Real-world evidence from a retrospective study on suicide during depression: clinical characteristics, treatment patterns and disease burden.

BACKGROUND: Suicide stands as both a primary symptom and the direst outcome of major depressive disorder (MDD). The scarcity of effective treatment strategies makes managing MDD patients with suicide especially challenging. Hence, it is crucial to investigate disease characteristics and efficacious therapeutic strategies for these patients, drawing insights from disease databases and real-world data. METHODS: In this retrospective study, MDD patients hospitalized between January 2013 and December 2020 were investigated using Electronic Health Records (EHR) data from Beijing Anding Hospital. The study enrolled 4138 MDD patients with suicidal ideation or behavior (MDS) and 3848 without (MDNS). Demographic data, clinical attributes, treatment approaches, disease burden, and re-hospitalization within one year of discharge were extracted and compared. RESULTS: Patients in the MDS group were predominantly younger and female, exhibiting a higher prevalence of alcohol consumption, experiencing frequent life stress events, and having an earlier onset age. Re-hospitalizations within six months post-discharge in the MDS group were significantly higher than in the MDNS group (11.36% vs. 8.91%, p < 0.001). Moreover, a more considerable fraction of MDS patients underwent combined electroconvulsive therapy treatment (56.72% vs. 43.71%, p < 0.001). Approximately 38% of patients in both groups were prescribed two or more therapeutic regimes, and over 90% used antidepressants, either alone or combined. Selective serotonin reuptake inhibitors (SSRIs) were the predominant choice in both groups. Furthermore, antidepressants were often prescribed with antipsychotics or mood stabilizers. When medication alterations were necessary, the favoured options involved combination with antipsychotics or transitioning to alternative antidepressants. Yet, in the MDS group, following these initial modifications, the addition of mood stabilizers tended to be the more prioritized alternative. CONCLUSIONS: MDD patients with suicidal ideation or behaviour displayed distinctive demographic and clinical features. They exhibited intricate treatment patterns, a pronounced burden of illness, and an increased likelihood of relapse.

Vascular stent with immobilized anti-inflammatory chemerin 15 peptides mitigates neointimal hyperplasia and accelerates vascular healing.

Endovascular stenting is a safer alternative to open surgery for use in treating cerebral arterial stenosis and significantly reduces the recurrence of ischemic stroke, but the widely used bare-metal stents (BMSs) often result in in-stent restenosis (ISR). Although evidence suggests that drug-eluting stents are superior to BMSs in the short term, their long-term performances remain unknown. Herein, we propose a potential vascular stent modified by immobilizing clickable chemerin 15 (C15) peptides on the stent surface to suppress coagulation and restenosis. Various characterization techniques and an animal model were used to evaluate the surface properties of the modified stents and their effects on endothelial injury, platelet adhesion, and inflammation. The C15-immobilized stent could prevent restenosis by minimizing endothelial injury, promoting physiological healing, restraining the platelet-leukocyte-related inflammatory response, and inhibiting vascular smooth muscle cell proliferation and migration. Furthermore, in vivo studies demonstrated that the C15-immobilized stent mitigated inflammation, suppressed neointimal hyperplasia, and accelerated endothelial restoration. The use of surface-modified, anti-inflammatory, endothelium-friendly stents may be of benefit to patients with arterial stenosis. STATEMENT OF SIGNIFICANCE: Endovascular stenting is increasingly used for cerebral arterial stenosis treatment, aiming to prevent and treat ischemic stroke. But an important accompanying complication is in-stent restenosis (ISR). Persistent inflammation has been established as a hallmark of ISR and anti-inflammation strategies in stent modification proved effective. Chemerin 15, an inflammatory resolution mediator with 15-aa peptide, was active at picomolar through cell surface receptor, no need to permeate cell membrane and involved in resolution of inflammation by inhibiting inflammatory cells adhesion, modulating macrophage polarization into protective phenotype, and reducing inflammatory factors release. The implications of this study are that C15 immobilized stent favors inflammation resolution and rapid re-endothelialization, and exhibits an inhibitory role of restenosis. As such, it helps the decreased incidence of ISR.

CMBEE: A constraint-based multi-task learning framework for biomedical event extraction.

OBJECTIVE: Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction. MATERIAL AND METHODS: We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events. RESULTS: Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13). CONCLUSIONS: The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.

An insect sclerotization-inspired antifouling armor on biomedical devices combats thrombosis and embedding.

Thrombus formation and tissue embedding significantly impair the clinical efficacy and retrievability of temporary interventional medical devices. Herein, we report an insect sclerotization-inspired antifouling armor for tailoring temporary interventional devices with durable resistance to protein adsorption and the following protein-mediated complications. By mimicking the phenol-polyamine chemistry assisted by phenol oxidases during sclerotization, we develop a facile one-step method to crosslink bovine serum albumin (BSA) with oxidized hydrocaffeic acid (HCA), resulting in a stable and universal BSA@HCA armor. Furthermore, the surface of the BSA@HCA armor, enriched with carboxyl groups, supports the secondary grafting of polyethylene glycol (PEG), further enhancing both its antifouling performance and durability. The synergy of robustly immobilized BSA and covalently grafted PEG provide potent resistance to the adhesion of proteins, platelets, and vascular cells in vitro. In ex vivo blood circulation experiment, the armored surface reduces thrombus formation by 95 %. Moreover, the antifouling armor retained over 60 % of its fouling resistance after 28 days of immersion in PBS. Overall, our armor engineering strategy presents a promising solution for enhancing the antifouling properties and clinical performance of temporary interventional medical devices.

A novel role of TGFBI in macrophage polarization and macrophage-induced pancreatic cancer growth and therapeutic resistance.

Tumor-associated macrophages (TAMs), as a major and essential component of tumor microenvironment (TME), play a critical role in orchestrating pancreatic cancer (PaC) tumorigenesis from initiation to angiogenesis, growth, and systemic dissemination, as well as immunosuppression and resistance to chemotherapy and immunotherapy; however, the critical intrinsic factors responsible for TAMs reprograming and function remain to be identified. By performing single-cell RNA sequencing, transforming growth factor-beta-induced protein (TGFBI) was identified as TAM-producing factor in murine PaC tumors. TAMs express TGFBI in human PaC and TGFBI expression is positively related with human PaC growth. By inducing TGFBI loss-of-function in macrophage (MΦs) in vitro with siRNA and in vivo with Cre-Lox strategy in our developed TGFBI-floxed mice, we demonstrated disruption of TGFBI not only inhibited MΦ polarization to M2 phenotype and MΦ-mediated stimulation on PaC growth, but also significantly improved anti-tumor immunity, sensitizing PaC to chemotherapy in association with regulation of fibronectin 1, Cxcl10, and Ccl5. Our studies suggest that targeting TGFBI in MΦ can develop an effective therapeutic intervention for highly lethal PaC.

Hormonal and inflammatory signatures of different mood episodes in bipolar disorder: a large-scale clinical study.

BACKGROUND: Bipolar disorder (BD) is characterized by intensive mood fluctuations. While hormones imbalance plays important role in the mood swings, it is unknown whether peripheral hormones profiles could differentiate the manic and depressive mood episodes in BD. In this study, we investigated the changes of various hormones and inflammatory markers across distinct mood episodes of BD in a large clinical study to provide mood episode-specific peripheral biomarkers for BD. METHODS: A total of 8332 BD patients (n = 2679 depressive episode; n = 5653 manic episode) were included. All patients were in acute state of mood episodes and need hospitalization. A panel of blood tests were performed for levels of sex hormones (serum levels of testosterone, estradiol, and progesterone), stress hormones (adrenocorticotropic hormone and cortisol), and an inflammation marker (C-reactive protein, CRP). A receiver operating characteristic (ROC) curve was used to analyze the discriminatory potential of the biomarkers for mood episodes. RESULTS: In overall comparison between mood episodes, the BD patients expressed higher levels of testosterone, estradiol, progesterone, and CRP (P < 0.001) and lower adrenocorticotropic hormone (ACTH) level (P < 0.001) during manic episode. The episode-specific changes of testosterone, ACTH, and CRP levels remained between the two groups (P < 0.001) after correction for the confounding factors including age, sex, BMI, occupation, marital status, tobacco use, alcohol consumption, psychotic symptoms, and age at onset. Furthermore, we found a sex- and age-specific impact of combined biomarkers in mood episodes in male BD patients aged ≥ 45 years (AUC = 0.70, 95% CI, 0.634-0.747), not in females. CONCLUSIONS: While both hormone and inflammatory change is independently associated with mood episodes, we found that the combination of sex hormones, stress hormones and CRP could be more effective to differentiate the manic and depressive episode. The biological signatures of mood episodes in BD patients may be sex- and age-specific. Our findings not only provide mood episode-related biological markers, but also better support for targeted intervention in BD treatments.

Factors Affecting Radial Artery Occlusion After Right Transradial Artery Catheterization for Coronary Intervention and Procedures.

Objective: To determine the factors influencing proximal radial artery occlusion (PRAO) right radial artery after coronary intervention. Methods: This is a single-center prospective observational study. A total of 460 patients were selected to undergo coronary angiography (CAG) or percutaneous coronary intervention (PCI) via the proximal transradial approach (PTRA) or distal transradial approach (DTRA). The 6F sheath tube were received by all patients. Radial artery ultrasound was performed 1 day before procedure and 1-4 days after procedure. Patients were divided into the PRAO group (42 cases) and the non-PRAO group (418 cases). General clinical data and preoperative radial artery ultrasound indexes of the two groups were compared to analyze related factors leading to PRAO. Results: The total incidence of PRAO was 9.1%, including 3.8% for DTAR and 12.7% for PTRA. The PRAO rate of DTRA was significantly lower than that of PTRA (p < 0.05). Female, low body weight, low body mass index (BMI) and CAG patients were more likely to develop PRAO after procedure (p < 0.05). The internal diameter and cross-sectional area of the distal radial artery and proximal radial artery were smaller in the PRAO group than in the non-PRAO group, and the differences were statistically significant (p < 0.05). Multifactorial model analysis showed that the puncture approach, radial artery diameter and procedure type were predictive factors of PRAO, and the receiver operating characteristic curve showed a good predictive value. Conclusion: A larger radial artery diameter and DTRA may reduce the incidence of PRAO. Preoperative radial artery ultrasound can guide the clinical selection of appropriate arterial sheath and puncture approach.

Agomelatine in the treatment of anhedonia, somatic symptoms, and sexual dysfunction in major depressive disorder.

Objective: This study evaluated the treatment outcomes of agomelatine on anhedonic state, anxiety/somatic symptoms, and sexual function in Chinese patients with major depressive disorder (MDD). Method: In total, 93 adult patients with MDD were enrolled, and 68 of them were included in a prospective, open-label, multicenter clinical study. All patients received agomelatine monotherapy during a 9-week treatment phase. The effectiveness of the treatment was reflected by the improvement of anhedonia and somatic symptoms based on the 17-item Hamilton Depression Rating Scale (HAMD-17). In addition, the Arizona Sexual Dysfunction Scale (ASEX), Sheehan Disability Scale (SDS), and Short Form of Quality-of-Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF) were administered to all participants at baseline and at the 3-, 6-, and 9-week follow-ups. Results: After 9 weeks of treatment with agomelatine, the response and remission rates were 73.5% and 39.7%, respectively. Somatic symptoms significantly improved at week 9 (p < 0.001), and significant effects were also observed on the HAMD anhedonia items (p < 0.001). The patients exhibited lower levels of disease severity (the SDS score dropped from 15.52 ± 4.7 to 7.09 ± 5.62 at week 9; the ASEX score dropped from 21.89 ± 4.06 to 16.19 ± 4.79, p < 0.001) and higher levels of QOL (the Q-LES-Q-SF score dropped from 41.02 ± 5.99 to 50.49 ± 8.57, p < 0.001) during the follow-up. Furthermore, treatment with agomelatine improved depressive symptoms without causing serious adverse events. Conclusion: These analyses indicate that agomelatine is a treatment option for improving anhedonic status, anxiety/somatic symptoms, and sexual dysfunction in MDD patients.

Genome-wide DNA methylation analysis in families with multiple individuals diagnosed with schizophrenia and intellectual disability.

OBJECTIVES: Schizophrenia (SZ) and intellectual disability (ID) are both included in the continuum of neurodevelopmental disorders (NDDs). DNA methylation is known to be important in the occurrence of NDDs. The family study is conducive to eliminate the effects of relative epigenetic backgrounds, and to screen for differentially methylated positions (DMPs) and regions (DMRs) that are truly associated with NDDs. METHODS: Four monozygotic twin families were recruited, and both twin individuals suffered from NDDs (either SZ, ID, or SZ plus ID). Genome-wide methylation analysis was performed in all samples and each family. DMPs and DMRs between NDD patients and unaffected individuals were identified. Functional and pathway enrichment analyses were performed on the annotated genes. RESULTS: Two significant DMPs annotated to CYP2E1 were found in all samples. In Family One, 1476 DMPs mapped to 880 genes, and 162 DMRs overlapping with 153 unique genes were recognised. Our results suggested that the altered methylation levels of FYN, STAT3, RAC1, and NR4A2 were associated with the development of SZ and ID. Neurodevelopment and the immune system may participate in the occurrence of SZ and ID. CONCLUSIONS: Our findings suggested that DNA methylation participated in the development of NDDs by affecting neurodevelopment and the immune system.

A multicenter, randomized, double-blind, duloxetine-controlled, non-inferiority trial of desvenlafaxine succinate extended-release in patients with major depressive disorder.

BACKGROUND: Desvenlafaxine and duloxetine are selective serotonin and norepinephrine reuptake inhibitors. Their efficacy has not been directly compared using statistical hypotheses. This study evaluated the non-inferiority of desvenlafaxine extended-release (XL) to duloxetine in patients with major depressive disorder (MDD). METHODS: In this study, 420 adult patients with moderate-to-severe MDD were enrolled and randomly assigned (1:1) to receive 50 mg (once daily [QD]) of desvenlafaxine XL (n = 212) or 60 mg QD of duloxetine (n = 208). The primary endpoint was evaluated using a non-inferiority comparison based on the change from baseline to 8 weeks in the 17-item Hamilton Depression Rating Scale (HAMD17) total score. Secondary endpoints and safety were evaluated. RESULTS: Least-squares mean change in HAM-D17 total score from baseline to 8 weeks was -15.3 (95% confidence interval [CI]: -17.73, -12.89) in the desvenlafaxine XL group and - 15.9 (95% CI, -18.44, -13.39) in the duloxetine group. The least-squares mean difference was 0.6 (95% CI: -0.48, 1.69), and the upper boundary of 95% CI was less than the non-inferiority margin (2.2). No significant between-treatment differences were found in most secondary efficacy endpoints. The incidence of the most common treatment-emergent adverse events (TEAEs) was lower for desvenlafaxine XL than for duloxetine for nausea (27.2% versus 48.8%) and dizziness (18.0% versus 28.8%). LIMITATIONS: A short-term non-inferiority study without a placebo arm. CONCLUSIONS: This study demonstrated that desvenlafaxine XL 50 mg QD was non-inferior to duloxetine 60 mg QD in efficacy in patients with MDD. Desvenlafaxine had a lower incidence of TEAEs than duloxetine did.

A prospective cohort study of depression (PROUD) in China: rationale and design.

Background: Major depressive disorder (MDD) imposes a heavy global disease burden. However, current etiology, diagnosis and treatment remain unsatisfactory and no previous study has resolved this problem. Building on the strengths and limitations of previous cohort studies of MDD, the prospective cohort study of depression (PROUD) is a 3-year large-scale cohort study designed to collect multidimensional data with a flexible follow-up schedule and strategy. The goal is to establish a nationally representative, high-quality, standardized depression cohort to support precise diagnosis and treatment of MDD and address the gap in current research. Methods: PROUD is a patient-based, nationally representative multicenter prospective cohort study with baseline and 3-year follow-up assessments. It will be carried out from January 2022 to December 2026 in 52 qualified tertiary hospitals in China. A total of 14,000 patients diagnosed with MDD, according to the DSM-5 criteria, and aged ≥ 16 years, will be recruited to PROUD. Participants aged 18-65 years who have not received any treatment during a depressive episode will be included in the precision medicine cohort (PMC) of PROUD (n=4,000). Patients who meet the general eligibility criteria but not the PMC criteria will be included in the naturalistic observation cohort (NOC) of PROUD (n=10,000). A multiple follow-up strategy, including scheduled, remote, telephone, external visits and patient self-reports, will be implemented to collect comprehensive sociodemographic, clinical information, biospecimens, neuroimaging, cognitive function and electrophysiology data and digital phenotypes according to strict standard operating procedures implemented across centers. Trial registration: ChiCTR2200059053, registered on 23 April 2022, http://www.chictr.org.cn/showproj.aspx?proj=165790. Conclusions: PROUD is a prospective cohort study of MDD patients in China. It will provide a comprehensive database facilitating further analyses and aiding the development of homeostatic and precision medicine in China.

Synthesis of Molecularly Imprinted Polymers Based on a New Monomer "2-(4-Vinylphenyl) Quinoline-4-Carboxylic Acid" for the Selective Solid-Phase Extraction of Lamotrigine.

A new molecularly imprinted polymers (MIPs) have been prepared for the high selective extraction of lamotrigine (LTG), a widely used antiepileptic drug, in human serum. The MIPs were polymerized by bulk polymerization using our synthesized compound, 2-(4-vinylphenyl) quinolin-4-carboxylic acid, as functional monomer, which achieved better adsorption specificity than universal MIPs. Then, the molecularly imprinted solid phase extraction (MISPE) based on this material was coupled with high-performance liquid chromatography (HPLC) for the detection of LTG in human serum. The results of method validation showed that the developed method presented a good precision and accuracy, and the linearity was in the range of 1.50-40.00 mg/mL with the limit of quantitation (LOQ) at 0.20 mg/mL. The recovery ranged from 80.8% to 83.8% with RSD ranges from 5.5% to 11.1%. The validated method was successfully used to determine the concentration of LTG in human simulate serum samples.

A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data.

Background: Identifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods. Methods: The GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model. Results: A total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort. Conclusion: We analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.

Machine Learning Algorithms for Rupture Risk Assessment of Intracranial Aneurysms: A Diagnostic Meta-Analysis.

OBJECTIVE: Several machine learning algorithms have been increasingly applied to predict the rupture risk of intracranial aneurysms. We performed the present diagnostic meta-analysis to comprehensively evaluate the diagnostic value of machine learning algorithms for assessing the rupture risk of intracranial aneurysms. METHODS: We systematically searched 3 electronic databases, including Medline (via PubMed), the Cochrane Register of Controlled Trials (via Ovid), and Embase (via Elsevier), to retrieve eligible studies from the databases' inception through March 2021. The latest update was performed in June 2021. StataMP, version 14, was used to estimate all pooled diagnostic values. RESULTS: A total of 4 studies involving 6 reports were considered to meet the inclusion criteria. Our diagnostic meta-analysis generated the following pooled diagnostic values: sensitivity, 0.84 (95% confidence interval [CI], 0.75-0.90); specificity, 0.78 (95% CI, 0.68-0.85); positive likelihood ratio, 3.8 (95% CI, 2.4-5.9); negative likelihood ratio, 0.21 (95% CI, 0.12-0.35), diagnostic odd ratio, 18 (95% CI, 7-46), and area under the summary receiver operating characteristic curve, 0.88 (95% CI, 0.85-0.90). CONCLUSIONS: Our findings have demonstrated that the diagnostic performance of machine learning algorithms for the rupture risk assessment of AIs is excellent. Considering that the negative effects resulted from the limited number of eligible studies, we suggest developing more well-designed studies with larger sample sizes to validate our findings.

The significant role of post-pairing male behavior on the evolution of male preferences and female traits.

Existing sexual selection theory postulates that a sufficiently large variation in female fecundity or other direct benefits are fundamental for generating male mate choice. In this study, we suggest that, in addition to pre-pairing preferences, choosy males can also have different post-pairing behaviors, a factor which has been comparatively overlooked by previous studies. We found that both male preferences and female traits could evolve much more easily than previously expected when the choosy males that paired with unpreferred females would allocate more efforts to seeking additional post-pairing mating opportunities. Furthermore, a costly female trait could evolve when there was a trade-off between seeking additional mating and paternal care investment within social pair for choosy males. Finally, a costly male preference and a costly female trait might still evolve and reach a stable polymorphic state in the population, which might give rise to a high variability in male choice and female traits in nature. We suggest that male mate choice may be even more common than expected, which needs to be verified empirically.

Switching costs in stochastic environments drive the emergence of matching behaviour in animal decision-making through the promotion of reward learning strategies.

A principle of choice in animal decision-making named probability matching (PM) has long been detected in animals, and can arise from different decision-making strategies. Little is known about how environmental stochasticity may influence the switching time of these different decision-making strategies. Here we address this problem using a combination of behavioral and theoretical approaches, and show, that although a simple Win-Stay-Loss-Shift (WSLS) strategy can generate PM in binary-choice tasks theoretically, budgerigars (Melopsittacus undulates) actually apply a range of sub-tactics more often when they are expected to make more accurate decisions. Surprisingly, budgerigars did not get more rewards than would be predicted when adopting a WSLS strategy, and their decisions also exhibited PM. Instead, budgerigars followed a learning strategy based on reward history, which potentially benefits individuals indirectly from paying lower switching costs. Furthermore, our data suggest that more stochastic environments may promote reward learning through significantly less switching. We suggest that switching costs driven by the stochasticity of an environmental niche can potentially represent an important selection pressure associated with decision-making that may play a key role in driving the evolution of complex cognition in animals.

AromTool: predicting aromatic stacking energy using an atomic neural network model.

Aromatic stacking exists widely and plays important roles in protein-ligand interactions. Computational tools to automatically analyze the geometry and accurately calculate the energy of stacking interactions are desired for structure-based drug design. Herein, we employed a Behler-Parrinello neural network (BPNN) to build predictive models for aromatic stacking interactions and further integrated it into an open-source Python package named AromTool for benzene-containing aromatic stacking analysis. Based on extensive testing, AromTool presents desirable precision in comparison to DFT calculations and excellent efficiency for high-throughput aromatic stacking analysis of protein-ligand complexes.