Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior.

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response. Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region-specific, particularly involving the corticolimbic system, including the ventral tegmental area, nucleus accumbens, prefrontal cortex, amygdala, and hippocampus. Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology. In this review, we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression. We focused on associated molecular pathways and neural circuits, with special attention to the brain-derived neurotrophic factor-tropomyosin receptor kinase B signaling pathway and the ventral tegmental area-nucleus accumbens dopamine circuit. We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature, severity, and duration of stress, especially in the above-mentioned brain regions of the corticolimbic system. Therefore, BDNF might be a biological indicator regulating stress-related processes in various brain regions.

Real-world efficacy, roll-out and uptake of intramuscular tixagevimab/cilgavimab as COVID-19 pre-exposure prophylaxis in people with multiple sclerosis and neuroimmunological conditions during the COVID-19 pandemic.

Background: In Australia, tixagevimab/cilgavimab 150 mg/150 mg was a government-funded pre-exposure prophylaxis for COVID-19 people with multiple sclerosis (pwMS) and other neuroimmunological conditions (pwNIc) treated with anti-CD20 antibodies or sphingosine-1-phosphate receptor modulators were eligible. Objective: To analyse the roll-out, uptake and real-world efficacy of tixagevimab/cilgavimab in the prevention and severity of COVID-19. To assess compliance with uptake depending on the location of delivery. Methods: We undertook a single-centre study. 440 pwMS and pwNIc were eligible. Logistic regression was used to assess predictors of COVID-19 during follow-up and to assess predictors of uptake among those who consented. Results: Of the eligible pwMS and pwNIc in our service, 52.7% (233/440) requested a consultation and were included in this study. Consultation resulted in 71.7% of people (167/233) receiving the treatment. Of these, 94.0% (157/167) had received three or more COVID-19 vaccines. Among those who received a single dose of tixagevimab/cilgavimab, 19.16% (32/167) tested positive for COVID-19 during the observational window. The majority of these were on ocrelizumab (68.8% (22/32)). None of those with COVID-19 required hospitalisation or supplemental oxygen. There was no difference in odds of COVID-19 during the observation period between those who received and did not receive tixagevimab/cilgavimab (adjusted OR, aOR 2.16 (95% CI 0.82 to 6.85), p=0.43). Uptake of tixagevimab/cilgavimab was highest when offered at the hospital infusion centre (aOR 3.09 (95% CI 1.08 to 9.94) relative to referral to the local pharmacy, p=0.04). Conclusion: Tixagevimab/cilgavimab administration did not protect against subsequent COVID-19 in our cohort. Compliance with uptake was influenced by administration location.

NanoMUD: Profiling of pseudouridine and N1-methylpseudouridine using Oxford Nanopore direct RNA sequencing.

Nanopore direct RNA sequencing provided a promising solution for unraveling the landscapes of modifications on single RNA molecules. Here, we proposed NanoMUD, a computational framework for predicting the RNA pseudouridine modification (Ψ) and its methylated analog N1-methylpseudouridine (m1Ψ), which have critical application in mRNA vaccination, at single-base and single-molecule resolution from direct RNA sequencing data. Electric signal features were fed into a bidirectional LSTM neural network to achieve improved accuracy and predictive capabilities. Motif-specific models (NNUNN, N = A, C, U or G) were trained based on features extracted from designed dataset and achieved superior performance on molecule-level modification prediction (Ψ models: min AUC = 0.86, max AUC = 0.99; m1Ψ models: min AUC = 0.87, max AUC = 0.99). We then aggregated read-level predictions for site stoichiometry estimation. Given the observed sequence-dependent bias in model performance, we trained regression models based on the distribution of modification probabilities for sites with known stoichiometry. The distribution-based site stoichiometry estimation method allows unbiased comparison between different contexts. To demonstrate the feasibility of our work, three case studies on both in vitro and in vivo transcribed RNAs were presented. NanoMUD will make a powerful tool to facilitate the research on modified therapeutic IVT RNAs and provides useful insight to the landscape and stoichiometry of pseudouridine and N1-pseudouridine on in vivo transcribed RNA species.

Schottky Junction Enhanced Photosynthesis of Hydrogen Peroxide by Ultrathin Porous Carbon Nitride Supported Ni Nanoparticles.

Artificial photosynthesis for high-value hydrogen peroxide (H2O2) through a two-electron reduction reaction is a green and sustainable strategy. However, the development of highly active H2O2 photocatalysts is impeded by severe carrier recombination, ineffective active sites, and low surface reaction efficiency. We developed a dual optimization strategy to load dense Ni nanoparticles onto ultrathin porous graphitic carbon nitride (Ni-UPGCN). In the absence and presence of sacrificial agents, Ni-UPGCN achieved H2O2 production rates of 169 and 4116 µmol g-1 h-1 with AQY (apparent quantum efficiency) at 420 nm of 3.14% and 17.71%. Forming a Schottky junction, the surface-modified Ni nanoparticles broaden the light absorption boundary and facilitate charge separation, which act as active sites, promoting O2 adsorption and reducing the formation energy of *OOH (reaction intermediate). This results in a substantial improvement in both H2O2 generation activity and selectivity. The Schottky junction of dual modulation strategy provides novel insights into the advancement of highly effective photocatalytic agents for the photosynthesis of H2O2.

Prediction of vascular complications in free flap reconstruction with machine learning.

OBJECTIVE: This study aims to explore the risk factors of vascular complications following free flap reconstruction and to develop a clinical auxiliary assessment tool for predicting vascular complications in patients undergoing free flap reconstruction leveraging machine learning methods. METHODS: We reviewed the medical data of patients who underwent free flap reconstruction at the Affiliated Hospital of Zunyi Medical University retrospectively from January 1, 2019, to December 31, 2021. Statistical analysis was used to screen risk factors. A training data set was generated and augmented using the synthetic minority oversampling technique. Logistic regression, random forest and neural network, models were trained, using this dataset. The performance of these three predictive models was then evaluated and compared using a test set, with four metrics, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 570 patients who underwent free flap reconstruction were included in this study, 46 of whom developed postoperative vascular complications. Among the models tested, the neural network model exhibited superior performance on the test set, achieving an AUC of 0.828. Multivariate logistic regression analysis identified that preoperative hemoglobin levels, preoperative fibrinogen levels, operation duration, smoking history, the number of anastomoses, and peripheral vascular injury as statistically significant independent risk factors for vascular complications post-free flap reconstruction. The top five predictive factors in the neural network were fibrinogen content, operation duration, donor site, body mass index (BMI), and platelet count. CONCLUSION: Hemoglobin levels, fibrinogen levels, operation duration, smoking history, and anastomotic veins are independent risk factors for vascular complications following free flap reconstruction. These risk factors enhance the ability of machine learning models to predict the occurrence of vascular complications and identify high-risk patients. The neural network model outperformed the logistic regression and random forest models, suggesting its potential to aid clinicians in early identification of high-risk patients thereby mitigating patient suffering and improving prognosis.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy.

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Dynamic changes in lysosome-related pathways in APP/PS1 mice with aging.

Senile plaque, composed of amyloid ß protein (Aß) aggregates, is a critical pathological feature in Alzheimer's disease (AD), leading to cognitive dysfunction. However, how Aß aggregates exert age-dependent toxicity and temporal cognitive dysfunction in APP/PS1 mice remains incompletely understood. In this study, we investigated AD pathogenesis and dynamic alterations in lysosomal pathways within the hippocampus of age-gradient male mice using transcriptome sequencing, molecular biology assays, and histopathological analyses. We observed high levels of ß-amyloid precursor protein (APP) protein expression in the hippocampus at an early stage and age-dependent Aß deposition. Transcriptome sequencing revealed the enrichment of differential genes related to the lysosome pathway. Furthermore, the protein expression of ATP6V0d2 and CTSD associated with lysosomal functions exhibited dynamic changes with age, increasing in the early stage and decreasing later. Similar age-dependent patterns were observed for the endosome function, autophagy pathway, and SGK1/FOXO3a pathway. Nissl and Golgi staining in the hippocampal region showed age-dependent neuronal loss and synaptic damage, respectively. These findings clearly define the age-gradient changes in the autophagy-lysosome system, the endosome/lysosome system, and the SGK1/FOXO3a pathway in the hippocampus of APP/PS1 mice, providing new perspectives and clues for understanding the possible mechanisms of AD, especially the transition from compensatory to decompensated state.

Optimization of α-L-arabinofuranosidase CcABF on clarification and beneficial active substances in fermented ginkgo kernel juice by artificial neural network and genetic algorithm.

This study aimed at using α-L-arabinofuranosidase CcABF to improve the clarity and active substances in fermented ginkgo kernel juice by artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. A credible three-layer feedforward ANN model was established to predict the optimal parameters for CcABF clarification. The experiments proved the highest transmittance of 89.40% for fermented ginkgo kernel juice with this understanding, which exhibited a 25.56% increase over the unclarified group. With the clarification of CcABF, the antioxidant capacity in juice was enhanced with the increase of total phenolic and flavone contents, and the maximum DPPH and hydroxyl radical scavenging rates were increased by 89.71% and 26.65%, respectively. The contents of toxic ginkgolic acids declined markedly, while the active ingredients of ginkgetin and ginkgolide B showed a modest increase. Moreover, changes in free amino acids and volatile compounds improved the nutritive value and flavor of clarified fermented ginkgo kernel juice.

Adaptive functions of structural variants in human brain development.

Quantifying the structural variants (SVs) in nonhuman primates could provide a niche to clarify the genetic backgrounds underlying human-specific traits, but such resource is largely lacking. Here, we report an accurate SV map in a population of 562 rhesus macaques, verified by in-house benchmarks of eight macaque genomes with long-read sequencing and another one with genome assembly. This map indicates stronger selective constrains on inversions at regulatory regions, suggesting a strategy for prioritizing them with the most important functions. Accordingly, we identified 75 human-specific inversions and prioritized them. The top-ranked inversions have substantially shaped the human transcriptome, through their dual effects of reconfiguring the ancestral genomic architecture and introducing regional mutation hotspots at the inverted regions. As a proof of concept, we linked APCDD1, located on one of these inversions and down-regulated specifically in humans, to neuronal maturation and cognitive ability. We thus highlight inversions in shaping the human uniqueness in brain development.

m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a genomic feature-based classifier that refines the identification of m6A sites, distinguishing those genuinely present from those that can be detected in in-vitro transcribed (IVT) control experiments. We find that m6ACali effectively identifies non-specific binding peaks reported by exomePeak2 and MACS2 in novel MeRIP-Seq datasets without the need for paired IVT controls. The model interpretation revealed that off-target antibody binding sites commonly occur at short exons and short mRNAs, originating from high read coverage regions that share the motif sequence with true m6A sites. We also reveal that the ML strategy can efficiently adjust differentially methylated peaks and other antibody-dependent, base-resolution m6A detection techniques. As a result, m6ACali offers a promising method for the universal enhancement of m6A profiles generated by MeRIP-Seq experiments, elevating the benchmark for omics-level m6A data integration.

Differential inhibition of sildenafil and macitentan on saxagliptin metabolism.

The development of diabetes mellitus (DM) is generally accompanied by erectile dysfunction (ED) and pulmonary arterial hypertension (PAH), which increases the use of combination drug therapy and the risk of drug-drug interactions. Saxagliptin for the treatment of DM, sildenafil for the treatment of ED and PAH, and macitentan for the treatment of PAH are all substrates of CYP3A4, which indicates their potential involvement in drug-drug interactions. Therefore, we investigated potential pharmacokinetic interactions between saxagliptin and sildenafil/macitentan. We investigated this speculation both in vitro and in vivo, and explored the underlying mechanism using in vitro hepatic metabolic models and molecular docking assays. The results showed that sildenafil substantially inhibited the metabolism of saxagliptin by occupying the catalytic site of CYP3A4 in a competitive manner, leading to the alterations in the pharmacokinetic properties of saxagliptin in terms of increased maximum plasma concentration (Cmax), area under the plasma concentration-time curve from time 0 to 24 h (AUC(0-t)), area under the plasma concentration-time curve from time 0 extrapolated to infinite time (AUC(0-∞)), decreased clearance rate (CLz/F), and prolonged terminal half-life (t1/2). In contrast, a slight inhibition was observed in saxagliptin metabolism when concomitantly used with macitentan, as no pharmacokinetic parameters were altered, except for CLz/F. Thus, dosage adjustment of saxagliptin may be required in combination with sildenafil to achieve safe therapeutic plasma concentrations and reduce the risk of potential toxicity, but it is not necessary for co-administration with macitentan.

Astroglial Activation Is Exacerbated in a Down Syndrome Mouse Model.

Down syndrome (DS), also known as trisomy 21, is one of the most common chromosomal disorders associated with intellectual disability. Mouse models are valuable for mechanistic and therapeutic intervention studies. The purpose of this study was to investigate astroglial anomalies in Dp16, a widely used DS mouse model. Brain sections were prepared from one-month-old Dp16 mice and their littermates, immunostained with an anti-GFAP or anti-S100B antibody, and imaged to reconstruct astroglial morphology in three dimensions. No significant difference in the number of astrocytes was found in either the hippocampal CA1 region or cortex between Dp16 and WT mice. However, the average astroglial volume in Dp16 was significantly (P < 0.05) greater than that in WT, suggesting the astroglial activation. Reanalysis of the single-nucleus RNA sequencing data indicated that the genes differentially expressed between WT and Dp16 astrocytes were associated with synapse organization and neuronal projection. In contrast, in vitro cultured neonatal astrocytes did not exhibit significant morphological changes. The expression of Gfap in in vitro cultured Dp16 astrocytes was not increased as it was in in vivo hippocampal tissue. However, after treatment with lipopolysaccharides, the inflammatory response gene IFNß increased significantly more in Dp16 astrocytes than in WT astrocytes. Overall, our results showed that the increase in astrogliogenesis in DS was not apparent in the early life of Dp16 mice, while astrocyte activation, which may be partly caused by increased responses to inflammatory stimulation, was significant. The inflammatory response of astrocytes might be a potential therapeutic target for DS intellectual disability.

Dysregulation of TCONS_00006091 contributes to the elevated risk of oral squamous cell carcinoma by upregulating SNAI1, IRS and HMGA2.

In this study, we aimed to study the role of TCONS_00006091 in the pathogenesis of oral squamous cellular carcinoma (OSCC) transformed from oral lichen planus (OLP). This study recruited 108 OSCC patients which transformed from OLP as the OSCC group and 102 OLP patients with no sign of OSCC as the Control group. ROC curves were plotted to measure the diagnostic values of TCONS_00006091, miR-153, miR-370 and let-7g, and the changes in gene expressions were measured by RT-qPCR. Sequence analysis and luciferase assays were performed to analyze the molecular relationships among these genes. Cell proliferation and apoptosis were observed via MTT and FCM. TCONS_00006091 exhibited a better diagnosis value for OSCC transformed from OLP. OSCC group showed increased TCONS_00006091 expression and decreased expressions of miR-153, miR-370 and let-7g. The levels of SNAI1, IRS and HMGA2 was all significantly increased in OSCC patients. And TCONS_00006091 was found to sponge miR-153, miR-370 and let-7g, while these miRNAs were respectively found to targe SNAI1, IRS and HMGA2. The elevated TCONS_00006091 suppressed the expressions of miR-153, miR-370 and let-7g, leading to the increased expression of SNAI1, IRS and HMGA2. Also, promoted cell proliferation and suppressed apoptosis were observed upon the over-expression of TCONS_00006091. This study demonstrated that the expressions of miR-153, miR-370 and let-7g were down-regulated by the highly expressed TCONS_00006091 in OSCC patients, which accordingly up-regulated the expressions of SNAI1, IRS and HMGA2, resulting in the promoted cell proliferation and suppressed cell apoptosis.

Disrupted cognitive and affective empathy network interactions in autistic children viewing social animation.

Empathy can be divided into two core components, cognitive empathy (CE) and affective empathy (AE), mediated by distinct neural networks. Deficient empathy is a central feature of autism spectrum conditions (ASCs), but it is unclear if this deficit results from disruption solely within empathy networks or from disrupted functional integration between CE and AE networks. To address this issue, we measured functional connectivity (FC) patterns both within and between empathy networks in autistic children (4-8 years, n = 31) and matched typically developing (TD) children (n = 26) using near-infrared spectroscopy during the presentation of an animated story evoking CE and AE. Empathy and social communication ability were also assessed using the Empathy Quotient/Systemizing Quotient (EQ/SQ) and Social Responsiveness Scale, respectively. The results showed that the FC in the AE network of autistic children did not differ from the TD group across conditions; however, the ASC group showed weaker FC in the CE network under the CE condition and weaker FC between networks when processing AE information, the latter of which was negatively correlated with EQ scores in ASC. The empathy defect in ASC may involve abnormal integration of CE and AE network activities under AE conditions.

Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram.

Introduction: Recently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition. Methods: We proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP). Results: For the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network). Discussion: In summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.

Association Between Long-Term Exposure to Ambient Air Pollution and the Risk of Mild Cognitive Impairment in a Chinese Urban Area: A Case-Control Study.

Background: As a prodromal stage of dementia, significant emphasis has been placed on the identification of modifiable risks of mild cognitive impairment (MCI). Research has indicated a correlation between exposure to air pollution and cognitive function in older adults. However, few studies have examined such an association among the MCI population inChina. Objective: We aimed to explore the association between air pollution exposure and MCI risk from the Hubei Memory and Aging Cohort Study. Methods: We measured four pollutants from 2015 to 2018, 3 years before the cognitive assessment of the participants. Logistic regression models were employed to calculate odds ratios (ORs) to assess the relationship between air pollutants and MCI risk. Results: Among 4,205 older participants, the adjusted ORs of MCI risk for the highest quartile of PM2.5, PM10, O3, and SO2 were 1.90 (1.39, 2.62), 1.77 (1.28, 2.47), 0.56 (0.42, 0.75), and 1.18 (0.87, 1.61) respectively, compared with the lowest quartile. Stratified analyses indicated that such associations were found in both males and females, but were more significant in older participants. Conclusions: Our findings are consistent with the growing evidence suggesting that air pollution increases the risk of mild cognitive decline, which has considerable guiding significance for early intervention of dementia in the older population. Further studies in other populations and broader geographical areas are warranted to validate these findings.

Research status of comprehensive utilization of coal-based solid waste (CSW) and key technologies of filling mining in China: A review.

Coal-based solid waste (CSW) is the solid waste generated in the process of coal mining, washing and pyrolysis, which is an important industrial solid waste. The comprehensive utilization of CSW is a key link in the process of clean and efficient utilization of coal, and the use of CSW for coal mine filling mining is an important means of "harmless, resourceful and large-scale" utilization. In order to study the research status of comprehensive utilization of CSW and key technologies of filling mining in China, this paper combs and analyzes the current situation of comprehensive utilization of CSW from three parts, namely, physical and chemical properties of CSW, Industry-related policies, and ways and means of comprehensive utilization. It is found that coal mine filling mining is a green disposal method with relatively reliable technical means, low supervision cost and large-scale disposal of CSW in the comprehensive utilization of CSW in China. Furthermore, an analysis was conducted on the current research status of key technologies in the CSW filling and mining process, including the integration of "mining, selection and filling", adsorption and complexation passivation of heavy metals in CSW, the preparation of CSW collaborative filling materials, and monitoring and control of the whole filling process, etc. Based on the above analysis and research, it was pointed out that there were some problems, namely: (1) large output of CSW and low level of comprehensive utilization; (2) high investment and high cost of CSW filling and mining; and (3) imperfect CSW waste filling mining theory and technology. In response to these issues, prospects have been made from the aspects of policy incentive mechanisms, collaborative utilization of CSW with multi-industry links, and the theory and technology of CSW filling mining. This study provided reference and inspiration for the comprehensive utilization of CSW in the world, and provides guidance for the large-scale promotion and application of CSW filling mining methods.

Comparing ocrelizumab to interferon/glatiramer acetate in people with multiple sclerosis over age 60.

BACKGROUND: Ongoing controversy exists regarding optimal management of disease modifying therapy (DMT) in older people with multiple sclerosis (pwMS). There is concern that the lower relapse rate, combined with a higher risk of DMT-related infections and side effects, may alter the risk-benefit balance in older pwMS. Given the lack of pwMS above age 60 in randomised controlled trials, the comparative efficacy of high-efficacy DMTs such as ocrelizumab has not been shown in older pwMS. We aimed to evaluate the comparative effectiveness of ocrelizumab, a high-efficacy DMT, versus interferon/glatiramer acetate (IFN/GA) in pwMS over the age of 60. METHODS: Using data from MSBase registry, this multicentre cohort study included pwMS above 60 who switched to or started on ocrelizumab or IFN/GA. We analysed relapse and disability outcomes after balancing covariates using an inverse probability treatment weighting (IPTW) method. Propensity scores were obtained based on age, country, disease duration, sex, baseline Expanded Disability Status Scale, prior relapses (all-time, 12 months and 24 months) and prior DMT exposure (overall number and high-efficacy DMTs). After weighting, all covariates were balanced. Primary outcomes were time to first relapse and annualised relapse rate (ARR). Secondary outcomes were 6-month confirmed disability progression (CDP) and confirmed disability improvement (CDI). RESULTS: A total of 248 participants received ocrelizumab, while 427 received IFN/GA. The IPTW-weighted ARR for ocrelizumab was 0.01 and 0.08 for IFN/GA. The IPTW-weighted ARR ratio was 0.15 (95% CI 0.06 to 0.33, p<0.001) for ocrelizumab compared with IFN/GA. On IPTW-weighted Cox regression models, HR for time to first relapse was 0.13 (95% CI 0.05 to 0.26, p<0.001). The hazard of first relapse was significantly reduced in ocrelizumab users after 5 months compared with IFN/GA users. However, the two groups did not differ in CDP or CDI over 3.57 years. CONCLUSION: In older pwMS, ocrelizumab effectively reduced relapses compared with IFN/GA. Overall relapse activity was low. This study adds valuable real-world data for informed DMT decision making with older pwMS. Our study also confirms that there is a treatment benefit in older people with MS, given the existence of a clear differential treatment effect between ocrelizumab and IFN/GA in the over 60 age group.

Differentiating the association between age of alcohol use initiation and conditional suicidal behaviors among adolescents.

INTRODUCTION: Suicide and early alcohol use initiation are public health concerns. Previous studies have explored the associations between age of alcohol use initiation and suicidal behaviors, which progresses from ideation to action. Distinguishing between the various associations can help gain a deeper understanding of suicidal behaviors and aid in developing social suicide prevention strategies. METHODS: The study utilized the Youth Risk Behavior Survey to investigate this association. A total of 17 209 students were finally included in the study. Conditional suicidal behaviors included no suicidal behavior (NS), suicidal ideation without a plan or attempt (SINPA), suicide plan without an attempt (SPNA) and suicide attempt (SA). RESULTS: Among 17 209 students, the prevalence of suicidal ideation, suicide plan, and suicide attempt were 21.4%, 17.3%, and 11.1%, respectively. Moreover, 15.2% of the students used alcohol before age 13, whereas 31.7% of students used alcohol at age 13 or older. Compared to NS, students using alcohol showed significant associations with SA (OR = 2.34, p < .001; OR = 1.29, p < .01), SPNA (OR = 1.68, p < .001; OR = 1.19, p < .05) and SINPA (OR = 1.55, p < .001; OR = 1.40, p < .001). Comparing with SINPA and SNPA, students using alcohol before age 13 were associated with SA (OR = 1.61, p < .001; OR = 1.46, p < .001), whereas those using alcohol at or after the age 13 were not associated with SA (OR = 0.98, p > .05; OR = 1.09, p > .05). DISCUSSION: This study demonstrated that early alcohol use initiation was significantly associated with suicide attempts among students with suicidal ideations or plans.