Efficacy of chimeric antigen receptor T cell therapy and autologous stem cell transplant in relapsed or refractory diffuse large B-cell lymphoma: A systematic review

BackgroundWe aimed to compare the efficacy of chimeric antigen receptor T (CAR-T) cell therapy with that of autologous stem cell transplantation (auto-HSCT) in relapsed/refractory diffuse large B cell lymphoma (R/R DLBCL).Research design and methodsWe searched eligible publications up to January 31st, 2022, in PubMed, Cochrane Library, Springer, and Scopus. A total of 16 publications with 3484 patients were independently evaluated and analyzed using STATA SE software.ResultsPatients who underwent CAR-T cell therapy showed a better overall response rate (ORR) and partial response (PR) than those treated with auto-HSCT (CAR-T vs. auto-HSCT, ORR: 80% vs. 73%, HR:0.90,95%CI:0.76-1.07,P = 0.001; PR: 20% vs. 14%, HR:0.65,95%CI:0.62-0.68,P = 0.034). No significant difference was observed in 6-month overall survival (OS) (CAR-T vs. auto-HSCT, six-month OS: 81% vs. 84%, HR:1.23,95%CI:0.63-2.38, P = 0.299), while auto-HSCT showed a favorable 1 and 2-year OS (CAR-T vs. auto-HSCT, one-year OS: 64% vs. 73%, HR:2.42,95%CI:2.27-2.79, P < 0.001; two-year OS: 54% vs. 68%, HR:1.81,95%CI:1.78-1.97, P < 0.001). Auto-HSCT also had advantages in progression-free survival (PFS) (CAR-T vs. auto-HSCT, six-month PFS: 53% vs. 76%, HR:2.81,95%CI:2.53-3.11,P < 0.001; one-year PFS: 46% vs. 61%, HR:1.84,95%CI:1.72-1.97,P < 0.001; two-year PFS: 42% vs. 54%, HR:1.62,95%CI:1.53-1.71, P < 0.001). Subgroup analysis by age, prior lines of therapy, and ECOG scores was performed to compare the efficacy of both treatment modalities.ConclusionAlthough CAR-T cell therapy showed a beneficial ORR, auto-HSCT exhibited a better long-term treatment superiority in R/R DLBCL patients. Survival outcomes were consistent across different subgroups

Identification of microtubule-associated biomarkers in diffuse large B-cell lymphoma and prognosis prediction

Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease with a complicated prognosis. Even though various prognostic evaluations have been applied currently, they usually only use the clinical factors that overlook the molecular underlying DLBCL progression. Therefore, more accurate prognostic assessment needs further exploration. In the present study, we constructed a novel prognostic model based on microtubule associated genes (MAGs).Methods: A total of 33 normal controls and 1360 DLBCL samples containing gene-expression from the Gene Expression Omnibus (GEO) database were included. Subsequently, the univariate Cox, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to select the best prognosis related genes into the MAGs model. To validate the model, Kaplan-Meier curve, and nomogram were analyzed.Results: A risk score model based on fourteen candidate MAGs (CCDC78, CD300LG, CTAG2, DYNLL2, MAPKAPK2, MREG, NME8, PGK2, RALBP1, SIGLEC1, SLC1A1, SLC39A12, TMEM63A, and WRAP73) was established. The K-M curve presented that the high-risk patients had a significantly inferior overall survival (OS) time compared to low-risk patients in training and validation datasets. Furthermore, knocking-out TMEM63A, a key gene belonging to the MAGs model, inhibited cell proliferation noticeably.Conclusion: The novel MAGs prognostic model has a well predictive capability, which may as a supplement for the current assessments. Furthermore, candidate TMEM63A gene has therapeutic target potentially in DLBCL

Increased Activity Imbalance in Fronto-Subcortical Circuits in Adolescents with Major Depression

BACKGROUND: A functional discrepancy exists in adolescents between frontal and subcortical regions due to differential regional maturational trajectories. It remains unknown how this functional discrepancy alters and whether the influence from the subcortical to the frontal system plays a primacy role in medication naïve adolescent with major depressive disorder (MDD). METHODOLOGY/PRINCIPAL FINDINGS: Eighteen MDD and 18 healthy adolescents were enrolled. Depression and anxiety severity was assessed by the Short Mood and Feeling Questionnaire (SMFQ) and Screen for Child Anxiety Related Emotional Disorders (SCARED) respectively. The functional discrepancy was measured by the amplitude of low-frequency fluctuations (ALFF) of resting-state functional MRI signal. Correlation analysis was carried out between ALFF values and SMFQ and SCARED scores. Resting brain activity levels measured by ALFF was higher in the frontal cortex than that in the subcortical system involving mainly (para) limbic-striatal regions in both HC and MDD adolescents. The difference of ALFF values between frontal and subcortical systems was increased in MDD adolescents as compared with the controls. CONCLUSIONS/SIGNIFICANCE: The present study identified an increased imbalance of resting-state brain activity between the frontal cognitive control system and the (para) limbic-striatal emotional processing system in MDD adolescents. The findings may provide insights into the neural correlates of adolescent MDD

Emergency Decision Making: A Literature Review and Future Directions

In recent decades, various types of emergencies have started to occur more frequently. Their impact and complexity have increased significantly, bringing serious challenges to the sustainable development of the economy, society and the environment. Emergency decision making (EDM) for emergencies is vital for successfully handling crisis events and achieving sustainable development goals. It has attracted widespread academic attention. The purpose of this study is to summarize the progress made so far in research and identify future directions through a literature review. First, a two-stage literature search was conducted to identify a sample of studies. Then, the literature was analyzed econometrically and coded for content. Finally, a theoretical framework based on stakeholder theory was developed to identify current insights and to uncover what needs to be further researched. The article suggests that future in-depth research should be conducted in four areas: analysis of social media information related to emergencies, improvement in computer-aided tools, the influence of decision makers’ characteristics on decision outcomes, and efficient linkage of multiple subjects in the organization and implementation phase of emergency projects. This study hopes to draw the attention of more scholars to conduct research related to EDM to promote theoretical progress and contribute knowledge on the sustainable development of the practice of EDM

How social media expression can reveal personality

Background: Personality psychology studies personality and its variation among&nbsp;individuals and is an essential branch of psychology. In recent years, machine&nbsp;learning research related to personality assessment has started to focus on&nbsp;the online environment and showed outstanding performance in personality&nbsp;assessment. However, the aspects of the personality of these prediction models&nbsp;measure remain unclear because few studies focus on the interpretability of&nbsp;personality prediction models. The objective of this study is to develop and&nbsp;validate a machine learning model with domain knowledge introduced to&nbsp;enhance accuracy and improve interpretability. Methods: Study participants were recruited via an online experiment platform.&nbsp;After excluding unqualified participants and downloading the Weibo posts of&nbsp;eligible participants, we used six psycholinguistic and mental health-related&nbsp;lexicons to extract textual features. Then the predictive personality model&nbsp;was developed using the multi-objective extra trees method based on 3,411&nbsp;pairs of social media expression and personality trait scores. Subsequently, the&nbsp;prediction model&rsquo;s validity and reliability were evaluated, and each lexicon&rsquo;s&nbsp;feature importance was calculated. Finally, the interpretability of the machine&nbsp;learning model was discussed. Results: The features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the&nbsp;prediction model for personality traits ranged between 0.44 and 0.48 (p &lt; 0.001).&nbsp;The correlation coefficients of five personality traits between the two &ldquo;splithalf&rdquo;datasets data ranged from 0.84 to 0.88 (p &lt; 0.001). Moreover, the model&nbsp;performed well in terms of contractual validity. Conclusion: By introducing domain knowledge to the development of a machine&nbsp;learning model, this study not only ensures the reliability and validity of the&nbsp;prediction model but also improves the interpretability of the machine learning&nbsp;method. The study helps explain aspects of personality measured by such&nbsp;prediction models and finds a link between personality and mental health. Our&nbsp;research also has positive implications regarding the combination of machine&nbsp;learning approaches and domain knowledge in the field of psychiatry and its&nbsp;applications to mental health.</p

Frequency-Specific Functional Connectivity Density as an Effective Biomarker for Adolescent Generalized Anxiety Disorder

Several neuropsychiatric diseases have been found to influence the frequency-specific spontaneous functional brain organization (SFBO) in resting state, demonstrating that the abnormal brain activities of different frequency bands are associated with various physiological and psychological dysfunctions. However, little is known about the frequency specificities of SFBO in adolescent generalized anxiety disorder (GAD). Here, a novel complete ensemble empirical mode decomposition with adaptive noise method was applied to decompose the time series of each voxel across all participants (31 adolescent patients with GAD and 28 matched healthy controls; HCs) into four frequency-specific bands with distinct intrinsic oscillation. The functional connectivity density (FCD) of different scales (short-range and long-range) was calculated to quantify the SFBO changes related to GAD within each above frequency-specific band and the conventional frequency band (0.01–0.08 Hz). Support vector machine classifier was further used to examine the discriminative ability of the frequency-specific FCD values. The results showed that adolescent GAD patients exhibited abnormal alterations of both short-range and long-range FCD (S-FCD and L-FCD) in widespread brain regions across three frequency-specific bands. Positive correlation between the State Anxiety Inventory (SAI) score and increased L-FCD in the fusiform gyrus in the conventional frequency band was found in adolescents with GAD. Both S-FCD and L-FCD in the insula in the lower frequency band (0.02–0.036 Hz) had the highest classification performance compared to all other brain regions with inter-group difference. Furthermore, a satisfactory classification performance was achieved by combining the discrepant S-FCD and L-FCD values in all frequency bands, with the area under the curve (AUC) value of 0.9414 and the corresponding sensitivity, specificity, and accuracy of 87.15, 92.92, and 89.83%, respectively. This study indicates that the alterations of SFBO in adolescent GAD are frequency dependence and the frequency-specific FCD can potentially serve as a valuable biomarker in discriminating GAD patients from HCs. These findings may provide new insights into the pathophysiological mechanisms of adolescent GAD