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The pathogenesis of acute leukemia involves interaction among genetic alterations. Mutations of IDH1/2 and PHF6 are common and co-exist in some patients of hematopoietic malignancies, but their cooperative effects remain unexplored. In this study, we addressed the question by characterizing the hematopoietic phenotypes of mice harboring neither, Phf6 knockout, Idh2 R172K, or combined mutations. We found that the combined Phf6

Background The assumptions of conventional spatial models cannot estimate the responses across space and over time. Here we propose new spatial panel data models to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD). Methods A longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We developed an algorithm to identify the patient’s residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors. Results In total, 73,995 patients with ESRD were included in this study. The western region had a higher proportion of high incidence rates than the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. We found that most “social environmental factors,” except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate. Conclusion Future comprehensive studies on townships located in higher-risk clusters of ESRD will help in designing healthcare policies for suitable action.

Background Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking. Methods Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342). Results Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44–1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset. Conclusions Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.

S100A8/A9 is a proinflammatory protein and plays an essential role in the pathogenesis of myelodysplastic syndromes (MDS) via the S100A8/A9-Toll-like receptors axis. While S100A8/A9 levels have been used as biomarkers in many inflammatory diseases, their clinical relevance has not been conclusively resolved in MDS. To address this, we used an enzyme-linked immunosorbent assay to quantify S100A8/A9 heterodimers in bone marrow (BM) plasma from 215 MDS patients and compared S100A8/A9 levels across patients with various disease risks and genotypes. S100A8/A9 levels correlated with ASXL1 variant allele frequencies significantly. Moreover, mutant ASXL1 with concurrent RUNX1, STAG2, ZRSR2, or EZH2 mutations was associated with higher S100A8/A9 levels. We further showed that higher S100A8/A9 independently predicted inferior leukemia-free survival and overall survival in MDS patients, irrespective of age, Revised International Prognostic Scoring System subgroups, and known detrimental mutations. Lastly, through deep-sequenced transcriptomic analysis, we demonstrated that higher S100A8/A9 in the BM intimated a perturbed microenvironment with enhanced myeloid-derived suppressor cell-mediated tumor immune escape signal, altered metabolism, and impairment in the functions and quantities of CD8+ T cells and NK cells. S100A8/A9 in the BM microenvironment may be a potential biomarker in the prognostication of MDS and target for novel therapy.

A set of myelodysplasia-related (MDS-R) gene mutations are incorporated into the 2022 European LeukemiaNet risk classification as adverse genetic factors for acute myeloid leukemia (AML) based on their poor prognostic impact on older patients. The impact of these mutations on younger patients (age de novo non-M3 AML, we identified MDS-R mutations in 32.7% of the total cohort, 44.9% of older patients and 23.4% of younger patients. The patients with MDS-R mutations had a significantly lower complete remission rate in both younger and older age groups. With a median follow-up of 9.2 years, the MDS-R group experienced shorter overall survival (P = 0.034 for older and 0.035 for younger patients) and event-free survival (P = 0.004 for older and 0.042 for younger patients). Furthermore, patients with MDS-R mutations more frequently harbored measurable residual disease that was detectable using next generation sequencing at morphological CR than those without MDS-R mutations. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) might ameliorate the negative impact of MDS-R mutations. In summary, AML patients with MDS-R mutations have significantly poorer outcomes regardless of age. More intensive treatment, such as allo-HSCT and/or novel therapies, is warranted for AML patients with MDS-R mutations.

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Cables or suspenders are the critical force-transmitting components of cable-supported bridges, and their timely tension monitoring is consequently the most important issue in the corresponding structural health monitoring. However, very few works regarding the full automation of vibration-based tension estimation have been reported in the literature. To develop a monitoring system of cable tension based on real-time vibration signals, this research first employs an efficient stochastic subspace identification (SSI) method with tailored parameter selection to continuously identify the three frequencies of adjacent modes for the cables of Mao-Luo-Hsi Bridge. More importantly, an automated sieving algorithm is delicately established to obtain the stable modal frequencies by making the best of the specific modal frequency distribution for cables. The ratios between the frequency values identified from SSI analysis are exhaustively checked to systematically extract the qualified cable frequencies and decide their corresponding mode orders. The tension is finally computed with one available cable frequency according to the priority order predetermined by the statistics of identification rate. Demonstrated by analyzing the vibration signals measured from the stay cable of Mao-Luo-Hsi Bridge in real time for two full years, the effectiveness and robustness of this real-time monitoring system have been extensively testified. The long-term success rates for the immediate determination of dependable tension are found to be perfect for 15 of the 18 investigated cables. As for the other three cables, their corresponding success rates are still higher than 99.99% with very few cases of absent or false tension values.

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This research compared the incidence of adjacent segment pathology (ASP) between anterior interbody lumbar fusion (ALIF) treatment and transforaminal lumbar interbody fusion (TLIF) treatment. Seventy patients were included in this retrospective study: 30 patients received ALIF treatment, and 40 patients received TLIF treatment at a single medical center between 2011 and 2020 with a follow-up of at least 12 months. The outcomes were radiographic adjacent segment pathology (RASP) and clinical adjacent segment pathology (CASP). The mean follow-up period was 42.10 ± 22.61 months in the ALIF group and 56.20 ± 29.91 months in the TLIF group. Following single-level lumbosacral fusion, ALIF is superior to TLIF in maintaining lumbar lordosis, whereas the risk of adjacent instability in the ALIF group is significantly higher. Regarding ASP, the incidence of overall RASP and CASP did not differ significantly between ALIF and TLIF groups.

Background The assumptions of conventional spatial models cannot estimate the responses across space and over time. Spatial panel data models were proposed to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD). Methods A longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We set up an algorithm to identify the patient’s residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors. Results In total, 73,995 patients with ESRD were included in this study. There was a higher proportion of high incidence rates in the western region than in the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. It was found that most of “social environmental factors,” except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate. Conclusions Future comprehensive studies on townships located in higher-risk clusters of ESRD will help in setting up healthcare policies for suitable action.

The mutant burden of FLT3-ITD modulates its prognostic impact on patients with acute myeloid leukemia (AML). However, for patients with low allelic ratio (AR) FLT3-ITD (FLT3-ITDlow, AR

Since patients with acute myeloid leukemia (AML) in the real world have a much different clinical picture than patients recruited in the clinical trials, obtaining real-world evidence of medication adoption is important for therapeutic efficiency and safety. This study used three population-based data in Taiwan, the National Health Insurance Research Database, Taiwan Cancer Registry, and National Death Registry, between 2001 and 2015, to investigate the effect of conventional chemotherapy (CCT) versus non-conventional chemotherapy (NCCT) on the overall survival (OS) of patients with AML (n = 7,763). Cox proportional hazard regression was used to estimate the hazard ratios (HR) of different treatments on the risk of mortality. To reduce the potential selection bias, we used the inverse probability of treatment weighting based on the propensity score to balance the baseline characteristics between patients receiving CCT and NCCT. The median survival time for CCT and NCCT arms was 10.2 months (95% confidence interval (95% CI): 9.7-10.9) and 4.1 months (95% CI: 3.8-4.5), respectively. Compared to the patients received NCCT, those receiving CCT had a lower risk of mortality (HR 0.63 (95% CI: 0.59-0.67, P

Background Reduction of lumbar spondylolisthesis during spinal fusion surgery is important for improving the fusion rate and restoring the sagittal alignment. Despite the variety of reduction methods, the fundamental mechanics of lumbar spondylolisthesis reduction remain unclear. This study aimed to investigate the biomechanical behavior while performing spondylolisthesis reduction with the anterior and posterior lever reduction method. Methods We developed an L4–L5 spondylolisthesis model using sawbones. Two spine surgeons performed the simulated reduction with a customized Cobb elevator. The following data were collected: the torque and angular motion of Cobb, displacement of vertebral bodies, change of lordotic angle between L4 and L5, total axial force and torque applied on the model, and force received by adjacent disc. Results Less torque value (116 N-cm vs. 155 N-cm) and greater angular motion (53o vs. 38o) of Cobb elevator were observed in anterior lever reduction. Moreover, the total axial force received by the entire model was greater in the posterior lever method than that in the anterior lever method (40.8 N vs. 16.38 N). Besides, the displacement of both vertebral bodies was greater in the anterior lever method. Conclusions The anterior lever reduction is a more effort-saving method than the posterior lever reduction method. The existing evidence supports the biomechanical advantage of the anterior reduction method, which might be one of the contributing factors to successfully treating high-grade lumbar spondylolisthesis with short-segment instrumentation.

Increasing evidence supports the role of the immune microenvironment and associated signalling in the pathogenesis of myelodysplastic syndromes (MDS). Nevertheless, the clinical relevancy of immune signals in patients with MDS remains elusive. To address this, we used single-sample gene-set enrichment analysis to score immune signatures of bone marrow (BM) samples from 176 patients with primary MDS. Enhanced signatures of 'immature dendritic cells' and 'natural killer cells with cluster of differentiation (CD)56bright' were correlated with better overall survival (OS), whilst higher 'CD103+ signature' was associated with reduced survival. An MDS-Immune-Risk (MIR) scoring system was constructed based on the weighted sums derived from Cox regression analysis. High MIR scores were correlated with higher revised International Prognostic Scoring System (IPSS-R) scores and mutations in ASXL transcriptional regulator 1 (ASXL1), Runt-related transcription factor 1 (RUNX1), and tumour protein p53 (TP53). High-score patients had significantly inferior leukaemia-free survival (LFS) and OS than low-score patients. The prognostic significance of MIR scores for survival remained valid across IPSS-R subgroups and was validated in two independent cohorts. Multivariable analysis revealed that a higher MIR score was an independent adverse risk factor for LFS and OS. We further proposed a model with the combination of MIR score and gene mutations to be complementary to IPSS-R for the prognostication of LFS and OS of patients with MDS.

Myeloproliferative neoplasms (MPNs) are characterized by upregulation of proinflammatory cytokines and immune dysregulation, which provide a reasonable basis for immunotherapy in patients. Megakaryocytes are crucial in the pathogenesis of primary myelofibrosis (PMF), the most clinically aggressive subtype of MPN. In this study, we aimed to explore PD‐L1 (programmed death‐ligand 1) expression in megakaryocytes and its clinical implications in PMF. We analyzed PD‐L1 expression on megakaryocytes in PMF patients by immunohistochemistry and correlated the results with clinicopathological features and molecular aberrations. We employed a two‐tier grading system considering both the proportion of cells positively stained and the intensity of staining. Among the 85 PMF patients, 41 (48%) showed positive PD‐L1 expression on megakaryocytes with the immune‐reactive score ranging from 1 to 12. PD‐L1 expression correlated closely with higher white blood cell count (p = 0.045), overt myelofibrosis (p = 0.010), JAK2V617F mutation (p = 0.011), and high‐molecular risk mutations (p = 0.045), leading to less favorable overall survival in these patients (hazard ratio 0.341, 95% CI 0.135–0.863, p = 0.023). Our study provides unique insights into the interaction between immunologic and molecular phenotypes in PMF patients. Future work to explore the translational potential of PD‐L1 in the clinical setting is needed.