Prevalence and effect on survival of pre-treatment sarcopenia in patients with hematological malignancies: a meta-analysis

Background & aimsEvidence regarding the prevalence of pre-treatment sarcopenia and its impact on survival in patients with hematological malignancies (HM) varies across studies. We conducted a systematic review and meta-analysis to summarize this discrepancy.MethodsPubMed, Embase and Cochrane library were systematically searched for relevant studies. Outcomes assessed were: prevalence of pre-treatment sarcopenia, overall survival (OS), progression-free survival (PFS) and complete response (CR). Weighted mean proportion, odds ratios (ORs) and hazard ratios (HRs) were estimated using a fixed-effects and a random-effects model.ResultsA total of 27 retrospective cohort studies involving 4,991 patients were included in this study. The prevalence of pre-treatment sarcopenia was 37.0% (95% CI: 32.0%-42.0%) in HM patients <60 years and 51.0% (95% CI: 45.0%-57.0%) in≥60 years. Patients with leukemia had the lowest prevalence, compared with those with other HM (38.0%; 95% CI: 33.0%-43.0%; P = 0.010). The presence of sarcopenia was independently associated with poor OS (HR = 1.57, 95% CI = 1.41-1.75) and PFS (HR = 1.50, 95% CI = 1.22-1.83) throughout treatment period, which may be partially attributed to decreased CR (OR = 0.54, 95% CI = 0.41-0.72), particularly for BMI ≥ 25 (P = 0.020) and males (P = 0.020).ConclusionSarcopenia is highly prevalent in patients with HM and an adverse prognostic factor for both survival and treatment efficacy. HM and sarcopenia can aggravate each other. We suggest that in future clinical work, incorporating sarcopenia into risk scores will contribute to guide patient stratification and therapeutic strategy, particularly for the elderly.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier (CRD42023392550)

An investigation into working behavior characteristics of parabolic CFST arches applying structural stressing state theory

This paper conducts the experimental and simulative analysis of stressing state characteristics for parabolic concretefilled steel tubular (CFST) arches undergoing vertical loads. The measured stain data is firstly modeled as the generalized strain energy density (GSED) to describe structural stressing state mode. Then, the normalized GSED sum Ej,norm at each load Fj derives the Ej,norm-Fj curve reflecting the stressing state characteristics of CFST arches. Furthermore, the Mann-Kendall criterion is adopted to detect the stressing state change of the CFST arch during its load-bearing process, leading to the revelation of a vital stressing state leap characteristic according to the natural law from quantitative change to qualitative change of a system. The revealed qualitative leap characteristic updates the existing definition of the CFST arch’s failure load. Finally, the accurate formula is derived to predict the failure/ultimate loads of CFST arches. Besides, a method of numerical shape function is proposed to expand the limited strain data for further analysis of the stressing state submodes. The GSED-based analysis of structural stressing state opens a new way to recognize the unseen working behavior characteristics of arch structures and the updated failure load could contribute to the improvement on the structural design codes

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R2) as the evaluation indicator (R2: 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R2: 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment

BmILF and I-motif Structure Are Involved in Transcriptional Regulation of \u3cem\u3eBmPOUM2\u3c/em\u3e in \u3cem\u3eBombyx mori\u3c/em\u3e

Guanine-rich and cytosine-rich DNA can form four-stranded DNA secondary structures called G-quadruplex (G4) and i-motif, respectively. These structures widely exist in genomes and play important roles in transcription, replication, translation and protection of telomeres. In this study, G4 and i-motif structures were identified in the promoter of the transcription factor gene BmPOUM2, which regulates the expression of the wing disc cuticle protein gene (BmWCP4) during metamorphosis. Disruption of the i-motif structure by base mutation, anti-sense oligonucleotides (ASOs) or inhibitory ligands resulted in significant decrease in the activity of the BmPOUM2 promoter. A novel i-motif binding protein (BmILF) was identified by pull-down experiment. BmILF specifically bound to the i-motif and activated the transcription of BmPOUM2. The promoter activity of BmPOUM2 was enhanced when BmILF was over-expressed and decreased when BmILF was knocked-down by RNA interference. This study for the first time demonstrated that BmILF and the i-motif structure participated in the regulation of gene transcription in insect metamorphosis and provides new insights into the molecular mechanism of the secondary structures in epigenetic regulation of gene transcription

Variable stiffness design of redundantly actuated planar rotational parallel mechanisms

Redundantly actuated planar rotational parallel mechanisms (RAPRPMs) adapt to the requirements of robots under different working conditions by changing the antagonistic internal force to tune their stiffness. The geometrical parameters of the mechanism impact the performances of modulating stiffness. Analytical expressions relating stiffness and geometrical parameters of the mechanism were formulated to obtain the necessary conditions of variable stiffness. A novel method of variable stiffness design was presented to optimize the geometrical parameters of the mechanism. The stiffness variation with the internal force was maximized. The dynamic change of stiffness with the dynamic location of the mechanism was minimized, and the robustness of stiffness during the motion of the mechanism was ensured. This new approach to variable stiffness design can enable off-line planning of the internal force to avoid the difficulties of on-line control of the internal force