A computational framework of routine test data for the cost-effective chronic disease prediction.

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma.

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

EmAtlas: a comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis.

The emerging importance of embryonic development research rapidly increases the volume for a professional resource related to multi-omics data. However, the lack of global embryogenesis repository and systematic analysis tools limits the preceding in stem cell research, human congenital diseases and assisted reproduction. Here, we developed the EmAtlas, which collects the most comprehensive multi-omics data and provides multi-scale tools to explore spatiotemporal activation during mammalian embryogenesis. EmAtlas contains data on multiple types of gene expression, chromatin accessibility, DNA methylation, nucleosome occupancy, histone modifications, and transcription factors, which displays the complete spatiotemporal landscape in mouse and human across several time points, involving gametogenesis, preimplantation, even fetus and neonate, and each tissue involves various cell types. To characterize signatures involved in the tissue, cell, genome, gene and protein levels during mammalian embryogenesis, analysis tools on these five scales were developed. Additionally, we proposed EmRanger to deliver extensive development-related biological background annotations. Users can utilize these tools to analyze, browse, visualize, and download data owing to the user-friendly interface. EmAtlas is freely accessible at http://bioinfor.imu.edu.cn/ematlas.

Ruddlesden-Popper Perovskite Nanocrystals as Interface Modification Layer for Efficient Perovskite Solar Cells.

Perovskite nanocrystals are advantageous for interfacial passivation of perovskite solar cells (PSCs), but the insulating long alkyl chain surface ligands impede the charge transfer, while the conventional ligand exchange would possibly introduce surface defects to the nanocrystals. In this work, we reported novel in situ modification of CsPbBr3 nanocrystals using a short chain conjugated molecule 2-methoxyphenylethylammonium iodide (2-MeO-PEAI) for interfacial passivation of PSCs. Transmission electron microscopy studies with atomic resolution unveil the transformation from cubic CsPbBr3 to Ruddlesden-Popper phase (RPP) nanocrystals due to halogen exchange. Synergic passivation by the RPP nanocrystals and 2-MeO-PEA+ has led to suppressed interface defects and enhanced charge carrier transport. Consequently, PSCs with in situ modified RPP nanocrystals achieved a champion power conversion efficiency of 24.39%, along with an improvement in stability. This work brings insights into the microstructural evolution of perovskite nanocrystals, providing a novel and feasible approach for interfacial passivation of PSCs.

Designing a structure-function alphabet of helix based on reduced amino acid clusters.

Several simple secondary structures could form complex and diverse functional proteins, meaning that secondary structures may contain a lot of hidden information and are arranged according to certain principles, to carry enough information of functional specificity and diversity. However, these inner information and principles have not been understood systematically. In our study, we designed a structure-function alphabet of helix based on reduced amino acid clusters to describe the typical features of helices and delve into the information. Firstly, we selected 480 typical helices from membrane proteins, zymoproteins, transcription factors, and other proteins to define and calculate the interval range, and the helices are classified in terms of hydrophilicity, charge and length: (1) hydrophobic helix (≤43%), amphiphilic helix (43%∼71%), and hydrophilic helix (≥71%). (2) positive helix, negative helix, electrically neutral helix and uncharged helix. (3) short helix (≤8 aa), medium-length helix (9-28 aa), and long helix (≥29 aa). Then, we designed an alphabet containing 36 triplet codes according to the above classification, so that the main features of each helix can be represented by only three letters. This alphabet not only preliminarily defined the helix characteristics, but also greatly reduced the informational dimension of protein structure. Finally, we present an application example to demonstrate the value of the structure-function alphabet in protein functional determination and differentiation.

RaacFold: a webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets.

Protein structure exhibits greater complexity and diversity than DNA structure, and usually affects the interpretation of the function, interactions and biological annotations. Reduced amino acid alphabets (Raaa) exhibit a powerful ability to decrease protein complexity and identify functional conserved regions, which motivated us to create RaacFold. The RaacFold provides 687 reduced amino acid clusters (Raac) based on 58 reduction methods and offers three analysis tools: Protein Analysis, Align Analysis, and Multi Analysis. The Protein Analysis and Align Analysis provide reduced representations of sequence-structure according to physicochemical similarities and computational biology strategies. With the simplified representations, the protein structure can be viewed more concise and clearer to capture biological insight than the unreduced structure. Thus, the design of artificial protein will be more convenient, and redundant interference is avoided. In addition, Multi Analysis allows users to explore biophysical variation and conservation in the evolution of protein structure and function. This supplies important information for the identification and exploration of the nonhomologous functions of paralogs. Simultaneously, RaacFold provides powerful 2D and 3D rendering performance with advanced parameters for sequences, structures, and related annotations. RaacFold is freely available at http://bioinfor.imu.edu.cn/raacfold.

Long-term outcomes of foramen magnum decompression with duraplasty for Chiari malformation type I in adults: a series of 297 patients.

OBJECTIVE: Foramen magnum decompression with duraplasty (FMDD) is one of the most frequently utilized surgical treatments for Chiari malformation type I (CMI) in adults. However, its long-term outcomes remain controversial. The object of this study was to evaluate the long-term outcomes of FMDD in adults with CMI. METHODS: In total, 297 adults with CMI who had undergone FMDD at the authors' institution between 2011 and 2020 were included in this retrospective study. Long-term (> 1 year) outcomes were evaluated using the Chicago Chiari Outcome Scale (CCOS), visual analog scale (VAS), and Japanese Orthopaedic Association (JOA) scale. RESULTS: The median patient age was 44 years (range 18-65 years). The mean clinical follow-up period was 67 months (range 14-123 months). Compared with preoperative conditions, the postoperative syringomyelia regression rate was 91.3% (242/265), and the cerebellar tonsil ascended in 18.2% of patients (54/297), was stable in 64.3% (191/297), and continuously descended in 17.5% (52/297). Long-term clinical follow-up data were acquired from 267 patients. According to the CCOS score, the patient's condition improved in 62.5% of cases (167/267), was stable in 31.8% (85/267), and worsened in 5.6% (15/267). According to the VAS score, the patient's condition improved in 59.5% of cases (110/185), remained unchanged in 27.6% (51/185), and worsened in 13.0% (24/185) among the follow-up patients with relevant data. According to the JOA score, the patient's condition improved in 40.1% of cases (107/267), remained unchanged in 50.2% (134/267), and worsened in 9.7% (26/267). Notably, there was no association between clinical outcomes and syringomyelia regression (p = 0.227) or changes in the cerebellar tonsillar position (p = 0.323). CONCLUSIONS: FMDD is a simple, safe, and effective surgical procedure for adult CMI that yields significant and sustained improvement in clinical and radiological outcomes. However, clinical improvement does not always correlate with syringomyelia regression and cerebellar tonsillar shift.

Can we 'Nudge' people to better waste separation behaviours? Policy interventions mediated by habit, sense of separation efficiency and external environmental perceptions.

This article studies the impact of intervention measures with nudge characteristics on residents' waste separation behaviours under the background of new 'Beijing Municipal Regulation on Domestic Waste Management'. We analyse whether and how nudge interventions affect residents' waste separation behaviours through the Ordered Logit model and mediation analysis. Our research results show that three nudge tools, namely, the timing and fixed-point separation system, the cleaning staff guidance system and the uniform clothing and styling, can effectively promote residents from low classification levels to high classification levels. In the process of nudging interventions, factors such as residents' waste separation habit, external environmental perception and the sense of separation efficiency play a mediating role. Our study's findings suggest that effective nudge tools are helpful in cultivating residents' separation habits and hence we recommend that policy makers promote and popularise them in their attempt to improve domestic waste management behaviours.

Cerebrovascular Dysregulation in Patients with Glioma Assessed with Time-shifted BOLD fMRI.

Background Microscopic vascular events, such as neovascularization and neurovascular uncoupling, are common in cerebral glioma. Mapping the cerebrovascular network remodeling at the macroscopic level may provide an alternative approach to assess hemodynamic dysregulation in patients with glioma. Purpose To investigate cerebrovascular dynamics and their relevance to tumor aggressiveness by using time-shift analysis (TSA) of the systemic low-frequency oscillation (sLFO) of the resting-state blood oxygenation level-dependent signal and a decision tree model. Materials and Methods In this retrospective study, 96 patients with histologically confirmed cerebral glioma were consecutively included (March 2012 to February 2017). TSA was performed to quantify the temporal properties of sLFO signals. Alteration in the time-shift properties was assessed in the tumor region and the contralesional hemisphere relative to the brains of healthy controls by using the Mann-Whitney U test. A decision tree model based on time-shift features was developed to predict the World Health Organization (WHO) glioma grade. Results A total of 88 patients with glioma (WHO grade II, 45; grade III, 21; grade IV, 22; mean age, 42 years; age range, 20-73 years; 51 men) and 40 healthy individuals from the 1000 Functional Connectomes Project (mean age, 32 years; age range, 24-49 years; 19 men) were included. The sLFO of the brain tissues was characterized by increased time shift in the tumor region and enhanced correlation with the global reference signal in the contralesional hemisphere compared with healthy brains. The proportion of tumor voxels with negative correlation to the reference signal significantly increased with the glioma malignancy grade. The decision tree model achieved an accuracy of 91% (80 of 88 patients) in predicting the glioma malignancy grade at the individual level (P = .004) based on the time-shift features. Conclusion Gliomas induced grade-specific cerebrovascular dysregulation in the entire brain, with altered time-shift features of systemic low-frequency oscillation signals. © RSNA, 2022 Online supplemental material is available for this article.

Development and validation of a novel survival prediction model for newly diagnosed lower-grade gliomas.

OBJECTIVE: Diffuse gliomas are the most common primary gliomas with a poor prognosis. This study aimed to develop and validate prognostic models for predicting the survival probability in newly diagnosed lower-grade glioma (LGG) patients. METHODS: Detailed data were obtained for newly diagnosed LGG from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts. Survival was assessed using Cox proportional hazards regression with adjustment for known prognostic factors. The model was established using the TCGA cohort, and independently validated using the CGGA cohort, to predict the 3-, 5-, and 10-year survival probabilities of patients. RESULTS: Data from 293 patients with newly diagnosed LGG from the TCGA cohort were used to establish a prognostic model, and from 232 patients with primary LGG in the CGGA cohort to validate the model. Age, tumor grade, molecular subtype, tumor resection, and preoperative neurological deficits were included in the prediction model. The Cox regression model had a satisfactory corrected concordance index of 0.8508, 0.8510, and 0.8516 in the internal bootstrap validation at 3, 5, and 10 years, respectively. The calibration plots demonstrated high consistency of the predicted and observed outcomes. The CGGA cohort was used for external validation and showed satisfactory discrimination of 0.7776, 0.7682, and 0.7051 at 3, 5, and 10 years, respectively. The calibration plots demonstrated an acceptable calibration capability in the external validation. CONCLUSIONS: This study established and validated a prognostic model to predict the survival probability of patients with newly diagnosed LGG. The model performed well in discrimination and calibration with ease of use, speed, accessibility, interpretability, and generalizability. An easily used nomogram based on the Cox model was established for clinical application. Moreover, a free, easy-to-use software interface based on the nomogram is provided online.

Tumor location-based classification of surgery-related language impairments in patients with glioma.

INTRODUCTION: Many patients with glioma experience surgery-related language impairment. This study developed a classification system to predict postoperative language prognosis. METHODS: Sixty-eight patients were retrospectively reviewed. Based on their location, tumors were subtyped as follows: (I) inferior frontal lobe or precentral gyrus; (II) posterior central gyrus or supramarginal gyrus (above the lateral fissure level); (III) posterior region of the superior or middle temporal gyri or supramarginal gyrus (below the lateral fissure level); and (IV) insular lobe. The distance from the tumor to the superior longitudinal fasciculus/arcuate fasciculus was calculated. The recovery of language function was assessed using the Western Aphasia Battery before surgery, and a comprehensive language test was conducted on the day of surgery; 3, 7, and 14 days after surgery. Our follow-up information of was the comprehensive language test from telephone interviews in 3 months after surgery. RESULTS: Thirty-three patients experienced transient language impairment within 1 week of surgery. Fourteen patients had permanent language impairment. Type II tumors, shorter distance from the tumor to the posterior superior longitudinal fasciculus/arcuate fasciculus, and isocitrate dehydrogenase mutations were risk factors for surgery-related language impairment. Regarding the presence or absence of permanent surgery-related language impairments, the cut-off distance between the tumor and posterior superior longitudinal fasciculus/arcuate fasciculus was 2.75 mm. CONCLUSIONS: According to our classification, patients with type II tumors had the worst language prognosis and longest recovery time. Our classification, based on tumor location, can reliably predict postoperative language status and may be used to guide tumor resection.

Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.

PURPOSE: PTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature. METHODS: In this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis. RESULTS: Six features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature. CONCLUSION: These results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker.

Molecular profiles for insular low-grade gliomas with putamen involvement.

BACKGROUND: The newly proposed putamen classification system shows good prognostic value in patients with insular LGGs, yet no study towards the molecular profiles of putamen involved LGGs has been proposed. METHODS: Clinical information and imaging data of patients diagnosed with insular low-grade gliomas were collected retrospectively. Genetic information of the 34 tumors was assessed using RNA-sequencing. Gene set enrichment analysis was further performed to identify the genes showing differential expression between putamen-involved tumors and putamen non-involved tumors. The level of Ki-67 expression was also evaluated. RESULTS: There were 843 genes identified to be differentially expressed between putamen-involved and non-involved gliomas. Specifically, Gene set enrichment analysis discovered 13 Kyoto Encyclopedia of Genes and Genomes pathways and 37 Gene Ontology Biological Process term were upregulated in putamen-involved low-grade glioma cells. The enriched GO sets with the highest gene counts included cell cycle (42 genes), mitotic cell cycle (24 genes), and cell division (19 genes). Furthermore, high expression of Ki-67 was associated with putamen involvement in insular gliomas. CONCLUSIONS: There is clear genetic variation between putamen-involved and non-involved insular low-grade gliomas. The differential expression of genes related to the processes of cell proliferation, cell migration, or DNA repair may lead to putamen involvement. The findings suggest that among the two subtypes, putamen-involved insular low-grade gliomas have higher malignancy, and the clinical treatment towards the putamen-involved insular low-grade gliomas should be more active.

Effect of esketamine combined with pregabalin on acute postsurgical pain in patients who underwent resection of spinal neoplasms: a randomized controlled trial.

ABSTRACT: Moderate-to-severe acute postsurgical pain (APSP) can prolong the recovery and worsen the prognosis of patients who undergo spinal surgery. Esketamine and pregabalin may resolve APSP without causing hyperpathia or respiratory depression after surgery. However, there are other risks, such as dissociative symptoms. We designed a randomized controlled trial to investigate the effect of the combination of these 2 drugs on the incidence of APSP in patients who underwent resection of spinal neoplasms. Patients aged 18 to 65 years were randomized to receive esketamine (a bolus dose of 0.5 mg·kg-1 and an infusion dose of 0.12 mg·kg-1·h-1 for 48 hours after surgery) combined with oral pregabalin (75-150 mg/day, starting 2 hours before surgery and ending at 2 weeks after surgery) or an identical volume of normal saline and placebo capsules. The primary outcome was the proportion of patients with moderate-to-severe APSP (visual analog scale score ≥ 40) during the first 48 hours after surgery. Secondary outcomes included the incidence of drug-related adverse events. A total of 90 patients were randomized. The incidence of moderate-to-severe APSP in the combined group (27.3%) was lower than that in the control group (60.5%) during the first 48 hours after surgery (odds ratio = 0.25, 95% CI = 0.10-0.61; P = 0.002). The occurrence of mild dissociative symptoms was higher in the combined group than in the control group (18.2% vs 0%). In conclusion, esketamine combined with pregabalin could effectively alleviate APSP after spinal surgery, but an analgesic strategy might increase the risk of mild dissociative symptoms.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

The Metal-binding Protein Atlas (MbPA): An Integrated Database for Curating Metalloproteins in All Aspects.

Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106,373 entries and 440,187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.

Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction.

Copper ion-binding proteins play an essential role in metabolic processes and are critical factors in many diseases, such as breast cancer, lung cancer, and Menkes disease. Many algorithms have been developed for predicting metal ion classification and binding sites, but none have been applied to copper ion-binding proteins. In this study, we developed a copper ion-bound protein classifier, RPCIBP, which integrating the reduced amino acid composition into position-specific scoring matrix (PSSM). The reduced amino acid composition filters out a large number of useless evolutionary features, improving the operational efficiency and predictive ability of the model (feature dimension from 2900 to 200, ACC from 83 % to 85.1 %). Compared with the basic model using only three sequence feature extraction methods (ACC in training set between 73.8 %-86.2 %, ACC in test set between 69.3 %-87.5 %), the model integrating the evolutionary features of the reduced amino acid composition showed higher accuracy and robustness (ACC in training set between 83.1 %-90.8 %, ACC in test set between 79.1 %-91.9 %). Best copper ion-binding protein classifiers filtered by feature selection progress were deployed in a user-friendly web server (http://bioinfor.imu.edu.cn/RPCIBP). RPCIBP can accurately predict copper ion-binding proteins, which is convenient for further structural and functional studies, and conducive to mechanism exploration and target drug development.

Research progress of reduced amino acid alphabets in protein analysis and prediction.

Proteins are the executors of cellular physiological activities, and accurate structural and function elucidation are crucial for the refined mapping of proteins. As a feature engineering method, the reduction of amino acid composition is not only an important method for protein structure and function analysis, but also opens a broad horizon for the complex field of machine learning. Representing sequences with fewer amino acid types greatly reduces the complexity and noise of traditional feature engineering in dimension, and provides more interpretable predictive models for machine learning to capture key features. In this paper, we systematically reviewed the strategy and method studies of the reduced amino acid (RAA) alphabets, and summarized its main research in protein sequence alignment, functional classification, and prediction of structural properties, respectively. In the end, we gave a comprehensive analysis of 672 RAA alphabets from 74 reduction methods.

The effect of loving-kindness meditation on employees' mindfulness, affect, altruism and knowledge hiding.

BACKGROUND: This study investigated the effects of the loving-kindness meditation (LKM) on employees' mindfulness, affect, altruism and knowledge hiding. METHODS: In total, 100 employees were recruited from a knowledge-based enterprise in China and randomly divided into the LKM training group (n = 50) and the control group (n = 50). The LKM training group underwent LKM training for 8 weeks, while the control group did not. Seven main variables (mindfulness, altruism positive affect, negative affect, playing dumb, rationalized hiding, and evasive hiding) were measured both before (pre-test) and after (post-test) the LKM training intervention. RESULTS: The LKM intervention significantly increased participants' altruism, and significantly reduced negative affect, playing dumb and evasive hiding, but did not significantly improve mindfulness, positive affect, and rationalized hiding. CONCLUSIONS: LKM significantly improved employees' altruism, and significantly reduce their negative affect, but did not significantly improve their mindfulness and positive affect. For knowledge hiding, LKM significantly reduced playing dumb and evasive hiding, but had no significant effect on rationalized hiding. These results further elucidate the psychological effects of LKM and suggest the possibility of reducing knowledge hiding in the workplace. TRIAL REGISTRATION: ChiCTR2200057460. Registered in Chinese Clinical Trial Registry (ChiCTR), 13 March 2022-Retrospectively registered.