Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma.

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

Identification of a miRNA-mRNA regulatory network for post-stroke depression: a machine-learning approach.

Objective: The study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA-mRNA regulatory network to reveal its potential pathogenesis. Methods: The transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA-mRNA network was constructed. Results: Using the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA-mRNA network was developed. Conclusion: The study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.

Altered functional connectivity of the default mode and frontal control networks in patients with insomnia.

AIMS: The purpose of this study was to investigate the association between spontaneous regional activity and brain functional connectivity, which maybe can distinguish insomnia while being responsive to repetitive transcranial magnetic stimulation (rTMS) treatment effects in insomnia patients. METHODS: Using resting-state functional magnetic resonance imaging data from 38 chronic insomnia patients and 36 healthy volunteers, we compared the amplitude of low-frequency fluctuations (ALFF) between the two groups. Of all the patients with insomnia, 20 received rTMS for 4 weeks, while 18 patients received a 4-week pseudo-stimulation intervention. Seed-based resting-state functional connectivity (RSFC) analysis was conducted from regions with significantly different ALFF values, and the association between RSFC value and Pittsburgh Sleep Quality Index score was determined. RESULTS: Our results revealed that insomnia patients presented a significantly higher ALFF value in the posterior cingulate cortex (PCC), whereas a significantly lower ALFF value was observed in the superior parietal lobule (SPL). Moreover, significantly reduced RSFC was detected from both PCC to prefrontal cortex connections, as well as from left SPL to frontal pole connections. In addition, RSFC from frontal pole to left SPL negatively predicted sleep quality (PSQI) and treatment response in patients' group. CONCLUSION: Our findings suggest that disrupted frontoparietal network connectivity may be a biomarker for insomnia in middle-aged adults, reinforcing the potential of rTMS targeting the frontal lobes. Monitoring pretreatment RSFC could offer greater insight into how rTMS treatments are responded to by insomniacs.

Rehabilomics: A state-of-the-art review of framework, application, and future considerations.

Rehabilomics is an important research framework that allows omics research built upon rehabilitation practice, especially in function evaluation, outcome prediction, and individualized rehabilitation. In the field of rehabilomics, biomarkers can serve as objectively measured indicators for body functioning, so as to complement the International Classification of Functioning, Disability, and Health (ICF) assessment. Studies on traumatic brain injury (TBI), stroke, and Parkinson's disease have shown that biomarkers (such as serum markers, MRI, and digital signals derived from sensors) are correlated with diagnosis, disease severity, and prognosis. Rehabilomics also examines a wide range of individual biological characteristics in order to develop personalized rehabilitation programs. Secondary prevention and rehabilitation of stroke have already adopted a rehabilomic approach to individualize treatment programs. Mechanisms of non-pharmacological therapies are expected to be unveiled in light of rehabilomics research. When formulating the research plan, learning from established databases is recommended and a multidisciplinary collaborative team is warranted. Although still in its infancy, the advancement and incorporation of rehabilomics has the potential to make a significant impact on public health.

BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study.

In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09801-6.

Validation of a Disability Assessment Tool Based on the International Classification of Functioning, Disability, and Health in the Chinese Context.

Background: The common standards of disability assessment for long-term care (LTC) insurance are currently absent. The International Classification of Functioning, Disability and Health (ICF) was designed for a better description of health and functioning, which could fill the demand gap for the standards of disability assessment and be a promising tool for the development of LTC insurance system. Objectives: To validate a disability assessment scale for disabled elderly individuals based on the ICF for LTC in the Chinese context. Methods: The present study is a cross-sectional study. A disability assessment tool based on the ICF was developed by referring to other assessment tools and an expert consensus meeting in the initial phase of the study. The developed tool was used to evaluate 1,610 elderly individuals in the LTC institutions. The Cronbach's α coefficient and split-half reliability were applied to test the internal consistency of the tool, while the Interclass correlation coefficients (ICCs) were used to evaluate the interrater reliability (IRR). Factor analysis was performed to verify the construct validity of the tool. The scores from the Medical Outcomes Short Form-12 (SF-12) were correlated with that from the disability assessment tool, to assess the criterion-related validity. Results: The Cronbach's α coefficient and split-half reliability of the disability assessment tool were 0.969 and 0.877, respectively. The ICCs of the sum scale was 0.85, and the ICCs of each of the 20 items in the scale ranged from 0.78 to 0.94. The items were divided into three factors through analysis, which is consistent with the structure expectation. The scores of each item and the sum score of the disability assessment scale were negatively correlated with the scores of the physical and psychological fields in SF-12 (p < 0.001). Overall, the data indicated that the tool was characterized by good internal consistency, IRR, construct validity, and criterion-related validity. Conclusions: The disability assessment tool based on the ICF is a reliable and valid tool for the collection of information on functioning across various LTC settings. The information of disability provided evidence for the distribution of LTC service and guided the development of LTC insurance standards.

Effectiveness of a Novel Contralaterally Controlled Neuromuscular Electrical Stimulation for Restoring Lower Limb Motor Performance and Activities of Daily Living in Stroke Survivors: A Randomized Controlled Trial.

Background: Contralaterally controlled neuromuscular electrical stimulation (CCNMES) is a novel electrical stimulation treatment for stroke; however, reports on the efficacy of CCNMES on lower extremity function after stroke are scarce. Objective: To compare the effects of CCNMES versus NMES on lower extremity function and activities of daily living (ADL) in subacute stroke patients. Methods: Forty-four patients with a history of subacute stroke were randomly assigned to a CCNMES group and a NMES group (n = 22 per group). Twenty-one patients in each group completed the study per protocol, with one subject lost in follow-up in each group. The CCNMES group received CCNMES to the tibialis anterior (TA) and the peroneus longus and brevis muscles to induce ankle dorsiflexion motion, whereas the NMES group received NMES. The stimulus current was a biphasic waveform with a pulse duration of 200 µs and a frequency of 60 Hz. Patients in both groups underwent five 15 min sessions of electrical stimulation per week for three weeks. Indicators of motor function and ADL were measured pre- and posttreatment, including the Fugl-Meyer assessment of the lower extremity (FMA-LE) and modified Barthel index (MBI). Surface electromyography (sEMG) assessments included average electromyography (aEMG), integrated electromyography (iEMG), and root mean square (RMS) of the paretic TA muscle. Results: Values for the FMA-LE, MBI, aEMG, iEMG, and RMS of the affected TA muscle were significantly increased in both groups after treatment (p < 0.01). Patients in the CCNMES group showed significant improvements in all the measurements compared with the NMES group after treatment. Within-group differences in all post- and pretreatment indicators were significantly greater in the CCNMES group than in the NMES group (p < 0.05). Conclusion: CCNMES improved motor function and ADL ability to a greater extent than the conventional NMES in subacute stroke patients.

A Novel lncRNA Panel Related to Ferroptosis, Tumor Progression, and Microenvironment is a Robust Prognostic Indicator for Glioma Patients.

Objective: To establish a lncRNA panel related to ferroptosis, tumor progression, and microenvironment for prognostic estimation in patients with glioma. Methods: LncRNAs associated with tumor progression and microenvironment were screened via the weighted gene co-expression network analysis (WGCNA). Overlapped lncRNAs highlighted in WGCNA, related to ferroptosis, and incorporated in Chinese Glioma Genome Atlas (CGGA) were identified as hub lncRNAs. With expression profiles of the hub lncRNA, we conducted the least absolute shrinkage and selection operator (LASSO) regression and built a ferroptosis-related lncRNA signature to separate glioma patients with distinct survival outcomes. The lncRNA signature was validated in TCGA, the CGGA_693, and CGGA_325 cohorts using Kaplan-Meier survival analysis and ROC curves. The ferroptosis-related lncRNA panel was validated with 15 glioma samples using quantitative real-time PCR (qRT-PCR). Multivariate Cox regression was performed, and a nomogram was mapped and validated. Immune infiltration correlated to the signature was explored using TIMER and CIBERSORT algorithms. Results: The present study identified 30 hub lncRNAs related to ferroptosis, tumor progression, and microenvironment. With the 30 hub lncRNAs, we developed a lncRNA signature with distinct stratification of survival chance in patients with glioma in two independent cohorts (HRs>1, p < 0.05). The lncRNA signature revealed a panel of 14 lncRNAs, i.e., APCDD1L-AS1, H19, LINC00205, LINC00346, LINC00475, LINC00484, LINC00601, LINC00664, LINC00886, LUCAT1, MIR155HG, NEAT1, PVT1, and SNHG18. These lncRNA expressions were validated in clinical specimens using qRT-PCR. Robust predictive accuracies of the signature were present across different datasets at multiple timepoints. With univariate and multivariate regressions, we demonstrated that the risk score based on the lncRNA signature is an independent prognostic indicator after clinical factors were adjusted. A nomogram was constructed with these prognostic factors, and it has demonstrated decent classification and accuracy. Additionally, the signature-based classification was observed to be correlated with multiple clinical characteristics and molecular subtypes. Further, extensive immune cells were upregulated in the high-risk group, such as CD8+ T cell, neutrophil, macrophage, and myeloid dendritic cell, indicating increased immune infiltrations. Conclusion: We established a novel ferroptosis-related lncRNA signature that could effectively stratify the prognosis of glioma patients with adequate predictive performance.

Efficacy of a Novel Exoskeletal Robot for Locomotor Rehabilitation in Stroke Patients: A Multi-center, Non-inferiority, Randomized Controlled Trial.

Objective: To investigate the efficacy and safety of a novel lower-limb exoskeletal robot, BEAR-H1 (Shenzhen Milebot Robot Technology), in the locomotor function of subacute stroke patients. Methods: The present study was approved by the ethical committee of the First Affiliated Hospital of Nanjing Medical University (No. 2019-MD-43), and registration was recorded on the Chinese Clinical Trial Registry with a unique identifier: ChiCTR2100044475. A total of 130 patients within 6 months of stroke were randomly divided into two groups: the robot group and the control group. The control group received routine training for walking, while in the robot group, BEAR-H1 lower-limb exoskeletal robot was used for locomotor training. Both groups received two sessions daily, 5 days a week for 4 weeks consecutively. Each session lasted 30 min. Before treatment, after treatment for 2 weeks, and 4 weeks, the patients were assessed based on the 6-minute walking test (6MWT), functional ambulation scale (FAC), Fugl-Meyer assessment lower-limb subscale (FMA-LE), and Vicon gait analysis. Results: After a 4-week intervention, the results of 6MWT, FMA-LE, FAC, cadence, and gait cycle in the two groups significantly improved (P < 0.05), but there was no significant difference between the two groups (P > 0.05). The ratio of stance phase to that of swing phase, swing phase symmetry ratio (SPSR), and step length symmetry ratio (SLSR) was not significantly improved after 4 weeks of training in both the groups. Further analyses revealed that the robot group exhibited potential benefits, as the point estimates of 6MWT and Δ6MWT (post-pre) at 4 weeks were higher than those in the control group. Additionally, within-group comparison showed that patients in the robot group had a significant improvement in 6MWT earlier than their counterparts in the control group. Conclusions: The rehabilitation robot in this study could improve the locomotor function of stroke patients; however, its effect was no better than conventional locomotor training.

Profile of gut microbiota in patients with traumatic thoracic spinal cord injury and its clinical implications: a case-control study in a rehabilitation setting.

Gut microbiota are the candidate biomarkers for neurogenic bowel dysfunction (NBD) in patients with spinal cord injury (SCI). We aimed to identify the common features between patients with varying degree of thoracic SCI and healthy individuals and subpopulations of microbiota correlated with the serum biomarkers. Twenty-one patients with complete thoracic SCI (CTSCI), 24 with incomplete thoracic SCI (ITSCI), and 24 healthy individuals (HC) were enrolled in this study. Fresh stool samples and clinical data were collected from all participants, and their bowel functions with SCI were assessed. Microbial diversity and composition were analyzed by sequencing the 16S rRNA gene. The features of gut microbiota correlated with the serum biomarkers and their functions were investigated. The mean NBD score of patients with CTSCI was higher than that of patients with ITSCI. Diversity of the gut microbiota in SCI group was reduced, and with an increase in the degree of damage, alpha diversity had decreased gradually. The composition of gut microbiota in patients with SCI was distinct from that in healthy individuals, and CTSCI group exhibited further deviation than ITSCI group compared to healthy individuals. Four serum biomarkers were found to be correlated with most differential genera. Patients with thoracic SCI present gut dysbiosis, which is more pronounced in patients with CTSCI than in those with ITSCI. Therefore, the gut microbiota profile may serve as the signatures for bowel and motor functions in patients with thoracic SCI.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma.

BACKGROUND: CD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG). METHODS: The prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways. RESULTS: Pan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG. CONCLUSIONS: Integrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.

Effects of Prism Adaptation on Unilateral Neglect After Stroke: An Updated Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: The aim of this study was to investigate the effects of prism adaptation (PA) on unilateral neglect after stroke. METHODS: Randomized clinical trials, published up to January 31, 2020, comparing PA with neutral goggles or no goggles were systematically searched and included. Two independent reviewers performed data extraction and assessed the quality of studies using the Physiotherapy Evidence Database scale. RESULTS: A total of seven randomized trials, involving 211 participants, satisfied the inclusion criteria. There was no significant immediate benefit of PA as measured by Behavioral Inattention Test (BIT) (weighted mean difference [WMD], 5.10; [95% confidence interval (CI), -6.68 to 16.88]), behavioral subset (BIT-B; WMD, 3.40 [95% CI, -3.97 to 10.76), conventional subset (BIT-C; WMD, 9.98 [95% CI, -0.42 to 20.38]), and Catherine Bergego Scale (WMD, -0.52 [95% CI, -1.98 to 0.93]). No statistical difference was observed between PA and control on the long-term effect (BIT: WMD, 1.92 [95% CI, -9.34 to 13.18]; BIT-B: WMD, -3.28 [95% CI, -11.89 to 5.34]; BIT-C: WMD, 2.66 [95% CI, -10.35 to 11.67]; Catherine Bergego Scale: WMD, -1.22 [95% CI, -3.05 to 0.62]). CONCLUSIONS: PA did not show a greater improvement on neglect symptoms in post-stroke patients with unilateral neglect, compared with placebo or no treatment. These findings do not support the routine use of PA in patients with unilateral neglect after stroke.

Noninvasive Brain Stimulation Does Not Improve Neuropathic Pain in Individuals With Spinal Cord Injury: Evidence From a Meta-Analysis of 11 Randomized Controlled Trials.

OBJECTIVE: The aim of the study was to examine the effectiveness of noninvasive brain stimulation on neuropathic pain in individuals with spinal cord injury. METHODS: A meta-analysis on pain intensity, depression, and anxiety levels was conducted to evaluate the effect of noninvasive brain stimulation on neuropathic pain in individuals with spinal cord injury. The authors searched Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (PubMed), Embase (OvidSP), PsycINFO (OvidSP), and Physiotherapy Evidence Database (PEDro). Randomized controlled trials comparing noninvasive brain stimulation with sham stimulation were included. RESULTS: Eleven studies were selected. The pooled analysis demonstrated no significant effect of repetitive transcranial magnetic stimulation, transcranial direct current stimulation, or cranial electrotherapy stimulation on neuropathic pain reduction after spinal cord injury. In addition, noninvasive brain stimulation showed no beneficial effect over sham stimulation on the improvement of depression, while it yielded a significant reduction of anxiety levels immediately after treatment. Subgroup analysis showed that only cranial electrotherapy stimulation had a significant effect on the reduction of anxiety levels among the three types of noninvasive brain stimulation. CONCLUSIONS: In individuals with spinal cord injury, no significant effects of noninvasive brain stimulation on neuropathic pain and depression were observed. Cranial electrotherapy stimulation may be beneficial for the management of anxiety. These findings do not support the routine use of noninvasive brain stimulation for neuropathic pain in individuals with spinal cord injury.

Identification of aberrantly methylated differentially expressed genes and associated pathways in endometrial cancer using integrated bioinformatic analysis.

Endometrial cancer (EC) is a fatal female reproductive tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. In this study, GSE17025 and GSE40032 were obtained from Gene Expression Omnibus (GEO). "limma" package and Venn diagram tool were used to identify hub genes. FunRich was used for functional analysis. Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) complex. Cancer Genome Atlas (TCGA), GEPIA, immunohistochemistry staining, and ROC curve analysis were carried out for validation. Univariate and multivariate regression analyses were performed to predict the risk score. Compound muscle action potential (CMap) was used to find potential drugs. GSEA was also done. We retrieved seven oncogenes which were upregulated and hypomethylated and 12 tumor suppressor genes (TSGs) which were downregulated and hypermethylated. The upregulated and hypomethylated genes were strikingly enriched in term "immune response" while the downregulated and hypermethylated genes were mainly focused on term "aromatic compound catabolic process." TCGA and GEPIA were used to screen out EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1. Among them, ESPL1 and ROR2 were identified by Cox regression analysis and were used to construct prognostic risk model. The result showed that ESPL1 was a negative independent prognostic factor. Cmap identified aminoglutethimide, luteolin, sulfadimethoxine, and maprotiline had correlation with EC. GSEA results showed that "hedgehog signaling pathway" was enriched. This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported eight hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC.

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes.

BACKGROUND: There has been no report of prognostic signature based on immune-related genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA). METHODS: RNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified via Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n = 284) and test datasets (n = 119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset, and external dataset GSE13507 (n = 165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BLCA. RESULTS: A set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes [hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6-19, P < 0.001]. The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754, and 0.772 at 1-, 3-, and 5-year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4+ T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit. CONCLUSION: The present immune-related signature can effectively classify BLCA patients into high-risk and low-risk groups in terms of survival rate, which may help select high-risk BLCA patients for more intensive treatment.

Prediction of Prolonged Length of Stay for Stroke Patients on Admission for Inpatient Rehabilitation Based on the International Classification of Functioning, Disability, and Health (ICF) Generic Set: A Study from 50 Centers in China.

BACKGROUND This study aimed to develop a risk prediction model for prolonged length of stay (LOS) in stroke patients in 50 inpatient rehabilitation centers in 20 provinces across mainland China based on the International Classification of Functioning, Disability, and Health (ICF) Generic Set case mix on admission. MATERIAL AND METHODS In this cohort study, 383 stroke patients were included from inpatient rehabilitation settings of 50 hospitals across mainland China. Independent predictors of prolonged LOS were identified using multivariate logistic regression analysis. A prediction model was established and then evaluated by receiver operating characteristic (ROC) curve analysis and the Hosmer-Lemeshow test. RESULTS Multivariate logistic regression analysis showed that the type of medical insurance and the performance of daily activities (ICF, d230) were associated with prolonged LOS (P<0.05). Age and mobility level measured by the ICF Generic Set demonstrated no significant predictive value. The prediction model showed acceptable discrimination shown by an area under the curve (AUC) of 0.699 (95% CI, 0.646-0.752) and calibration (χ²=11.66; P=0.308). CONCLUSIONS The risk prediction model for prolonged LOS in stroke patients in 50 rehabilitation centers in China, based on the ICF Generic Set, showed that the scores for the type of medical insurance and the performance of daily activities (ICF, d230) on admission were independent predictors of prolonged LOS. This prediction model may allow stakeholders to estimate the risk of prolonged LOS on admission quantitatively, facilitate the financial planning, treatment regimens during hospitalization, referral after discharge, and reimbursement.

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma.

OBJECTIVE: In the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma. METHODS: Stromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model. RESULTS: We obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells. CONCLUSIONS: The PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

Electrical Stimulation in the Treatment of Hemiplegic Shoulder Pain: A Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: The aim of the study was to determine the effect of electrical stimulation in the treatment of hemiplegic shoulder pain. DESIGN: Eight databases were systematically searched for randomized controlled trials with a treatment duration of at least 2 wks comparing electrical stimulation with sham stimulation or no stimulation for patients with hemiplegic shoulder pain. Shoulder pain on the hemiplegic side after stroke at baseline was required at study selection. The overall effects of electrical stimulation were calculated using a meta-analytic method. RESULTS: Six studies were included. The pooled data indicated that electrical stimulation may have a positive effect for patients with hemiplegic shoulder pain on pain reduction (n = 193, standardized mean difference = -1.89, 95% confidence interval = -3.05 to -0.74) and pain-free external rotation (n = 164, weighted mean difference = 18.92, 95% confidence interval = 7.00 to 30.84). Meta-analysis also showed better recovery of activities of daily living independence in patient groups receiving electrical stimulation (n = 167, weighted mean difference = 8.96, 95% confidence interval = 5.26 to 12.66). CONCLUSIONS: Electrical stimulation may be an effective pain management methodology for hemiplegic shoulders and may contribute to pain-free range of external rotation as well as activities of daily living recovery. However, these results should be interpreted with caution, given the low number of selected studies and risk of potential bias.