Early Pregnancy Loss (EPL) represents a formidable challenge in reproductive medicine. Its complex pathophysiology is primarily characterized by dysregulation of both the immune and coagulation systems. In recent years, abnormal activation of the complement system and neutrophil extracellular trap formation (NETosis) have received increasing scientific attention as key molecular events underlying EPL. This interest is fueled significantly by compelling evidence from non-pregnant models, such as antiphospholipid syndrome (APS) and severe infections (e.g., COVID-19), which demonstrate a pathogenic synergy between complement and NETosis in thrombo-inflammatory pathways. Dysregulation of the complement system can trigger strong inflammatory responses and promote a prothrombotic state. Similarly, excessive NETosis acts as a significant driver of tissue damage and thrombosis. This review aims to systematically delineate the complex crosstalk between complement activation and NETosis in EPL, with particular emphasis on their bidirectional regulatory relationship within autoimmune and thrombo-inflammatory pathways. By thoroughly analyzing the critical pathological role of this "complement-NETosis axis" in disrupting uteroplacental interface homeostasis and ultimately causing pregnancy failure, this review not only presents an integrative conceptual framework for comprehensively understanding the molecular mechanisms of EPL, but, more importantly, proposes a novel hypothesis, based on mechanistic insights derived from non-pregnant models, that this axis may play acentral role in EPL. This perspective provides a framework for future research, encompassing the validation of associated potential biomarkers and the exploration of targeted intervention strategies, thereby holding promise for advancing precise clinical intervention paradigms for specific EPL subtypes.

Bruton's tyrosine kinase inhibitors (BTKIs) have become the first-line treatment for B-cell malignancies such as chronic lymphocytic leukemia, significantly improving patient outcomes. However, hypertension is a common and critical cardiovascular adverse effect of BTKIs, with incidence rates ranging from 23% to 75%. For some patients, traditional antihypertensive therapy is ineffective, creating significant risks to cardiovascular safety and the sustainability of the treatment plan. Current reviews have primarily addressed general BTKI cardiotoxicity or isolated signaling pathways, failing to systematically analyze how the proprotein convertase subtilisin/kexin type 9 (PCSK9)/Notch3 pathway coordinates hypertension. Furthermore, current reviews lack comprehensive integration of individual susceptibility and mechanism-targeted strategies. Thus, this review systematically evaluates the epidemiological characteristics, molecular mechanisms, individual risk-modifying factors, and management strategies of BTKI-related hypertension by searching the PubMed, Embase, and Web of Science databases (up to 2025). Key findings suggest that BTKIs induce endothelial dysfunction and increase peripheral mechanisms through multiple mechanisms. These agents promote vascular inflammation, remodeling, and altered lipid metabolism by upregulating PCSK9, abnormally activating Notch3 signaling, increasing renin-angiotensin-aldosterone system activity, and driving nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-reactive oxygen species-mediated oxidative stress. Additionally, off-target inhibition of TEC/interleukin 2-inducible T-cell kinase contributes to these adverse vascular effects. Disease risk is further modified by Notch3/PCSK9 gene polymorphisms and clinical features such as existing cardiovascular disease and advanced age. Clinical management primarily relies on traditional antihypertensive drugs such as angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, as well as calcium channel blockers. In contrast, novel intervention strategies such as PCSK9 inhibitors and curcumin show potential advantages. To our knowledge, this review systematically elaborates the core role of the PCSK9/Notch3 signaling pathway in BTKI-related hypertension, integrating the "mechanism - susceptibility - intervention" three-dimensional network, providing a theoretical basis for precise risk stratification and individualized treatment, and promoting the dual optimization of cardiovascular protection and anti-tumor therapy.

Post-ischemic inflammation is strongly influenced by interactions between neurons and microglia. We previously observed that spermatogenesis-associated protein 2 (SPATA2) alleviates inflammatory injury following cerebral ischemia/reperfusion; however, the relationship between SPATA2, cylindromatosis (CYLD)-associated inflammatory signaling, and microglial inflammatory responses remains incompletely understood.

Adult Sprague-Dawley (SD) rats were subjected to transient middle cerebral artery occlusion followed by reperfusion. SPATA2 expression was knocked down in vivo via intracerebroventricular lentiviral delivery. Neurological function and infarct volume were evaluated after reperfusion. Selected pro-inflammatory and anti-inflammatory/reparative markers were assessed by immunofluorescence, real-time quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assay (ELISA). Double immunofluorescence staining of SPATA2 with neuronal nuclei (NeuN), ionized calcium-binding adapter molecule 1 (Iba1), or glial fibrillary acidic protein (GFAP) was performed to examine the cellular localization of SPATA2 in the peri-ischemic cortex. To further assess neuron-to-microglia regulation, primary neurons and microglia were cultured in a Transwell system and exposed to oxygen-glucose deprivation/reperfusion. SPATA2 knockdown and overexpression, together with CYLD knockdown in SPATA2-overexpressing cells, were used to investigate CYLD-associated signaling. Co-immunoprecipitation (Co-IP) was performed to assess CYLD-heme-oxidized IRP2 ubiquitin ligase 1L-like interacting protein (HOIP) association.

SPATA2 knockdown exacerbated neurological deficits and increased infarct volume following middle cerebral artery occlusion (MCAO). In peri-ischemic cortex tissue, SPATA2 knockdown increased the proportion of Iba1+/cluster of differentiation 86 (CD86)+ cells while reducing the proportion of Iba1+/CD206+ cells. Representative double-immunofluorescence images showed that SPATA2 immunoreactivity predominantly overlapped with NeuN-positive neurons, with relatively limited overlap with Iba1-positive microglia or GFAP-positive astrocytes. In the Transwell-based neuron-microglia system, depletion of SPATA2 in neurons shifted microglia toward a stronger inflammatory profile, as reflected by elevated secretion of interleukin-1 beta (IL-1β), IL-6, tumor necrosis factor alpha (TNF-α), and soluble C-X-C motif 3 chemokine ligand 1 (CX3CL1). By contrast, neuronal SPATA2 overexpression attenuated these inflammatory changes. At the mechanistic level, SPATA2 overexpression strengthened the association between CYLD and HOIP, together with reduced nuclear factor-κB (NF-κB) p65 expression and decreased soluble CX3CL1 release. When CYLD was silenced, the suppressive influence of SPATA2 overexpression on NF-κB p65 expression and soluble CX3CL1 release was partly weakened, indicating that CYLD contributes to this regulatory process.

SPATA2 attenuates inflammatory injury following focal cerebral ischemia/reperfusion and modulates selected microglial inflammatory markers. In vitro results further support the role of neuron-derived SPATA2 in regulating microglial inflammatory responses in a Transwell co-culture system. Based on these observations, SPATA2 may be involved in CYLD-related inflammatory signaling and the regulation of CX3CL1/CX3C chemokine receptor 1 (CX3CR1) pathway activity.

Patients with cardiovascular implantable electronic devices (CIEDs) who develop bacteremia require risk stratification for device infection. Although prediction scores exist for Staphylococcus aureus bacteremia, no comparable CIED-specific tool exists for non-S. aureus bacteremia. Our study aimed to estimate the probability of definite CIED infection in adults with non-S. aureus bacteremia without pocket infection.

We performed a prediction-model analysis of adults with CIEDs and non-S. aureus bacteremia, comprising gram-negative bacteremia (GNB) and gram-positive cocci other than S. aureus (GPC) bacteremia, at Mayo Clinic Rochester during 2012-2019. Patients with pocket infection were excluded. Definite CIED infection was classified using 2019 European Heart Rhythm Association criteria. Penalized logistic regression was used to construct two prediction models for definite infection: a full model with nine prespecified predictors, and a reduced model from which a point-based risk score could be obtained.

A total of 282 patients with CIEDs developed non-S. aureus bacteremia. Median age was 75 years; 69% were men. GPC bacteremia occurred in 156 patients (55%) and GNB in 126 (45%). Sixty patients (21%) had definite CIED infection. Both the full and reduced models demonstrated excellent predictive discrimination (apparent area under receiver operating characteristic curve 0.946 and 0.940). The reduced model retained four predictors: GPC bacteremia, persistent bacteremia, longer time to positivity, and unknown bacteremia source.

A four-variable risk score incorporating organism category, persistent bacteremia, time to positivity, and unknown bacteremia source may guide early device-focused evaluation, but external validation is required before clinical adoption.

Poststroke aphasia is a common disabling complication following stroke, severely affecting patients' communication abilities and quality of life. Currently, various non-pharmacological therapies (eg, speech and language therapy, transcranial magnetic stimulation, computer-assisted therapy, etc) have been applied in clinical practice and show potential. However, the relative effectiveness and superiority among these interventions remain unclear. This study aims to systematically compare the effectiveness of different non-pharmacological therapies in improving language function in poststroke aphasia through a network meta-analysis and to rank their efficacy, thereby providing the best evidence for clinical decision-making.

The protocol will be reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. The subsequent completed systematic review and network meta-analysis will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for network meta-analyses (an extension of the PRISMA 2020 statement) guidelines. We will systematically search PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science and China National Knowledge Infrastructure for randomised controlled trials on non-pharmacological therapies for poststroke aphasia published from inception to March 2026. Two researchers will independently conduct literature screening, data extraction and risk of bias (RoB) assessment using the Cochrane RoB tool. The primary outcome is overall language function; secondary outcomes include specific language domains and quality of life. We will employ a frequentist framework for network meta-analysis using random-effects models. The node-splitting model will assess inconsistency, and surface under the cumulative ranking curve will evaluate treatment ranking probabilities.

As this review will use only published aggregated data, ethical approval is not required. Findings will be disseminated through a peer-reviewed publication and presentations at relevant conferences.

CRD420261297981.

Deep Learning (DL) has transformed cardiac image segmentation, yet its application to congenital heart disease (CHD) remains underexplored, with no prior systematic review in this emerging domain. This review evaluates current DL approaches for CHD CT segmentation, identifies best performing model architectures, assesses quality of reporting and highlights challenges for clinical implementation.

Following PRISMA guidelines, six databases: PubMed, IEEE Xplore, MDPI, Science Direct, Web of Science, and Scopus were searched (2004-2025). Extracted characteristics include source of dataset, ground truth labelling approach, DL architecture and evaluation methods. Reporting quality was evaluated using the Checklist for Artificial Intelligence in Medical imaging (CLAIM).

14 studies met the inclusion criteria, reflecting the emergence of this research area. The UNet architecture and its variants dominated (8 studies). The highest reported Dice Similarity Coefficient (DSC) for cardiac structures segmentation was achieved utilising an advanced hybrid ResNet-based model (Aorta, DSC = 0.945). Well documented areas include the training approach (12 studies) and model description (all). Only two studies performed external validation, and two assessed inter- and intra-rater variability. No studies reported sample size determination and handing of missing data. Small vascular structures consistently underperformed compared to whole heart segmentation.

Deep learning models show strong potential for CHD CT segmentation but remain limited by small datasets, inconsistent reporting and lack of clinical evaluation. Advancing this field requires multidisciplinary collaboration, standardised reporting, and integration of clinical co- design. As the first comprehensive review in this area, we provide a strong baseline for evaluating future studies.

Patients with ST-segment elevation myocardial infarction (STEMI) continue to show substantial inflammatory heterogeneity after reperfusion therapy, and recovery trajectories may differ markedly across patients. Compared with traditional diagnostic models, characterizing the dynamic immune trajectory from acute inflammation toward recovery may better identify programs associated with impaired resolution. This study aimed to construct an immune recovery-failure framework that is transferable across cohorts and anchored by single-cell data.

We integrated six public GEO peripheral-blood bulk transcriptome cohorts and one public single-cell RNA-seq dataset. An acute-to-recovery immune axis was constructed from directionally concordant longitudinal and cross-sectional contrasts, and a recovery-failure phenotype was defined in the discovery longitudinal cohort from paired transcriptomic features. An 18-gene monocyte-associated module was then derived using bulk recovery-failure associations, monocyte specificity, monocyte-state differences, and pseudotime information. External cohorts were used to evaluate temporal transfer and baseline-to-follow-up associations rather than definitive clinical prediction, and the single-cell dataset was used for donor-aware cell-type anchoring and exploratory state-level interpretation.

Among 81 STEMI patients who met the longitudinal definition criteria, unsupervised stratification classified 35 as recovery-failure patients and 46 as recovery-sufficient patients. We constructed an 18-gene monocyte-associated recovery-failure module. The module distinguished the two transcriptomically defined groups in the discovery cohort and declined from the acute phase toward recovery in external longitudinal cohorts. Baseline module burden was associated with later composition-adjusted inflammatory burden, but this was interpreted as a baseline-to-follow-up association rather than clinical event prediction. At the single-cell level, donor-aggregated lesion-associated shifts were largest in monocytes, and the module decreased along the acute-high-to-recovery-biased monocyte pseudotime direction. In 1,000 matched random monocyte-gene modules, the observed module showed a higher aggregate recovery statistic than nearly all random signatures.

We propose a STEMI immune recovery-failure framework derived from public peripheral-blood transcriptomes, evaluated across independent longitudinal datasets, and linked to monocyte state variation in a public single-cell context. The framework is does not evidence of a causal mechanism or a validated hard-endpoint prediction model; rather, it provides a reproducible transcriptomic description of impaired inflammatory resolution that should be tested prospectively and experimentally.

Ataxia-telangiectasia (A-T) is a rare autosomal recessive genome-instability disorder due to pathogenic variants in ATM, a central kinase of the DNA double-strand break signaling, checkpoint activation and stress-response pathways. Although microRNAs (miRNAs) regulate DNA damage response (DDR), apoptosis, senescence and cell-cycle control, the stress-responsive behavior of selected ATM/DDR-associated miRNAs remains incompletely characterized in A-T fibroblast models.

Four predefined ATM/DDR-associated candidate miRNAs (hsa-miR-34a-5p, hsa-miR-26a-5p, hsa-miR-106b-5p and hsa-miR-20a-5p) were quantified in one ATM-proficient fibroblast control and three A-T fibroblast cell lines both under basal and bleomycin exposed conditions. The relative expression of selected miRNAs was measured by RT-qPCR. miR-34a-5p and miR-26a-5p levels were lower in each of the three bleomycin-treated A-T lines relative to the single bleomycin-treated WT control line. miR-34a-5p and miR-26a-5p levels were lower in bleomycin-treated three A-T lines relative to the single control WT line. Within-cell-line analysis showed significant miR-34a-5p induction after bleomycin in WT, AT648 and AT9607, whereas significant miR-26a-5p induction was observed only in AT648. AT648 showed the broadest within-cell-line response, with significant induction of all four tested miRNAs after bleomycin. Target-based enrichment and network analyses of the predefined candidate panel identified annotations related to cell-cycle regulation, G1/S transition, RB/E2F signaling, p53 signaling, apoptosis, senescence, PI3K-Akt signaling, and DNA damage-associated processes. Network analysis revealed a number of shared candidate targets such as CCND1, CCND2, E2F1, E2F3, RB1, WEE1, PTEN, SMAD4, MYC and VEGFA.

In this fibroblast panel, miR-34a-5p and miR-26a-5p showed the most consistent baseline alterations, whereas bleomycin responsiveness was cell-line specific and heterogeneous.

Survivors of breast cancer frequently experience anxiety, stress, and depression during the follow-up and survivorship phases, when access to psychosocial support is often limited. Although psychological interventions have demonstrated effectiveness, their implementation in routine clinical practice remains challenging. Digital mental health interventions offer a promising alternative. However, many existing solutions show limited personalization, engagement, and long-term adherence, partly due to insufficient involvement of end users in their design.

This study aims to develop and pilot-test a cocreated digital psychological intervention (SerenApp) aimed at improving psychological well-being among women during breast cancer follow-up and to evaluate its feasibility, usability, and preliminary effectiveness.

This mixed methods study adopts a participatory cocreation approach structured into 3 phases. Phase 1 focuses on identifying unmet psychosocial needs and co-designing the app's content and functionalities through workshops involving patients and health care professionals, applying design thinking principles and the Cocre-Ar-E (cocreate, assemble, and evaluate) framework. Phase 2 involves the technical development of the intervention using an agile scrum framework methodology, followed by usability and technical validation (alpha testing). Phase 3 consists of a pilot randomized controlled trial (n=90; 45 participants in each of the intervention and control groups) conducted in Andalusia and the Canary Islands, Spain. Outcomes include acceptability, usability, adherence, psychological well-being, and cost-effectiveness. Validated instruments include the Cancer Patients' Stress Coping Questionnaire (CAEPO), Patient Health Questionnaire-9 (PHQ-9), Hospital Anxiety and Depression Scale (HADS), EQ-5D-5L, System Usability Scale (SUS), Client Satisfaction Questionnaire-8 (CSQ-8), Client Service Receipt Inventory (CSRI), and Mobile App Rating Scale (MARS). Quantitative analyses will include ANOVA, multilevel regression models, and cost-effectiveness analyses (incremental cost-effectiveness ratio and incremental cost-utility ratio), whereas qualitative data will be analyzed using reflexive thematic analysis supported by NVivo software.

Funding was awarded by the Instituto de Salud Carlos III (PI24/01193) in 2024. The SerenApp project began in January 2025 with an exploratory phase and a comprehensive literature review. The cocreation phase was conducted between September 2025 and May 2026, involving 24 breast cancer survivors and 15 multidisciplinary health care professionals. Development and alpha testing are ongoing, with recruitment expected to begin in 2027. Data analysis has not yet started, and the main study findings are expected to be published in 2028.

This protocol describes the development and pilot evaluation of SerenApp. By integrating the perspectives of survivors, health care professionals, researchers, and technology developers, the study seeks to develop an intervention that is relevant, acceptable, and feasible within real-world health care settings. The findings generated through the cocreation process, usability evaluation, and pilot randomized controlled trial are expected to contribute to the evidence base on participatory approaches in digital mental health and inform the future refinement, implementation, and large-scale evaluation of SerenApp.

ClinicalTrials.gov NCT07169539; https://clinicaltrials.gov/study/NCT07169539.

DERR1-10.2196/87151.

People with intellectual disabilities have a high prevalence of mental health problems, which are often underdiagnosed and treated late due to difficulties in assessment. The limited availability of validated instruments, communication barriers, and reliance on external informants hinder the early detection of symptoms and the monitoring of their progression.

This study describes the protocol for the development and piloting of MENTALSED, a digital assessment system designed for the continuous monitoring of mental health and emotional well-being in people with intellectual disability through ecological momentary assessment using traditional questionnaires supplemented with psychophysiological measures.

The study uses a mixed methods design, combining quantitative and qualitative procedures. Following a literature review, a survey assessed the needs of direct care professionals. On the basis of these findings, self-report and proxy-report EMA questionnaires were developed. The professional version was evaluated by university experts and direct care professionals. The self-report version was adapted into an easy-to-read format with the support of a focus group of people with intellectual disabilities and subsequently evaluated by individuals with intellectual disabilities and professionals. The questionnaires were implemented on the Avicenna Research platform. Objective psychophysiological measures (heart rate variability via a chest strap and sleep quality and heart rate via a smartwatch) were included. All data are integrated into a custom application displaying 4 well-being dimensions. The subsequent pilot study will involve individuals with intellectual disability and support professionals over a 2-week period in natural settings.

The project was funded by the Ministry of Science, Innovation and Universities, the State Research Agency, and the European Regional Development Fund/European Union (grant PID2023-150190OA-I00/MENTAL-SED). The needs assessment and validation phases were conducted between January and May 2025. As of manuscript submission, a total of 91 participants (71 professionals and experts and 20 individuals with intellectual disability) have been recruited and have participated in the development and validation stages. The 2-week pilot study is currently concluding data collection. Data analysis for the pilot phase is expected to begin in October 2026, and the final results are expected to be published in 2027.

MENTALSED is expected to facilitate the early detection of emotional and behavioral changes, improve diagnostic accuracy, and enable the development of evidence-based preventive interventions. The integration of multiple sources of information is expected to help overcome traditional limitations in mental health assessment in this population.