Changes in lymphocyte subsets in patients with Guillain-Barré syndrome treated with immunoglobulin

BACKGROUND: Guillain-Barré syndrome (GBS) is an autoimmune condition characterized by peripheral neuropathy. The pathogenesis of GBS is not fully understood, and the mechanism of how intravenous immunoglobulin (IVIG) cures GBS is ambiguous. Herein, we investigated lymphocyte subsets in patients with two major subtypes of GBS (acute inflammatory demyelinating polyneuropathy, AIDP, and acute motor axonal neuropathy, AMAN) before and after treatment with IVIG, and explored the possible mechanism of IVIG action. METHODS: Sixty-four patients with GBS were selected for our study and divided into two groups: AIDP (n = 38) and AMAN (n = 26). Thirty healthy individuals were chosen as the control group. Relative counts of peripheral blood T and B lymphocyte subsets were detected by flow cytometry analysis. RESULTS: In the AIDP group, the percentage of CD4(+)CD45RO(+) T cells was significantly higher, while the percentage of CD4(+)CD45RA(+) T cells was notably lower, than in the control group. After treatment with IVIG, the ratio of CD4(+)/CD8(+) T cells and the percentage of CD4(+)CD45RA(+) T cells increased, while the percentages of CD8(+) T cells and CD4(+)CD45RO(+) T cells decreased significantly, along with the number of CD19(+) B cells. However, there were not such obvious changes in the AMAN group. The Hughes scores were significantly lower in both the AIDP and AMAN groups following treatment with IVIG, but the changes in Hughes scores showed no significant difference between the two groups. CONCLUSIONS: This study suggested that the changes in T and B-lymphocyte subsets, especially in CD4(+)T-lymphocyte subsets, might play an important role in the pathogenesis of AIDP, and in the mechanism of IVIG action against AIDP

Possible molecular states from interactions of charmed baryons

In this work, we perform a systematic study of possible molecular states composed of two charmed baryons including hidden-charm systems $\Lambda_c\bar{\Lambda}_c$, $\Sigma_c^{(*)}\bar{\Sigma}_c^{(*)}$, and $\Lambda_c\bar{\Sigma}_c^{(*)}$, and corresponding double-charm systems $\Lambda_c\Lambda_c$, $\Sigma_c^{(*)}\Sigma_c^{(*)}$, and $\Lambda_c\Sigma_c^{(*)}$. With the help of the heavy quark chiral effective Lagrangians, the interactions are described with $\pi$, $\rho$, $\eta$, $\omega$, $\phi$, and $\sigma$ exchanges. The potential kernels are constructed, and inserted into the quasipotential Bethe-Salpeter equation. The bound states from the interactions considered is studied by searching for the poles of the scattering amplitude. The results suggest that strong attractions exist in both hidden-charm and double-charm systems considered in the current work, and bound states can be produced in most of the systems. More experiment studies about these molecular states are suggested though the nucleon-nucleon collison at LHC and nucleon-antinucleon collison at $\rm \bar{P}ANDA$.Comment: 7 pages, 5 figure

Identification of CD133+ intercellsomes in intercellular communication to offset intracellular signal deficit

CD133 (prominin 1) is widely viewed as a cancer stem cell marker in association with drug resistance and cancer recurrence. Herein, we report that with impaired RTK-Shp2-Ras-Erk signaling, heterogenous hepatocytes form clusters that manage to divide during mouse liver regeneration. These hepatocytes are characterized by upregulated CD133 while negative for other progenitor cell markers. Pharmaceutical inhibition of proliferative signaling also induced CD133 expression in various cancer cell types from multiple animal species, suggesting an inherent and common mechanism of stress response. Super-resolution and electron microscopy localize CD133 on intracellular vesicles that apparently migrate between cells, which we name 'intercellsome.' Isolated CD133+ intercellsomes are enriched with mRNAs rather than miRNAs. Single-cell RNA sequencing reveals lower intracellular diversity (entropy) of mitogenic mRNAs in Shp2-deficient cells, which may be remedied by intercellular mRNA exchanges between CD133+ cells. CD133-deficient cells are more sensitive to proliferative signal inhibition in livers and intestinal organoids. These data suggest a mechanism of intercellular communication to compensate for intracellular signal deficit in various cell types

Identification of FKBP10 prognostic value in lung adenocarcinoma patients with surgical resection of brain metastases: A retrospective single-institution cohort study

Objective: To explore the expression levels and clinical value of FKBP10 in lung adenocarcinoma brain metastases. Design: A retrospective single-institution cohort study. Patients: The perioperative records of 71 patients with lung adenocarcinoma brain metastases who underwent surgical resection at the authors’ institution between November 2012 and June 2019 were retrospectively analyzed. Methods: The authors evaluated FKBP10 expression levels using immunohistochemistry in tissue arrays of these patients. Kaplan-Meier survival curves were constructed, and a Cox proportional hazards regression model was used to identify independent prognostic biomarkers. A public database was used to detect FKBP10 expression and its clinical value in primary lung adenocarcinoma. Results: The authors found that the FKBP10 protein was selectively expressed in lung adenocarcinoma brain metastases. Survival analysis showed that FKBP10 expression (p = 0.02, HR = 2.472, 95% CI [1.156, 5.289]), target therapy (p < 0.01, HR = 0.186, 95% CI [0.073, 0.477]), and radiotherapy (p = 0.006, HR = 0.330, 95% CI [0.149, 0.731]) were independent prognostic factors for survival in lung adenocarcinoma patients with brain metastases. The authors also detected FKBP10 expression in primary lung adenocarcinoma using a public database, found that FKBP10 is also selectively expressed in primary lung adenocarcinoma, and affects the overall survival and disease-free survival of patients. Limitations: The number of enrolled patients was relatively small and patients’ treatment options varied. Conclusions: A combination of surgical resection, adjuvant radiotherapy, and precise target therapy may benefit the survival of selected patients with lung adenocarcinoma brain metastases. FKBP10 is a novel biomarker for lung adenocarcinoma brain metastases, which is closely associated with survival time and may serve as a potential therapeutic target

Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps

Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced

Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects

The complexity of psychological principles underscore a significant societal challenge, given the vast social implications of psychological problems. Bridging the gap between understanding these principles and their actual clinical and real-world applications demands rigorous exploration and adept implementation. In recent times, the swift advancement of highly adaptive and reusable artificial intelligence (AI) models has emerged as a promising way to unlock unprecedented capabilities in the realm of psychology. This paper emphasizes the importance of performance validation for these large-scale AI models, emphasizing the need to offer a comprehensive assessment of their verification from diverse perspectives. Moreover, we review the cutting-edge advancements and practical implementations of these expansive models in psychology, highlighting pivotal work spanning areas such as social media analytics, clinical nursing insights, vigilant community monitoring, and the nuanced exploration of psychological theories. Based on our review, we project an acceleration in the progress of psychological fields, driven by these large-scale AI models. These future generalist AI models harbor the potential to substantially curtail labor costs and alleviate social stress. However, this forward momentum will not be without its set of challenges, especially when considering the paradigm changes and upgrades required for medical instrumentation and related applications

AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts

Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care. In current society, individuals frequently express negative emotions on social media on specific topics, often exhibiting cognitive distortions, including suicidal behaviors in extreme cases. Yet, there is a notable absence of methodologies for analyzing cognitive pathways that could aid psychotherapists in conducting effective interventions online. In this study, we gathered data from social media and established the task of extracting cognitive pathways, annotating the data based on a cognitive theoretical framework. We initially categorized the task of extracting cognitive pathways as a hierarchical text classification with four main categories and nineteen subcategories. Following this, we structured a text summarization task to help psychotherapists quickly grasp the essential information. Our experiments evaluate the performance of deep learning and large language models (LLMs) on these tasks. The results demonstrate that our deep learning method achieved a micro-F1 score of 62.34% in the hierarchical text classification task. Meanwhile, in the text summarization task, GPT-4 attained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86, surpassing the experimental deep learning model's performance. However, it may suffer from an issue of hallucination. We have made all models and codes publicly available to support further research in this field

Curcumin Protects Intestinal Mucosal Barrier Function of Rat Enteritis via Activation of MKP-1 and Attenuation of p38 and NF-κB Activation

BACKGROUND: Intestinal mucosa barrier (IMB) dysfunction results in many notorious diseases for which there are currently few effective treatments. We studied curcumin's protective effect on IMB and examined its mechanism by using methotrexate (MTX) induced rat enteritis model and lipopolysaccharide (LPS) treated cell death model. METHODOLOGY/PRINCIPAL FINDINGS: Curcumin was intragastrically administrated from the first day, models were made for 7 days. Cells were treated with curcumin for 30 min before exposure to LPS. Rat intestinal mucosa was collected for evaluation of pathological changes. We detected the activities of D-lactate and diamine oxidase (DAO) according to previous research and measured the levels of myeloperoxidase (MPO) and superoxide dismutase (SOD) by colorimetric method. Intercellular adhesion molecule-1 (ICAM-1), tumor necrosis factor α (TNF-α) and interleukin 1β (IL-1β) were determined by RT-PCR and IL-10 production was determined by ELISA. We found Curcumin decreased the levels of D-lactate, DAO, MPO, ICAM-1, IL-1β and TNF-α, but increased the levels of IL-10 and SOD in rat models. We further confirmed mitogen-activated protein kinase phosphatase-1 (MKP-1) was activated but phospho-p38 was inhibited by curcumin by western blot assay. Finally, NF-κB translocation was monitored by immunofluorescent staining. We showed that curcumin repressed I-κB and interfered with the translocation of NF-κB into nucleus. CONCLUSIONS/SIGNIFICANCE: The effect of curcumin is mediated by the MKP-1-dependent inactivation of p38 and inhibition of NF-κB-mediated transcription. Curcumin, with anti-inflammatory and anti-oxidant activities may be used as an effective reagent for protecting intestinal mucosa barrier and other related intestinal diseases

Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media

On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of these signs is critical for effective psychological intervention. In this paper, we introduce two novel datasets from Chinese social media: SOS-HL-1K for suicidal risk classification and SocialCD-3K for cognitive distortions detection. The SOS-HL-1K dataset contained 1,249 posts and SocialCD-3K dataset was a multi-label classification dataset that containing 3,407 posts. We propose a comprehensive evaluation using two supervised learning methods and eight large language models (LLMs) on the proposed datasets. From the prompt engineering perspective, we experimented with two types of prompt strategies, including four zero-shot and five few-shot strategies. We also evaluated the performance of the LLMs after fine-tuning on the proposed tasks. The experimental results show that there is still a huge gap between LLMs relying only on prompt engineering and supervised learning. In the suicide classification task, this gap is 6.95% points in F1-score, while in the cognitive distortion task, the gap is even more pronounced, reaching 31.53% points in F1-score. However, after fine-tuning, this difference is significantly reduced. In the suicide and cognitive distortion classification tasks, the gap decreases to 4.31% and 3.14%, respectively. This research highlights the potential of LLMs in psychological contexts, but supervised learning remains necessary for more challenging tasks. All datasets and code are made available.Comment: 10 page

Does hysteroscopy worsen prognosis in women with type II endometrial carcinoma?

Prior studies evaluating the impact of hysteroscopy on outcomes in endometrial cancer have predominantly evaluated type I tumors. We sought to evaluate whether hysteroscopy worsens prognosis in type II endometrial cancer