Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = -2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = -2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.

Metastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates.

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

TiO2 is an attractive catalyst for the photocatalytic degradation of organic pollutants. However, owing to its large band gap, it can only be activated by ultraviolet (UV) light, which constitutes a small portion of solar energy. Therefore, there has been significant interest in extending its light absorption range from UV to visible light. In this study, fluorinated TiO2 hollow spheres (FTHSs) were prepared via a rapid and simple wet chemical process using ammonium hexafluorotitanate, and then FTHS/WO3 heterostructures with different weight ratios of the FTHS and WO3 nanoparticles were synthesized via a simple wet impregnation method. The formation of the hybrid structure was confirmed by various characterization techniques. The photocatalytic activity of the synthesized photocatalysts in the photodegradation of rhodamine B, a model pollutant, was evaluated under visible light irradiation. The FTHS/WO3 heterostructures exhibited significantly improved photocatalytic activity compared to the bare FTHS or WO3 nanoparticles. The photodegradation efficiency of the FTHS/WO3 heterostructure in the present study was up to 0.0581 min-1. Detailed mechanisms that lead to the enhanced photocatalytic activity of the heterostructures are discussed. In addition, comparative experiments reveal that the photodegradation efficiency of the FTHS/WO3 heterostructure under visible light irradiation is superior to that of the P25/WO3 heterostructure prepared from the commercially available TiO2 catalyst (P25) via the same impregnation method.

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

The incidence of patients with pancreatic cystic lesions, particularly intraductal papillary mucinous neoplasm (IPMN), is increasing. Current guidelines, which primarily consider radiological features and laboratory data, have had limited success in predicting malignant IPMN. The lack of a definitive diagnostic method has led to low-risk IPMN patients undergoing unnecessary surgeries. To address this issue, we discovered IPMN marker candidates by analyzing pancreatic cystic fluid by mass spectrometry. A total of 30 cyst fluid samples, comprising IPMN dysplasia and other cystic lesions, were evaluated. Mucus was removed by brief sonication, and the resulting supernatant was subjected to filter-aided sample preparation and high-pH peptide fractionation. Subsequently, the samples were analyzed by LC-MS/MS. Using several bioinformatics tools, such as gene ontology and ingenuity pathway analysis, we detailed IPMNs at the molecular level. Among the 5834 proteins identified in our dataset, 364 proteins were differentially expressed between IPMN dysplasia. The 19 final candidates consistently increased or decreased with greater IPMN malignancy. CD55 was validated in an independent cohort by ELISA, Western blot, and IHC, and the results were consistent with the MS data. In summary, we have determined the characteristics of pancreatic cyst fluid proteins and discovered potential biomarkers for IPMN dysplasia.

Hepatocellular carcinoma (HCC) is a malignant cancer with one of the highest mortality rates. Des-γ-carboxyprothrombin (DCP) is an HCC serologic surveillance marker that can complement the low sensitivity of alpha-fetoprotein (AFP). DCP exists in the blood as a mixture of proteoforms from an impaired carboxylation process at glutamic acid (Glu) residues within the N-terminal domain. The heterogeneity of DCP may affect the accuracy of measurements because DCP levels are commonly determined using an immunoassay that relies on antibody reactivity to an epitope in the DCP molecule. In this study, we aimed to improve the DCP measurement assay by applying a mass spectrometry (MS)-based approach for a more inclusive quantification of various DCP proteoforms. We developed a multiple-reaction monitoring-MS (MRM-MS) assay to quantify multiple noncarboxylated peptides included in the various des-carboxylation states of DCP. We performed the MRM-MS assay in 300 patients and constructed a robust diagnostic model that simultaneously monitored three noncarboxylated peptides. The MS-based quantitative assay for DCP had reliable surveillance power, which was evident from the area under the receiver operating characteristic curve (AUROC) values of 0.874 and 0.844 for the training and test sets, respectively. It was equivalent to conventional antibody-based quantification, which had AUROC values at the optimal cutoff (40 mAU/mL) of 0.743 and 0.704 for the training and test sets, respectively. The surveillance performance of the MS-based DCP assay was validated using an independent validation set consisting of 318 patients from an external cohort, resulting in an AUROC value of 0.793. Conclusion: Due to cost effectiveness and high reproducibility, the quantitative DCP assay using the MRM-MS method is superior to antibody-based quantification and has equivalent performance.