Extracellular amyloid plaques, the pathognomonic hallmark of Alzheimer's disease (AD), are also observed in cognitively unimpaired subjects in the preclinical stages. Progressive accumulation of fibrillar amyloid-β (Aβ) as plaques and perivascular deposits occur two decades before clinical onset, making Aβ a long-lived peptide. To characterize the amyloid plaques biochemically, both the Aβ-load as well the post-translational modifications (PTMs) could serve as markers for distinguishing the pre-clinical stage compared to later prodromal and clinical stages of AD. Recently, we described the presence of extensive isomerization of the Aβ N-terminus in AD post-mortem brains that is significantly increased compared to the age-matched non-AD control brains with Aβ aggregates. In this report, we performed Lys-N enzymatic digestion followed by mass spectrometry-based quantitative analysis of the most common PTMs associated with plaque Aβ. We focused on pyroglutamation (pGlu3), citrullination (cit5), N-terminal truncation (Aβ_4-x), C-terminal isoforms (Aβ_x-42 and Aβ_x-40), and isomerization of aspartic acid residues (Asp-1 and Asp-7) in postmortem human brain tissue from pathologically negative (no Aβ plaques) controls (n = 23), controls with Aβ plaques (n = 35), Parkinson's disease (PD) with (n = 28) and without Aβ accumulation/plaques (n = 30) and symptomatic AD (n = 60). The AD cases contained statistically significant amounts of Asp-1 and Asp-7 isomerized Aβ (~ 90%) compared to controls (preclinical AD) and PD brains with fibrillar Aβ aggregates/deposits. We find that the ratio of isomerized N-terminus Aβ (Aβ_1-x) species in the brain detergent soluble pool differentiates older fibrillar Aβ deposits in symptomatic AD brain compared to Aβ deposits detected in preclinical AD and PD. Citrullinated pGlu3-Aβ was increased only in symptomatic AD, highlighting this Aβ PTM is a unique feature of parenchymal plaques in advanced AD. Our results have implications for early therapeutic targeting of these modified species as well as potential for better biofluid biomarker development for drug efficacy monitoring.

Existing theoretical models suggest that paranoid thoughts develop against the background of increased interpersonal sensitivity, thus heightening feelings of vulnerability, social evaluative concerns, and fears of social rejection. However, the complex interrelatedness among the risk factors contributing to the development of paranoid thoughts remains poorly understood.

A total of 1019 adults from the non-clinical population participated in a study that employed anetwork approach to explore the complex interactions between paranoid thoughts and severalrisk factors, including traumatic childhood experiences, sleep quality, rejection sensitivity, negative self-views, negative emotional states, and aberrant salience.

The results revealed that negative emotional states, mainly stress and anxiety, low self-esteem, negative body image, increased rejection sensitivity, and emotional neglect in childhood werethe most central nodes in the network. Furthermore, aside from the experience of emotionalneglect, these variables were also identified as the strongest bridge factors within the network.

The findings offer new insights into the risk factors associated with the development ofparanoid thoughts, suggesting potential triggers that could propagate other symptoms withinthe paranoia network. The results indicate that interventions targeting self-esteem, negativebody image, anxiety, and rejection sensitivity may prevent the progression of paranoidthoughts into more severe forms.

Delirium is a mental condition defined as fluctuating disturbances in attention, awareness, and cognition. It is often seen in older, hospitalized patients and is currently hard to predict, with long- and short-term outcomes being detrimental to patients.

We leveraged electronic health records (EHR) to identify 7492 UCSF patients and 19,417 UC health system patients with an inpatient delirium diagnosis and the same number of control patients without delirium. We used the Fisher's exact test with multiple corrections for the association studies and the Cox regression model for the longitudinal analyses.

Here we show significant associations between comorbidities or laboratory values and an inpatient delirium diagnosis, including metabolic abnormalities and psychiatric diagnoses. Some associations are sex-specific, including dementia subtypes and infections. We further explore the associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis to development of delirium, demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to increased risk of mortality.

These results demonstrate the powerful application of the EHR to shed insights into prior diagnoses and laboratory values that could help predict development of inpatient delirium and the importance of sex when making these assessments.

Drug-resistant tuberculosis (DR-TB) poses significant challenges not only to public health but also imposes substantial psychological and economic burdens on individuals and their families. As a severe infectious disease that jeopardizes both physical and mental well-being, DR-TB frequently spreads in underdeveloped regions due to inadequate diagnostic technologies.In this study, we validated the binding interaction between miR-122-5p and the proteins kininogen-1 (KNG1)/complement C3 using a dual-luciferase reporter assay. Furthermore, we employed liquid biopsy techniques to quantify miR-122-5p expression in plasma exosomes from DR-TB patients, alongside measuring plasma levels of KNG1, complement C3, and other coagulation and immune function parameters. This approach aims to identify efficient, non-invasive laboratory biomarkers for the early diagnosis of DR-TB. 50 patients with drug-susceptible tuberculosis (DS-TB) and 50 patients with DR-TB who were diagnosed in the nearby hospital between April 2024 and January 2025 were chosen. 51 healthy people who had physical exams over the same time frame were also selected as the control group. In the early morning, 5 ml of fasting venous blood was drawn from each of all subjects and centrifuged for standby. Informed consent was obtained from all Participants, who then signed the informed consent forms. We used Western blotting (WB), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA) to find the biomarkers in the exosomes that were taken from each of the three groups' plasma. The dual-luciferase experiment was used to verify the targeting relationship between miR-122-5p and protein KNG1 and complement C3. The RNA level of the miR-122-5p gene in plasma exosomes was detected by real-time fluorescence quantitative PCR (qRT-PCR). The KNG1 level in the plasma of the subjects was measured by ELISA, and the clinical indicators of the patients were also collected. To assess the diagnostic effectiveness of the genes found in the plasma exosomes, we used the receiver operating characteristic (ROC) curve. We found that there is a targeting relationship between miR-122-5p and protein KNG1 as well as complement C3. Meanwhile, the level of miR-122-5p in the DR-TB group was significantly higher than that in the DS-TB group and the HCs group, indicating a relatively high diagnostic efficacy. A useful biomarker to enhance the diagnosis of DR-TB is the level of miR-122-5p in plasma exosomes.

Cognitive impairments are generally observed in patients with schizophrenia. However, it is unclear whether neurocognitive dysfunction can predict the efficacy of antipsychotics for first-episode schizophrenia (FES). Machine learning methods provide a relatively unbiased approach when evaluating heterogeneous data, especially when building multifactor prediction models. This study conducted a secondary analysis based on the Chinese FES Trial (CNFEST), which was a 1-year study involving a randomized controlled trial for the first eight weeks followed by a 48-week open-label observation. The current study aimed to build a prediction model of eight-week antipsychotic response based on baseline clinical and demographic features. Six machine learning algorithms, including random forest, eXtreme gradient boosting (XGBoost), logistic regression, linear support vector machine (SVM), radial basis function SVM and poly SVM were applied and compared to draw the prediction model. The predictive effects were evaluated by balanced accuracy, sensitivity and specificity. The predictive factors were compared with F scores. A total of 450 qualified subjects contributed to the model. The prediction model constructed via XGBoost algorithm had the highest accuracy (68.8%) and prognostic certainty (44.3%) among all the algorithms. The baseline neurocognitive tests with strong predictive significance were the Grooved Pegboard Test, Trail Making Test Part A, Paced Auditory Serial Addition Test, Brief Visuospatial Learning Test, Hopkins Verbal Learning Test and Color Trails Test. This study emphasizes the importance of fine motor skills, verbal learning, visual learning, working memory and attention for the response of drug-naïve FES patients to antipsychotics. The model generated by XGBoost, which shows preferable accuracy, provides psychiatric practitioners with a possible way to predict efficacy for FES patients.

Congenital myasthenic syndromes (CMS) are a rare, heterogeneous group of disorders caused by pathogenic variants in genes encoding proteins essential for neuromuscular transmission. DOK7 variants are among the most common causes of CMS and one of the subtypes that may worsen with pyridostigmine. We report two patients who presented in adulthood with fatigable limb girdle weakness, initially diagnosed with seronegative myasthenia gravis, who slowly progressed over time despite escalating treatment and eventually needed intensive care admission. Revisiting the history led to the diagnosis of DOK7 CMS. Both patients improved after stopping immunosuppressants and pyridostigmine and starting salbutamol. These cases highlight the importance of considering CMS in patients with seronegative myasthenia gravis.

Previous studies identified atrophy-based Alzheimer's disease(AD) subtypes linked to distinct clinical symptoms, but their consistency across subtyping approaches remains unclear. This large-scale study evaluates subtype concordance using two data-driven approaches. In this work, we analyzed data from n=10,011 patients across 10 AD cohorts spanning Europe, the US, and Australia, extracting regional volumes using Freesurfer. To characterize atrophy heterogeneity in the AD continuum, we developed a two-step approach, Snowphlake(Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs), to identify subtypes and atrophy-event sequences within each subtype. Results were compared with SuStaIn(Subtype and Stage Inference), which jointly estimates subtypes and staging, using similar training and validation. Training included Aβ+ participants (n=1,195) and Aβ- cognitively unimpaired controls (n=1,692). We validated model-staging in a held-out clinical dataset (n=6,362) and an independent dataset(n=762), and assessed clinical significance in Aβ+ subsets(n=1,796 held-out; n=159 external). Concordance analysis evaluated consistency between methods. In the AD dementia(AD-D) training data, both Snowphlake and SuStaIn identified four subtypes. In the validation datasets, staging with both methods correlated with Mini-Mental State Examination(MMSE) scores. The Snowphlake subtypes assigned in Aβ+ validation datasets were associated with alterations in specific cognitive domains(Cohen's f:[0.15-0.33]). Similarly, the SuStaIn subtypes were also associated specific cognitive domains(Cohen's f:[0.17-0.34]). However, we observed low concordance between Snowphlake and SuStaIn, with 39.7% of AD-D patients grouped in concordant subtypes by both methods. In conclusion, Snowphlake and SuStaIn identified four atrophy-based subtypes that linked to distinct symptom profiles. While this highlights that the neuro-anatomically defined subtypes also meaningfully associate with different cognitive impairments at a group level, the low concordance between methods suggests that future research is needed to better understand the biological and methodological factors contributing to the observed variability.

Schizophrenia is increasingly recognized as a disorder with a prominent neuroimmune component. Researchers have observed elevated markers of inflammation (e.g., cytokines, CRP, and NLR) not only during first-episode psychosis but also in chronic stages, suggesting that immune dysregulation may play a key role in the illness's pathophysiology. Yet, current pharmacological treatment mainly targets dopaminergic dysregulation, which is effective in reducing positive symptoms but is ineffective in managing negative symptoms and cognitive decline associated with schizophrenia. Antipsychotics (APs) may exert anti-inflammatory effects, possibly through attenuating glial activation and modulation of the immune pathways, though these effects remain still underexplored. That is why, in this narrative review, we synthesize evidence from in vitro, animal, and human studies to examine whether APs influence inflammatory processes and assess their potential in mitigating the refractory symptoms of schizophrenia through the immune modulation. Despite promising findings, several key uncertainties persist: inflammatory markers exhibit inconsistent patterns across studies, methodological approaches differ considerably, and antipsychotic-induced metabolic alterations further complicate interpretation. To fully understand the anti-inflammatory potential of APs, future research should identify the most effective compounds, determine optimal treatment timing, and rigorously control for confounding factors. Crucially, a paradigm shift is needed: clinical trials must adopt biomarker-guided stratification, and drug development should focus on agents that modulate the innate immunity. These steps are essential for developing more effective treatments for the refractory symptoms of schizophrenia.

Using data from the VA Million Veteran Program (MVP), this study aimed to (1) examine rates of environmental exposures as a function of traumatic brain injury (TBI) history in post-9/11 veterans and (2) examine the independent and interactive effects of TBI and exposures on subjective cognition. Participants included 6707 MVP-enrolled veterans (78 % male; age: M = 44.66, SD = 10.91) who were deployed in support of the Iraq/Afghanistan-era conflicts, completed MVP surveys, and participated in the VA TBI Screening and Evaluation Program (TBI-SEP). Veterans were classified into three groups based on the results of the TBI-SEP: (1) negative TBI screen; (2) positive TBI screen but no TBI diagnosis; or (3) positive TBI screen and confirmed TBI diagnosis. Environmental exposures were extracted from MVP surveys and included solvents/fuels; pesticides; lead; other metals; combustion products; open-air burn pits; and chemical/biological warfare (CBW) agents. The Medical Outcomes Study Cognitive Functioning-Revised (MOS-Cog-R) scale was used to assess subjective cognition. Chi-square tests showed that exposure rates were highest among veterans screening positive for TBI. The most commonly reported exposure types were combustion products, burn pits, and solvents/fuels. Adjusted linear regressions showed that both TBI and environmental exposures independently contributed to worse subjective cognition, but there were no synergistic effects between TBI and exposures on cognition, except for CBW agents. Our findings emphasize the importance of considering environmental exposures as independent risk factors for subjective cognitive difficulties in post-9/11 veterans and support the use of toxic exposure screenings to connect veterans with appropriate resources and clinical care.

Suicide attempts (SA) are common in elderly patients with major depressive disorder (MDD), but few studies have focused on the prevalence and associated factors in elderly Chinese Han patients with MDD.

A total of 266 first episode and drug naïve older patients with MDD (age ≥ 50 years) were recruited. We measured depressive symptoms, anxiety symptoms and psychotic symptoms by the Hamilton Depression Rating Scale (HAMD), the Hamilton Anxiety Rating Scale (HAMA), the positive subscale of the Positive and Negative Syndrome Scale (PANSS) and the Clinical General Impression Inventory (CGIS). Fasting serum samples were collected to measure thyroid hormone levels and metabolic parameters.

The prevalence of SA was 24.4 %. Patients with SA had higher HAMD, HAMA, PANSS positive subscale and CGI-S scores, as well as higher levels of TSH, TgAb, TPOAb, FBG, TC, LDL-C, TG, and systolic and diastolic blood pressure. Logistic regression analysis showed that suicide attempts were correlated with HAMA score, CGI-S score and TC levels in elderly patients with MDD.

The findings show a high prevalence of suicide attempts in elderly patients with MDD. In elderly patients with MDD, severity of anxiety, CGI-S score and TC levels were associated factors for suicide attempts.