DS in hospital stay, blood loss, spinous process fracture, DH decrease, VAS score, ODI score, and ZCQS, and was better in some indices such as operation time, dural violation, ZCQP, and FH decrease than DS. However, due to the higher rate of reoperation in the IS group, we considered that both IS and DS were acceptable strategies for treating LSS. As a novel technique, further well-designed studies with longer-term follow-up are needed to evaluate the effectiveness and safety of IS.Data curation: Jia-Ju Che, Zhe Wang, Yu-Ming Chen, Bing Leng.Writing – original draft: Jian-Hai Xin.Writing – review & editing: Dalin Wang.


Patients with single left ventricle (SLV) and tricuspid atresia (TA) have impaired systolic and diastolic function. However, there are few comparative studies among patients with SLV, TA and children without heart disease. The current study includes 15 children in each group. The parameters measured by two-dimensional echocardiography, three-dimensional speckle tracking echocardiography (3DSTE), and vortexes calculated by computational fluid dynamics were compared among these three groups. Twist is best correlated with ejection fraction measured by 3DSTE. Twist, torsion, apical rotation, average radial strain, peak velocity of systolic wave in left lateral wall by tissue Doppler imaging (sL), and myocardial performance index are better in the TA group than those in the SLV group. sL by tissue Doppler imaging in the TA group are even higher than those in the Control group. In patients with SLV, blood flow spreads out in a fan-shaped manner and forms two small vortices. In the TA group, the main vortex is similar to the one in a normal LV chamber, but smaller. The vortex rings during diastolic phase are incomplete in the SLV and TA groups. In summary, patients with SLV or TA have impaired systolic and diastolic function. Patients with SLV had poorer cardiac function than those with TA due to less compensation and more disordered streamline. Twist may be good indicator for LV function. Single left ventricle (SLV) and tricuspid atresia (TA) are two common subtypes of functional single left ventricle (FSLV), a kind of complex cyanotic congenital heart disease with single or systemic left ventricle . CardiacThe impact of ventricular morphology on ventricular function and clinical outcomes are controversial. Only limited studies were focused on the characteristics of different subtypes of FSLV –6. AlterEchocardiography is a reliable and convenient clinical assessment tool for ventricular function measurement . It has Fifteen patients with SLV and 15 with TA who were diagnosed using echocardiography and confirmed by surgery from August 2015 to May 2022 in Shanghai Children's Medical Center were recruited. Children with serious arrhythmia or other conditions such as taking medicine which affects the ventricular function were excluded. The exclusion criteria were poor image quality that was inadequate for analysis. All the children had conventional two-dimensional echocardiography, three-dimensional speckle tracking echocardiography (3DSTE), and CMR examination. In addition, 3DSTE examination was performed on 15 age- and gender-matched children (Control group), who had echocardiography examination for heart murmur but no positive finding. Characteristics of the subjects are shown in a-b)/b, while “a” is the period starting from the end of mitral or atrioventricular valve inflow to the start of next one, and “b” is the ejection time of aortic valve. Corresponding parameters of the atrioventricular valve in the SLV group were compared with those of the mitral valve, while average values were used in single left ventricle with two atrioventricular valves. The ratio of E/e was calculated.Each of the children lay quietly, and oral chloral hydrate (50 mg/kg) was administered if necessary. Children with dextrocardia were scanned in the right decubitus position while the remaining subjects were scanned in the left lateral decubitus position, and the ECG was recorded simultaneously. The peak velocity of the early filling wave (E wave), deceleration time of the E wave (DecT) were measured by two-dimensional echocardiography. The peak velocity of systolic wave (sL) and early filling wave (eL) of left lateral wall, systolic wave (sR) and early filling wave (eR) of right lateral wall, isovolumic relaxation time (IRT), and isovolumetric contraction time (ICT) were measured by tissue Doppler imaging (TDI). MAPSE was defined as mitral annular plane systolic excursion or left lateral annular plane systolic excursion. TAPSE was defined as tricuspid annular plane systolic excursion or right lateral annular plane systolic excursion. The myocardial performance index (MPI), also called the Tei index, is widely used as a good parameter in the assessment of cardiac function in LV as well as single ventricle. This index is calculated by the ratio of total ICT and IRT to ejection time ; it can DLV/LLV, where DLV is the diameter of the left ventricle and LLV is the length of the left ventricle. TomTec 4D LV-Analysis 3  was applied for post-processing analysis (A), basal rotation (RotationB), average principal tangential strain (TSaverage), average circumferential strain (CSaverage), average longitudinal strain (LSaverage), and average radial strain (RSaverage) were automatically calculated by the software. Images that were not satisfied with automatic tracking could be manually modified and analyzed again.A Philips iE33 ultrasonic diagnostic apparatus , equipped with matrix-array X5-1 transducer was applied to acquire full volume image loops . The 3D analysis . Primaryanalysis  and thenCMR studies were performed on a 1.5-T Intera Achieva scanner18  on all the patients. Retrospectively, EKG-gated balanced steady-state free precession (SSFP) cine movies were used. Slice thickness was 4–8 mm, while the interslice gap was set to zero. The temporal resolution was over 30 frames per cardiac cycle. Then images acquired were analyzed using commercial software .3. The second-order upwind scheme was employed to complete the steady-state numerical simulation by ANSYS®-ICEM 17.0 software. The standard k – ε model was employed to solve the motion of intraventricular blood flow inside the ventricle. The results were analyzed and processed by ANSYS CFD-Post 14.5 software.Four-dimensional ECHO images were imported into medical imaging software ; then, we used the “Thresholding” tool to determine the appropriate gray value range to include all the left ventricle information. Then, we entered the “Edit masks” module, used the “Region growing” and “Calculate 3D from mask” tool to generate the mask and 3D model. At last, the edge was smoothed by the “Smooth mask” tool and smoothed papillary muscles and valves out. After that, the anatomy was represented by five body-fitted prism layers using the commercial software ANSYS ® -ICEM 14.5; velocity obtained by two-dimensional Doppler was used as boundaries for numerical solutions of the Navier–Stokes flow equations. The distance from the first layer to the model surface was 0.02 mm, with 1,000,000 to 3,000,000 tetrahedral mesh elements filling the remaining of the calculated domain, with a tetrahedral mesh filling the remainder of the calculated domain. The blood in the LV is assumed to be an incompressible Newtonian fluid with a constant viscosity of 0.004 kg/ms and density of 1,060 kg/mt-test and Mann–Whitney test between groups. A two-sided p-value <0.05 was considered statistically significant. The intraclass correlations (ICCs)  was applied for statistical analysis. Quantitative variables were presented as mean ± SD and compared with s (ICCs)  of intraFifteen patients with SLV (SLV group) and 15 patients with TA (TA group) underwent echocardiography examination. There were no missing data during the study. In addition, 3DSTE examination was performed on 15 age- and gender-matched children (Control group). Then, statistical analysis was performed between patients and controls. Concomitant malformation in SLV group and TA group data is shown in p < 0.05), but they are all within the normal range. The Bland–Altman plot revealed almost no bias between EDV, ESV, SV, and EF measured by 3DSTE and CMR , and HR among the three groups . Diastol and CMR .p < 0.05 for sR, p < 0.01 for TAPSE), IRT, DecT, and MPI are larger in the SLV than in the Control group (p < 0.01). There are no significant differences in E, eL, E/eL, sR, ICT, DecT, and MAPSE between the TA group and the Control group. eR and TAPSE are smaller in the TA group than those in the Control group . sL, IRT, and MPI are larger in the TA group than in the Control group . There are no significant differences in E, eL, E/eL, sR, eR, IRT, ICT, DecT, TAPSE, and MAPSE between the SLV and the TA groups, while MPI is larger in the SLV group (p < 0.05) and sL is larger in the TA Group (p < 0.05).As is shown in A, RotationB, TSaverage, CSaverage, LSaverage, and RSaverage are −0.579, −0.540, 0.746, 0.594, 0.432, −0.675, −0.363, −0.538, −0.316, and 0.479, respectively. Twist is best correlated with EF measured by 3DSTE.The ICCs of 3DSTE in 45 children are shown in A, RotationB, TSaverage, CSaverage, LSaverage, and RSaverage are poorer in the SLV group (p < 0.01) than those in the Control group. SI, EDV, ESV, and SV are larger, and EF, GLS, GCS, Twist, Torsion, RotationA, RotationB, TSaverage, CSaverage, LSaverage, and RSaverage are poorer in the TA group than those in the Control group . The differences in SI, EDV, ESV, SV, EF, GLS, GCS, TSaverage, LSaverage, and RSaverage are not significant between the SLV group and the TA group. However, twist, torsion, and RotationA are poorer in SLV Group .As shown in As shown in In patients with SLV and TA, abnormal systolic and diastolic function are common, but they are usually evaluated separately. A previous study had implied that impaired relaxation may result from decreased systolic motion . In realThere are no significant difference in E/eL ratios among these three groups, similar to the study by Hershenson et al. , indicatPatients in both SLV and TA groups have prolonged IRT than those in the Control group. Prolongation of isovolumic time is likely to be affected by the slow rate of ventricular relaxation resulting from decline in the change rate of ventricular pressure or decrease in ventricular compliance. Such changes are likely correlated to abnormal myocardial untwisting. As presented by Appleton et al. , longer Patients with FSLV have higher MPI and lower TAPSE values than those in the Control group, similar to previous studies , 23. MPIPatients in the SLV and TA groups had enlarged EDV, ESV, and SV, which are consistent with previously reports . SLV andAn experimental study  showed tNot surprisingly, strain values in FSLV children are significantly poorer than those in the Control group, which are consistent with previous reports . In normMyocardial fibrosis in FSLV is also an underlying reason for its impaired cardiac function, which is confirmed by cardiac magnetic resonance imaging . MyocardOur study also showed poorer twist, torsion, apical, and basal rotation in children with FSLV when compared with controls, as previous reported , 37, andIn a normal left ventricle, myocardial relaxation and contraction push flood flow with gyratory motion. The geometry and myofiber orientation of a normal left ventricle are optimized to eject blood in the systole with least energy dissipation and stored potential energy for untwisting; by untwisting, blood is effectively sucked into the ventricle. Recoiling during diastole allows the myocardium to relax and suck blood from left atrium into left ventricle, with vortexes and vortex rings formed in LV, which can redirect the flow and preserve energy. It can provide dynamic energy sources for blood to flow forward, forming vortexes, which can convert vortex kinetic energy into the rotational kinetic energy, and avert convective deceleration. Natural asymmetric geometry of the heart could minimize dissipative interaction of flow convection to arrange the flow for ejection.While in patients with FSLV vortexes in ventricular chambers are smaller and rounder, and even disappear, the vortex ring during the diastolic phase is incomplete. The enlarged dominated chamber, hypoplastic residual cavity and the symmetric geometry make the structure of FSLV significantly different from normal LV; therefore, the streamline inside the ventricle is disordered and flows without enough swirling, resulting in smaller vortex and incomplete vortex ring. Thus, more energy dissipates, and more performance is needed to maintain proper stroke volume.Therefore, the abnormal orientation of the myofiber, fibrosis of myocardium, special geometry, and disordered flow fields, along with the overload of the LV, may cause weakness of contractility in systole as well as diastole, deteriorating the ventricular performance.It is notable that sL is larger in the TA group compared with the SLV group, and even the Control Group, indicating that the left lateral wall in the TA group contributes more to cardiac function, which may reflect adaptation and compensation; it may also explain the better performance in the TA group than in the SLV group. Patients in the TA group also had higher sR value than those in the SLV group, indicating that cardiac motion in the right lateral wall in the TA group are better than the SLV group, similar to a previous study .Compared with the Control group, patients in the SLV group have more prolonged DecT; the difference is significant, while the difference between the TA group and the Control group is not significant, reflecting decreased ventricular compliance in early diastolic period in SLV than in TA. As stated by Thomas and Weyman , impairmThough without significant difference, there is a trend toward lower values of E in the SLV group than the TA and Control groups. Lower E is suggestive of volume overload and increased atrial reliance during diastolic in TA for ventricular filling, similar to the study by Khoo et al. . This inCompared with the TA group, patients in the SLV group have poorer twist, torsion, and rotation. The orientation of myofibers is more longitudinal in TA, with interstitial fibrous deposition greater than normal heart in all sites analyzed ; this waThe shape of the ventricle may affect a lot in the turning of streamlines and the formation of the vortices. Compared with the TA group, LV in the SLV group has rounder apex and more symmetrical structure, while it is more ellipsoidal and asymmetrical in TA. In patients with SLV, blood flows straight ahead to the apex without enough swirling; when the blood flow reaches the enlarged part in the middle of the main cardiac cavity, it spreads out in a fan-shaped manner and forms two small vortices. When viewed from the front, it is clockwise on the left and counterclockwise on the right in the base region of LV. More energy dissipates during this period. The unnatural asymmetry flow could reduce the efficiency of the heart pump by more than 10% . In the Since the A wave fused into the E wave in many children in the study, we did not discuss these parameters in our article. Not only the impairment of cardiac function but also the preload, volume, and atrial and ventricular stiffness influence the results, but it is difficult to distinguish them completely. The shape of the ventricle and anatomic structures such as valve and papillary muscle may affect the turning of streamlines and the formation of the vortices, while in our study, valves and papillary muscles were smoothed out to optimize CFD modeling. Our sample size of this study was relatively small. Furthermore, more patients and long-term follow-up are needed for further study.Both SLV and TA have impaired systolic and diastolic function. Patients with SLV have poorer cardiac function than those with TA due to less compensation and more disordered streamline. Our study demonstrates that twist is a good indicator for measuring ventricular performance, which may predate more noticeable echocardiographic signs of deterioration.


The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long Short Term Memory (LSTM) networks to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. For this, we merge two datasets for November 2017 in Chicago: (1) aggregated electricity use data in 30-min intervals within the city of Chicago and (2) traffic volume data captured on the Chicago expressway network. Four analyses are conducted to identify interrelationships: (a) correlation between two time series, (b) temporal relationships, (c) spatial relationships, and (d) prediction of electricity consumption based on the total traffic volume. Overall, from over 250 models, we identify and discuss complex interrelationships between travel demand and electricity consumption. We also analyze and discuss how and why model performance varies across Chicago. In part because of these interdependencies, but also intrinsic to how people live, the way infrastructure systems are used is also interrelated. For example, Movahedi and Derrible3 showed that electricity, gas, and water consumption in large-scale buildings are interrelated . Zhang and Qian4 classified the patterns of electricity consumption over a night to estimate the traffic congestion of a highway in the morning. Overall, infrastructure systems are often more interrelated than initially expected, for example by sharing physical surface and subsurface space5 and by competing for time and resources7.The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand interdependencies and interrelationships between them, with the potential to enhance their sustainability and resilience. Indeed, no infrastructure system works in isolation. All infrastructure systems—including transport, water, wastewater, electricity, gas, and telecommunications—are interdependentUnderstand the correlation between electricity consumption and traffic volume.Investigate the temporal relationships between electricity consumption and traffic volume.Investigate the spatial relationships between electricity consumption and traffic volume.Develop models to predict electricity consumption based on traffic volume.In this study, by using zip code-level electricity data as well as traffic loop detector data, we seek to identify and understand interrelationships between travel demand and electricity demand. More precisely, using traffic data to count the number of vehicles entering and exiting a zip code can capture the number of people in a zip code at a given time who may be in buildings otherwise, consuming electricity. Concurrently, a decrease in electricity consumption can express that people have left a building and may use a vehicle, generating traffic. In this study, we use electricity consumption data of several zip codes in Chicago at 30-min intervals for November 2017. To achieve our goal, we use Long Short Term Memory (LSTM) network—a type of deep learning model—to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. The specific objectives of the study are to:In the next section, we review the literature on electricity consumption and traffic modeling, and on interrelationships between infrastructure systems in cities. After, we describe the electricity consumption and traffic datasets used in the study. Next, we go over the results by addressing each objective sequentially, and we then discuss these results. Finally, we explain in detail the methodological approach utilized in the study.8. The stable and uninterrupted operation of the power grid plays a vital role in economic development, national security, and overall social welfare. As of this writing, electricity cannot be cheaply and effectively stored in required massive amounts. As a result, electric utilities and other power market players must forecast electricity consumption in the (a) short-term (few minutes to hours), (b) mid-term (hours to a day ahead), and (c) long-term  in generation, transmission, and distribution networks. Thanks to the deployment of smart meters, predictions have become generally more accurate. This accurate forecasting of electricity consumption levels is crucial for power systems, and the selected method for making predictions provides a better understanding of the dynamics of the system and can even help ease operating costs for market players. The traditional predictive techniques include the construction of mathematical and statistical models such as auto-regressive and moving average (ARMA) models9; auto-regressive integrated moving average (ARIMA) models10; multiple linear regression (MLR) and principal component analysis (PCA) models11; gray models (GM)12; and Kalman filter-based (KF) models13. Nonetheless, traditional statistical models are known to be limited. For instance, GM models are not always effective for electrical load forecasting but work better for addressing small sample problems14 and ARMA models may fail to consider the influence of random variables other than in typical time series forecasting methods14. This means that traditional statistical models work well in normal daily conditions, but they become less reliable while dealing with meteorological, sociological, and economic changes15 or with relations to other systems.The electricity power grid is a complex system with many components18 fuzzy-logic-based algorithms20, genetic-algorithm-based (GA) neural network21, support vector machine (SVM)22, tree-based models25, LSTM-based neural network26; single hidden layer network configurations with random weights (RWSLFN)27, and multilayer perceptron (MLP)28 to name a few. In the literature, LSTM has been shown to perform particularly well on time series data for a range of applications, including to predict the spread of COVID-1932. Specifically looking at traffic forecasting and flow prediction, several studies34 also found that LSTM performed better than traditional techniques like ARIMA or other ML techniques like support vector regression (SVR). In this study, we have opted to solely use LSTM as our main goal is not to find the best performing model but to investigate the presence of interrelationships between electricity consumption and traffic volume.To deal with complex nonlinear relationships, machine learning (ML) and deep learning techniques are generally preferred. The following techniques are mentioned in the literature: artificial neural networks (ANN)51, electricity consumption is linked to myriads of variables, from urban characteristics  and building characteristics  to weather characteristics  and socio-economic and demographic characteristics . Yet, this list is not exhaustive. As infrastructure systems are interdependent and interrelated by nature52, electricity consumption is also linked to demand patterns for other infrastructure services, such as residents commute time, traffic, and urban mobility patterns, suggesting that traffic network data can be used as a source of information to predict electricity consumption as well53. To date, little research has been carried out and not many studies are available that focus on the interrelation between travel and electricity demand. Few studies explore the causal interdependencies between electricity, transport, and weather data54. Gilanifar et al.55 developed a Bayesian Gaussian Process model that explores usage of electricity to enhance short-term load forecasting. Aparicio et al.56 studied the dependencies between power demand and road traffic data using linear correlation and compare the results with other standard features, such as historical load and temperature.As many studies demonstrate57. Each measurement in the dataset represents the total electricity consumed (in kWh) for a specific customer in a certain time interval (30 min). We decided to build our research on this dataset because we assume that the raw high-resolution interval data that we get directly from the automated metering infrastructure (AMI) have a high level of accuracy and fidelity.In this article, we work with anonymized energy usage data in 30-min intervals at the zip code level within the city of Chicago collected by the local utility Commonwealth Edison (ComEd) and accessible (for a fee)58. It is often used to identify energy use trends and peaks in the interest of anomaly detection59 and to make predictions of electricity consumption60 to improve the stability of the power grid. Household data include load shapes measured at the household level considers seasonal and daily fluctuations and show significant differences in electricity consumption during the day, week, month, and year. We are interested in observing the loads in one month with a specific focus on the time of the day and the day of the week. We used residential electricity consumption from 28 zip codes located along the main transport corridors of Chicago: I-290, I-90, I-55, I-57, and I-94 interstate expressways for the month of November 2017.Interval data from AMI has become widely available to utilities throughout the U.S.61. Although Martínez et al.62 observes a potential privacy issue of simple anonymization methods, the distribution of fine-grained data is normally considered acceptable as long as they cannot be linked to the households they originate from through an anonymization process63.While beneficial for both utilities and customers, data collected and utilized using AMI systems have caused concerns regarding customers’ privacyIn this study, we use data that consists of fine-grained records of electricity consumption aggregated by 5-digit zip codes where specific identifiers, including but not limited to name, address, and electric account number, are omitted. Table Traffic volume is captured by loop detectors on the Chicago expressway network. These data are collected by the Gateway Traveler Information System and provided by the Illinois Department of Transportation (IDOT). For this study, 211 loop detectors across Chicago from the Kennedy (I-90/94), Eden (I-94), Eisenhower (I-290), Stevenson (I-55), Dan Ryan (I-90/94), Bishop Ford (I-94), and I-57 expressways are used. Each loop detector includes the number of cars that pass a point in the last 5 min. Standard data cleaning processes were applied to remove missing and erroneous data points that may originate from detector malfunction, pavement condition, or from any other reason. Finally, we aggregated traffic volumes to 30-min time periods to be able to merge the traffic dataset with the electricity consumption dataset. Table r correlation coefficient to measure the linear relation between electricity consumption and traffic volume. Specifically, we calculate the Pearson coefficients in three levels: loop detector, zip code, and citywide.First, we can look at the correlation between two time-series datasets, traffic volume and electricity consumption. For that, we utilize the Pearson r value for each loop detector across the city. Figure r values. They are distributed between 0.004 and 0.81. The highest frequency of Pearson r values  is in the range  should be updated; the output gate determines the portion of the state that must be outputted; and the forget gate decides the part of the information that needs to be forgotten and eliminated from the previous cell state h[t-1]. The main flow of information happens through a cell state. The cell state is updated in a forward process and the output is computed as displayed in Eq.  is a sigmoid function, g1(·) and g2(·) denote the point wise nonlinear activation function, (∙) denotes the entry wise multiplication between two vectors, Ro, Ru, Rh, and Rf represents weight matrices of the recurrent connections, Wo, Wu, Wh, and Wf are weight matrices for the inputs of LSTM cells, bo, bu, bf, and bh are bias vectors5. The LSTM model was developed in Python (v3.7.3) using the Keras (v2.2.4) Deep Learning Library that itself uses TensorFlow (v2.0.0b0) in the backend.In sequence prediction problems, Long Short-Term Memory (LSTM) networks are a specific type of RNN that can learn the dependency in the sequence of time-series data. Since there could be a lag between the events of interest in a time series, these networks can perform well with different types of problem such as classification, processing, and prediction using time series data. One important issue in the standard RNN models is the inefficiency of the model to learn when there are time lags greater than five to ten discrete time steps between the input data target variable that can cause a vanishing gradient—that is, the gradient is too small, preventing the weight from changing its value. LSTMs were developed to cope with the problem of vanishing gradient. They can learn to connect minimal time lags when there exist many discrete time steps by enforcing constant error flow through special units, called cells; see Eqs.  and 3)..3). In Ld in Eq. :2\documeIn this study, the dataset is split into two groups: the first 22 days of November for training and the last 8 days of November for testing. The groups were not split randomly on purpose to ensure both the training and testing sets had weekdays and weekends. Moreover, it is a common practice when modeling time series to use earlier data for training and later data for testing. The premise is that a good model should be able to capture new, unseen trends. We kept the same practice even our goal is not to develop the best performing model, but to study interrelationships.Around 250 LSTM models were trained and compared to select optimal hyperparameters. The hyperparameters used in the end are as follows: number of epochs: 200; batch size: 50; learning rate: 0.001, optimizer: Adam; activation function: sigmoid; loss function: Binary crossentropy.In terms of performance, we use goodness of fit r value, defined as:To calculate the correlation between two time-series we use Pearson


Dysmobility Syndrome (DS) is characterized as an accumulation of clinical risk factors for functional disability, such as osteoporosis, sarcopenia, and obesity. Neurological disorders that affect the motor and sensory systems can also contribute to the condition, resulting in gait and muscle strength disturbances, as well as a history of falls and fractures. The study aimed to determine the association between fat distribution in different body areas and the odds of older adults developing DS, as there is still uncertainty about the accumulation of fat in which area is most closely linked to the condition.This cross-sectional study was conducted according to the data from the second phase of the Bushehr Elderly Health Cohort (BEH). Dysmobility Syndrome was defined based on the co-occurrence of at least three outcomes of its criteria. Body composition was measured using dual-energy X-ray absorptiometry (DXA) and anthropometric studies. For evaluating the relationship, multivariate logistic regression and adjusted univariate linear regression were used.Of 2,359 who were recruited in the study, 1,277 participants (54.13%) had DS. According to the final logistic regression model in the limb region, FM and FM to FFM ratios were significantly associated with DS . Also, In the trunk region, the FM and FM to FFM ratio were significantly related to the odds of DS, although this relationship was weaker than in the limbs region .Our findings indicate that a higher regional and whole-body amount of fat mass rather than fat-free mass is closely linked to an increased risk of DS, particularly in the elderly population. Notably, higher fat mass in the limbs  is associated with greater odds of DS, while a higher android-to-gynoid fat mass ratio is associated with lower DS risk. Screening fat mass distribution in older individuals can be a valuable strategy for promptly diagnosing DS, implementing interventions to prevent disabilities, and improving their quality of life. The Obesity epidemic is a serious public health issue for different societies, especially for the older people around the world –3, whichThe prevalence of DS varies according to the measurements used to define the syndrome and the selected population . Among 6Although there are no accurate statistics on the status of DS in Iran, but due to the aging of the population, the prevalence of related functional disabilities, including sarcopenia, osteoporosis, and obesity, is increasing in Iran .The risk factors for DS are female sex and older age , 14, 15,Studies have found that if older people suffer from obesity, the risk of falling and the prevalence of immobility in them increases ; These aNotably, although Fat Mass (FM) is a crucial component to the diagnosis of DS, there remains a lack of clarity regarding which specific area of the body's fat distribution is most closely linked to this condition. Therefore, the present study aimed to investigate the relationship between anthropometric indices, body composition, and, particularly, the accumulation of fat in different areas of the body  with the likelihood of older adults having DS. To the best of our knowledge, there is no study that has examined the association between body composition and fat distribution with DS.This cross-sectional study was conducted as part of first phase (Stage II) of the Bushehr Elderly Health Program (BEH) project, a prospective and population-based cohort study in Bushehr, southern Iran. The full methodology of this project has been reported in detail previously , 29. DurIn a confidential manner, using a valid questionnaire , 29, the2  or ≤ 7.26 kg/m2 , Osteoporosis: T-score of ≤  − 2.5 at lumbar spine, femoral neck, or total proximal femur, Low grip strength: Hand-held dynamometer: < 20 kg ; < 30 kg , Slow gait speed: < 1.0 m/s (comfortable speed), Falls in the preceding year: Self-report of one or more falls over the past 12 months , neck circumference (NC), Waist circumferences (WC), Hip circumference (HC), and Waist to hip ratio (WHR) were collected.Body composition assessment was performed by using dual-energy X-ray absorptiometry (DXA) , with minimal exposure to radiation. Using Hologic DXA system, APEX software, analysis of raw scans of bone breakdown, muscle tissue, and fat tissue, for different regions of the whole body, android and gynoid area, trunk, legs, and arms, was performed. To ensure accurate DEXA measurements, individuals were required to avoid wearing clothing with metal objects and jewelry, undergo fasting before the scan, maintain hydration, share information about recent contrast material injections, and provide details about any surgeries or metal Implants. These conditions were essential for improving the precision of bone density and body composition assessments. Elsewhere, the details of the examinations and measurements are described .Total body mass (BM) and fat mass (FM) represent the sum of BM and FM across all reported regions. Total body and regional fat-free mass (FFM) was calculated by subtracting FM from BM. Limbs FM and FFM were obtained from the sum of FM and FFM of the arms and legs.Smoking was classified into three categories: current cigarette or hookah users, former cigarette or hookah users who quit, and those who never smoke or hookah. The classification of alcohol consumption was also done in four groups: regular, occasional, non-drinkers, and those who refused to report their status. Using medical records, chronic diseases were defined as having a history of the following diseases; hypertension , chronic kidney disease (CKD), liver disease, cardiovascular diseases, thyroid disease, diabetes , rheumatoid arthritis and osteoarthritis. The prevalence of chronic diseases was also reported in the following three categories: No history of chronic disease, history of one chronic disease and history of two or more chronic diseases.The level of physical activity was evaluated, using a 20-items questionnaire designed for the Iranian population , 31. EacOther variables were defined as follows: age (years), sex , marital status  and income level was considered based on the report of the Social Security Organization and the government, in three income groups: low, medium and high, respectively.This study is approved by the Research Ethics Organization and the Research Committee of Bushehr University of Medical Sciences (IR.BPUMS.REC.1401.172) and the Endocrine and Metabolism Research Institute affiliated to Tehran University of Medical Sciences. . All methods were carried out in accordance with the approved protocols and in accordance with the Helsinki Declaration.p-value < 0.05 was taken as statistically significant for all analyses.Categorical variables were presented as numbers and percentages whereas mean values and standard deviation (SD) were used for continuous variables. Data normality was checked using the Kolmogorov–Smirnov test. Differences between the two groups were evaluated using t-test and weighted Chi-square for continuous and categorical variables, respectively. Multivariable logistic regression analyses were performed to assess the association between DS and a variety of anthropometric and body composition indices. We selected relevant confounders based on an extensive literature search for which a significant clinical and pathophysiological association with desired outcome and/or exposures were first assessed by univariate regression models; then, statistically significant covariates, which have clinical implications were included in the multivariable logistic regression models. Covariates were adjusted as: model 1 = sex; model 2 = model 1 + age; model 3 = model 2 + number of chronic diseases, income and marital; model 4 = model 3 + smoking statues and physical activity. All analysis was done in Stata MP (version 17), and p-value < 0.001) and older . There were no statistically significant differences between participants with and without DS in smoking, alcohol consumption, and prevalence of chronic diseases, whereas marital state and income level had statistically significant differences . In married subjects, the prevalence of the DS was lower. People with high income level were also less affected by DS.Of 2,359 who were recruited in the study, 1,277 participants (54.13%) had DS. The mean age of all participants was 69.3 ± 6.3, and 51.5% were female. Participants with DS were mostly female , Appendicular Skeletal Muscle (ASM), Lumbar and Hip bone marrow density were statistically lower in participants with DS. Furthermore, it was observed that the individuals with DS exhibit sedentary levels of physical activity (no activity and sedentary: 82.6% vs. 69.5%), display inferior physical performance (8.84 vs 9.97) and lower Gait Speed (0.71 vs 1.0) and Handgrip (17.64 vs. 27.62) (Table p < 0.001).According to Table p-value < 0.001 and OR (95%CI) = 3.42 (1.95 to 5.99), p-value < 0.001, respectively]. In the trunk region, the FM and FM to FFM ratio were significantly related to the odds of DS, although this relationship was weaker than limb region . This association was also established in the total body .Table p-value = 0.952). However, an inverse association between android to gynoid FM ratio and the odds of DS was observed .In addition, in the final models, there was no significant association between trunk to limb FM ratio and DS .Figure This cross-sectional study aimed to assess the association between regional FM distribution and incident of DS in an older population. Our results indicated that FM and especially the FM to FFM ratio in different regions of the body  are associated with increased odds of DS, in such a way that this relationship is stronger for the limbs region. Also, interestingly, a protective association between the android to gynoid FM ratio with the odds of DS was observed.Interestingly, the average BMI of the DS group in our population was found to be significantly lower than that of the normal group (26.96 vs. 27.81 kg/m2). This observation may be attributed to the fact that although the total amount of FM was similar in both groups, the DS group had significantly lower LBM and ASM, leading to a higher FM% and limiting the efficacy of BMI in accurately reflecting the correlation with DS that highlight the importance of considering the fat distribution indexes instead of BMI. As we know, during the aging process, the occurrence of sarcopenia, or in other words, a decrease in LBM and an increase in body FM, is directly related to the various disabling components of DS , 32. EviBased on our results, the numbers of individuals with lower physical activity tend to be higher in those diagnosed with DS. With the passage of time and the natural aging process, the human body experiences a series of changes that have implications for both muscle mass and functionality. Physical activity has the potential to mitigate the adverse impacts of aging on muscle mass and function. Research indicates that both aerobic and strength training can significantly enhance strength and motor performance in older individuals –44. A siThe primary objective of our research study was to investigate the potential impact of the distribution of FM throughout different regions of the body on the odds of developing DS. In a recent study , it was Regarding the adjusted variables in the statistical models, age and sex as background variables based on previous studies were associated with DS , 54, 55.To the best of our knowledge, this study was the first to examine the association between body fat distributions and DS, with data derived from a large-scale population-based cohort study. The prevalence of the DS in our study population was estimated as approximately 54%, which was higher than previous studies (22%–34%) . The higHigher body FM is associated with a higher risk of the DS. Also, interestingly, our results show that in older people, higher limbs FM is associated with a greater odd of DS, in a way that a higher ratio of android to gynoid FM is associated with a lower risk of the DS. Therefore, the screening of FM distribution in older people can be a valuable complementary strategy for the rapid diagnosis of the DS and introduction of interventions for the prevention of related disabilities and improving the quality of life in this population.