Search Results (151)

Background: The long-term clinical and laboratory results of a 33-year follow-up of a Greek family with abetalipoproteinemia (ABL) are described. Case Report: The patients (two brothers and their sister, aged 57, 49, and 62 years, respectively) are still alive, being under close surveillance. In two of the three patients, diarrhea appeared in early infancy, while in the third, it appeared during adolescence. CNS symptomatology worsened after the second decade of life. At the same time, night blindness appeared in the advanced stages of the disease, resulting in almost complete loss of vision in one of the male patients and severe impairment in the other. The diagnosis was based on the clinical picture, ophthalmological findings, serum lipid estimations, and presence of peripheral acanthocytosis. All patients exhibited typical serum lipidemic profile, ophthalmological findings, and acanthocytes in the peripheral blood. During the follow-up period, strict dietary modifications were applied, including the substitution of fat with medium-chain triglycerides (MCT oil). After 33 years since the initial diagnosis, all patients are alive without any sign of liver dysfunction despite continuous use of MCT oil. However, symptoms from the central nervous system and vision impairment worsened. Conclusion: The course of these patients suggests that the application of a modified diet, including MCT oil, along with close surveillance, could prolong the survival of patients without significant side effects from the liver. Full article

Malignant mediastinal tumors are a group representing some of the most demanding oncological challenges for early, multi-level, and successful management. The timely identification of any suspicious clinical symptomatology is urgent in achieving an accurate, staged histological diagnosis, in order to follow up with an equally detailed medical therapeutic plan (interventional or not) and determine the principal goals regarding efficient overall treatment in these patients. We report a case of a 24-year-old male patient with an incident-free prior medical history. An initial chest X-ray was performed after the patient reported short-term, consistent moderate chest pain symptomatology, early work fatigue, and shortness of breath. The following imaging procedures (chest CT, PET-CT) indicated the presence of an anterior mediastinal mass (meas. ~11 cm × 10 cm × 13 cm, SUV: 8.7), applying additional pressure upon both right heart chambers. The Alpha-Fetoprotein (aFP) blood levels had exceeded at least 50 times their normal range. Two consecutive diagnostic attempts with non-specific histological results, a negative-for-malignancy fine-needle aspiration biopsy (FNA-biopsy), and an additional tumor biopsy, performed via mini anterior (R) thoracotomy with “suspicious” cellular gatherings, were performed elsewhere. After admission to our department, an (R) Video-Assisted Thoracic Surgery (VATS) was performed, along with multiple tumor biopsies and moderate pleural effusion drainage. The tumor’s measurements had increased to DMax: 16 cm × 9 cm × 13 cm, with a severe degree of atelectasis of the Right Lower Lobe parenchyma (RLL) and a pressure-displacement effect upon the Superior Vena Cava (SVC) and the (R) heart sinus, based on data from the preoperative chest MRA. The histological report indicated elements of a combined, non-seminomatous germ-cell mediastinal tumor, posthuberal-type teratoma, and embryonal carcinoma. The imminent chemotherapeutic plan included a “BEP” (Bleomycin^®/Cisplatin^®/Etoposide^®) scheme, which needed to be modified to a “VIP” (Cisplatin^®/Etoposide^®/Ifosfamide^®) scheme, due to an acute pulmonary embolism incident. While the aFP blood levels declined, even reaching normal measurements, the tumor’s size continued to increase significantly (DMax: 28 cm × 25 cm × 13 cm), with severe localized pressure effects, rapid weight loss, and a progressively worsening clinical status. Thus, an emergency surgical intervention took place via median sternotomy, extended with a complementary “T-Shaped” mini anterior (R) thoracotomy. A large, approx. 4 Kg mediastinal tumor was extracted, with additional RML and RUL “en-bloc” segmentectomy and partial mediastinal pleura decortication. The following histological results, apart from verifying the already-known posthuberal-type teratoma, indicated additional scattered small lesions of combined high-grade rabdomyosarcoma, chondrosarcoma, and osteosarcoma, as well as numerous high-grade glioblastoma cellular gatherings. No visible findings of the previously discovered non-seminomatous germ-cell and embryonal carcinoma elements were found. The patient’s postoperative status progressively improved, allowing therapeutic management to continue with six “TIP” (Cisplatin^®/Paclitaxel^®/Ifosfamide^®) sessions, currently under his regular “follow-up” from the oncological team. This report underlines the importance of early, accurate histological identification, combined with any necessary surgical intervention, diagnostic or therapeutic, as well as the appliance of any subsequent multimodality management plan. The diversity of mediastinal tumors, especially for young patients, leaves no place for complacency. Such rare examples may manifest, with equivalent, unpredictable evolution, obliging clinical physicians to stay constantly alert and not take anything for granted. Full article

Objectives: The objective of this study was to assess the effect of aerobic exercise on depressive symptoms and to determine the optimal exercise prescription for Parkinson’s disease (PD) patients. Methods: A comprehensive search was conducted across PubMed, Web of Science, Cochrane, Scopus, and Embase databases. A meta-analysis was conducted to determine the standardized mean difference (SMD) and 95% confidence interval. Results: Aerobic exercise significantly alleviated depressive symptoms in PD patients (SMD, −0.68, p = 0.002). Subgroup analyses revealed that moderate intensity aerobic exercise (SMD, −0.72, p = 0.0006), interventions conducted for ≥12 weeks (SMD, −0.85, p = 0.04), ≥3 times per week (SMD, −0.68, p = 0.002), ≥60 min per session (SMD, −0.57, p < 0.0001), and ≥180 min per week (SMD, −0.87, p = 0.0002) were more effective in improving depressive symptoms in PD patients, especially in PD patients with a disease duration of ≤6 years (SMD, −1.00, p = 0.04). Conclusions: Integrating the available data, it is clear that aerobic exercise is a proven method for alleviating depressive symptoms in PD patients. This meta-analysis provides empirical support for clinicians to recommend that PD patients engage in aerobic exercise regimens of no less than 12 weeks’ duration, performed at a minimum frequency of three sessions per week, with each session lasting in excess of 60 min and a cumulative weekly duration of at least 180 min, to effectively attenuate depressive symptomatology. Earlier implementation of aerobic exercise interventions is recommended, as PD patients in the early stages of the disease (up to 6 years post-diagnosis) may derive the greatest benefit in terms of depression symptom improvement from such programs. Full article

Background: Post-concussion syndrome (PCS) and Functional Neurological Disorder (FND), including Functional Cognitive Disorder (FCD), are two frequently encountered but diagnostically complex conditions. While PCS is conceptualized as a sequela of mild traumatic brain injury (mTBI), FND/FCD encompasses symptoms incompatible with recognized neurological disease, often arising in the absence of structural brain damage. Yet, both conditions exhibit considerable clinical overlap—particularly in the domains of cognitive dysfunction, emotional dysregulation, and symptom persistence despite negative investigations. Objective: This review critically examines the shared and divergent features of PCS and FND/FCD. We explore their respective epidemiology, diagnostic criteria, and risk factors—including personality traits and trauma exposure—as well as emerging insights from neuroimaging and biomarkers. We propose the “Functional Overlay Model” as a clinical tool for navigating diagnostic ambiguity in patients with persistent post-injury symptoms. Results: PCS and FND/FCD frequently share features such as subjective cognitive complaints, fatigue, anxiety, and heightened somatic vigilance. High neuroticism, maladaptive coping, prior psychiatric history, and trauma exposure emerge as common risk factors. Neuroimaging studies show persistent network dysfunction in both PCS and FND, with overlapping disruption in fronto-limbic and default mode systems. The Functional Overlay Model helps to identify cases where functional symptomatology coexists with or replaces an initial organic insult—particularly in patients with incongruent symptoms and normal objective testing. Conclusions: PCS and FND/FCD should be conceptualized along a continuum of brain dysfunction, shaped by injury, psychology, and contextual factors. Early recognition of functional overlays and stratified psychological interventions may improve outcomes for patients with persistent, medically unexplained symptoms after head trauma. This review introduces the Functional Overlay Model as a novel framework to enhance diagnostic clarity and therapeutic planning in patients presenting with persistent post-injury symptoms. Full article

Background/Objectives: Neurocognitive disorders (NCDs) encompass a spectrum of conditions that significantly impact cognitive domains, including attention, memory, and language. Mild NCD, increasingly prevalent with aging, represents an early stage of these disorders, characterized by cognitive deficits that do not interfere with daily functioning. Non-pharmacological therapies, especially cognitive stimulation, are widely recommended to preserve cognitive function of older adults. This study aimed to evaluate the effectiveness of a 12-week individual cognitive stimulation (iCS) program on cognitive performance, mood, and prefrontal cortex activation in older adults with mild NCD using a single-blind, randomized, parallel two-arm RCT. Methods: A sample of 36 older adults were selected from a central region of Portugal. The intervention group (n = 18) received 24 iCS sessions, twice weekly for 12 weeks. The control group (n = 18) completed their regularly scheduled activities. Outcomes included global cognitive function, executive functioning, and mood. All participants were assessed at baseline and after the intervention. Functional near infra-red spectroscopy (fNIRS) was also collected to measure prefrontal cortex activity at both time points in the intervention group. Results: The intervention group showed a significant improvement in global cognition and executive functions, and reduced depressive symptomatology compared to the control group. fNIRS data revealed enhanced activation and functional efficiency in the lateral prefrontal cortex following the iCS program. Adherence and degree of collaboration to the intervention were very high. Conclusions: These findings suggest that iCS is an effective approach to improving cognitive function and mood in mildly cognitively impaired older adults. Full article

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting countless individuals worldwide. PD is characterized by the onset of a marked motor symptomatology in association with several non-motor manifestations. The clinical phase of the disease is usually preceded by a long prodromal phase, devoid of overt motor symptomatology but often showing some conditions such as sleep disturbance, constipation, anosmia, and phonatory changes. To date, speech analysis appears to be a promising digital biomarker to anticipate even 10 years before the onset of clinical PD, as well serving as a useful prognostic tool for patient follow-up. That is why, the voice can be nominated as the non-invasive method to detect PD from healthy subjects (HS). Methods: Our study was based on cross-sectional study to analysis voice impairment. A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Cross-validation technique was applied to ensure the reliability of performance metrics on train and test subsets. These metrics (accuracy, recall, and precision), help determine the most effective models for distinguishing PD from healthy subjects. Result: Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. Although other models, such as support vector machine (SVM), could be considered with an accuracy of 75.29% and a ROC-AUC score of 82.63%, RF was by far the best one when evaluated across all metrics. The K-nearest neighbor (KNN) and decision tree (DT) performed the worst. Notably, by combining a comprehensive set of long-term, short-term, and non-standard acoustic features, unlike previous studies that typically focused on only a subset, our study achieved higher predictive performance, offering a more robust model for early PD detection. Conclusions: This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector. Full article

Background and Objectives: Sleep apnea (OSA) significantly impacts patients’ health, affecting cognitive, emotional, and social functioning, thus reducing overall quality of life (QoL). Despite global research interest, there are limited data on the QoL of Romanian patients with OSA. Aim of study: This study aimed to assess the quality of life of patients diagnosed with OSA using the Sleep Apnea Quality of Life Index (SAQLI), a validated, disease-specific questionnaire. Material and Methods: A cross-sectional study was conducted between January 2021 and February 2022 on 28 adult patients from medical units in Iași (Romania) and Albania. The patients were clinically evaluated and confirmed to have OSA (AHI > 5). QoL was assessed pre-treatment using the SAQLI, covering daily activities, social interactions, emotional status, and symptomatology. Statistical analyses were performed using SPSS v26.0. Results: Most patients had moderate (32.1%) or severe OSA (53.6%). Lower QoL scores correlated with increased disease severity. Significant impairments were observed in daily functioning, social relationships, and emotional well-being. Patients with severe OSA reported the lowest scores across all domains. Conclusions: OSA severely affects quality of life, particularly in patients with moderate to severe forms. Early diagnosis and personalized, multidisciplinary management strategies are essential to improving outcomes and overall patient well-being. Full article

Background and objectives. Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) are frequently present in university students, even without a clinical diagnosis, and may be aggravated by various factors. This study analyzes the relationship between these symptoms and the use of alcohol, tobacco, cannabis, and other drugs by young university students. Materials and methods. A cross-sectional study was conducted with 397 university students using an anonymous online questionnaire. ADHD symptoms were assessed with the Adult ADHD Self-Report Scale (ASRS), alcohol use with the Alcohol Use Disorders Identification Test (AUDIT-C), nicotine dependence with the Fagerström test, cannabis use with the Cannabis Abuse Screening Test (CAST), and the use of other substances with an adaptation of the latter. Results. The mean age was 21.4 years, and most participants (76.6%) were women. Of the participants, 46.1% presented symptoms compatible with ADHD according to the ASRS. The most frequent items were difficulty maintaining attention during boring tasks (77.3%), avoiding tasks that require mental effort (76.8%), and being easily distracted by noise or external activity (73.8%). Significant differences were found between ASRS scores and gender, with scores being higher and more consistent among those students who identified themselves as non-binary gender (male or female) (p < 0.01). A significant association was also found between smoking and a higher ASRS score (p < 0.01). Although no significant associations with body mass index were detected, a trend toward greater symptomatology was observed in obese individuals. In multivariate analysis, still gender and smoking significantly (p = 0.12 and p = 0.031, respectively) predicted ADHD symptoms (ASRS score). The model R = 0.228 (R squared = 0.052, F = 1.62, p = 0.077). No statistically significant differences were found between ADHD symptoms and the use of alcohol, cannabis, or other substances in either bivariate or multivariate analyses. Conclusions. This study underscores the importance of early detection of ADHD symptoms in the university setting, considering factors such as gender and smoking habit. Future research should focus on aggravating factors such as academic stress and problematic technology use. Full article

Background/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through unstructured narratives in clinical notes, online forums, and telehealth platforms. This study proposes a machine learning pipeline (sleepCare) that classifies sleep-related narratives into clinically meaningful categories, including stress-related, neurodegenerative, and breathing-related disorders. The proposed framework employs natural language processing (NLP) and machine learning techniques to support remote applications and real-time patient monitoring, offering a scalable solution for the early identification of sleep disturbances. Methods: The sleepCare consists of a three-tiered classification pipeline to analyze narrative sleep reports. First, a baseline model used a Multinomial Naïve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. Finally, a transformer-based model (BERT) was fine-tuned to extract contextual embeddings, using the [CLS] token as input for SVM classification. Each model was evaluated using stratified train-test splits and 10-fold cross-validation. Hyperparameter tuning via GridSearchCV optimized performance. The dataset contained 475 labeled sleep narratives, classified into five etiological categories relevant for clinical interpretation. Results: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of 81% on the test set. Class-wise F1-scores ranged from 0.72 to 0.91, with the highest performance observed in classifying normal or improved sleep (F1 = 0.91). The macro average F1-score was 0.78, indicating balanced performance across all categories. GridSearchCV identified the optimal SVM parameters (C = 4, kernel = ‘rbf’, gamma = 0.01, degree = 2, class_weight = ‘balanced’). The confusion matrix revealed robust classification with limited misclassifications, particularly between overlapping symptom categories such as stress-related and neurodegenerative sleep disturbances. Conclusions: Unlike generic large language model applications, our approach emphasizes the personalized identification of sleep symptomatology through targeted classification of the narrative input. By integrating structured learning with contextual embeddings, the framework offers a clinically meaningful, scalable solution for early detection and differentiation of sleep disorders in diverse, real-world, and remote settings. Full article

This study aims to illustrate the impact of accumulated traumatic experiences in adolescence and to evaluate the potential of brief focal psychotherapy (BFP) as a treatment approach for complex trauma. We present the case of a 14-year-old boy who experienced vicarious gender-based violence, child abuse, early maternal separation without alternative secure attachment figures, and forced sudden migration. The patient exhibited symptoms consistent with post-traumatic stress disorder (PTSD) and complex trauma. The culturally sensitive intervention, delivered at a public child and adolescent mental health center, consisted of twenty weekly individual sessions of 45 min each, complemented by three 45 min psychoeducation sessions with the caregiver. The assessment was conducted using a multitrait and multi-informant approach, systematically gathering information across multiple domains of functioning (emotional–behavioral, physical, cognitive, self-perception, and relational) and from different sources (the adolescent, his mother, and the clinician) through clinical interviews, projective techniques, and parental feedback. The primary therapeutic focus was the establishment of a secure therapeutic alliance to facilitate emotional exploration and trauma processing. Following treatment, the patient demonstrated significant improvements in emotional regulation, family relationships, and school performance, as measured by both self-report and parental observations. This case highlights the potential of BFP in addressing complex trauma in adolescents, particularly during a developmental stage marked by increased vulnerability to the effects of chronic trauma exposure. The findings suggest that BFP can effectively reduce both acute symptomatology and broader psychosocial consequences associated with prolonged and cumulative trauma. Further research, particularly controlled studies and longitudinal follow-ups, is needed to refine and optimize the use of BFP by mental health professionals working with adolescents affected by complex trauma. Full article

Background and objectives: Pelvic inflammatory disease (PID), primarily associated with sexually transmitted infections (STIs), represents a diagnostic challenge in virgin pediatric patients due to the often vague, non-specific symptomatology, which can mimic other conditions. Management prioritizes targeted antimicrobial therapy, with surgical intervention reserved for complications like tubo-ovarian abscess (TOA). The present systematic review aimed to critically evaluate the available evidence from case reports of PID in virgin pediatric and adolescent patients. Methods: The search strategy was in accordance with PRISMA guidelines. Case reports published up to March 2025 were searched through PubMed, Embase, Scopus, and Google Scholar databases. We included English-language case reports on non-sexually active pediatric and adolescent patients with available full text, excluding commentaries, reviews, and editorials. The Critical Appraisal Checklist for Case Reports was used for the quality assessment of case reports. Through descriptive analysis, PID symptoms, diagnostic, and management modalities were reviewed. The quality of the included case reports was assessed using the JBI Critical Appraisal Checklist. This review was not registered and did not receive external funding. Results: Among the 56 case reports searched, 20 reports were selected and analyzed based on eight criteria. The most frequently reported symptoms were lower abdominal pain (95.8%), fever (63.6%), and gastrointestinal symptoms (50%). Common comorbidities included urinary tract infections (22.7%), congenital anomalies (18.1%), and appendicitis history (18.1%). Escherichia coli and Streptococcus species were the predominant pathogens identified. All patients received antibiotic therapy, while 90.9% underwent surgical intervention. Favorable outcomes were achieved in 72.7% of cases, though 27.2% experienced complications or recurrences. Conclusions: Although commonly linked to sexual activity, PID should be considered in sexually inactive pediatric patients presenting with abdominal pain and adnexal masses. Early diagnosis, appropriate imaging, and timely treatment are crucial to improve outcomes and reduce complications. The evidence in this review is limited by its reliance on case reports, which may introduce bias and restrict generalizability. Full article

Aberrant salience, defined as the inappropriate attribution of significance to neutral stimuli, is increasingly recognized as a critical mechanism in the onset of psychotic disorders. In young individuals at ultra-high risk (UHR) for psychosis, abnormal salience processing may serve as a precursor to full-blown psychotic symptoms, contributing to distorted perceptions and the onset of psychotic ideation. This review examines current literature on aberrant salience among UHR youth, exploring its neurobiological, psychological, and behavioral dimensions. Through a comprehensive analysis of studies involving neuroimaging, cognitive assessments, and symptomatology, we assess the consistency of findings across diverse methodologies. Additionally, we evaluate factors contributing to aberrant salience, including neurochemical imbalances, dysregulation in dopamine pathways, and environmental stressors, which may jointly increase psychosis vulnerability. Identifying aberrant salience as a measurable trait in UHR populations could facilitate earlier identification and targeted interventions. Implications for clinical practice are discussed, highlighting the need for specialized therapeutic approaches that address cognitive and emotional dysregulation in salience attribution. Recent research underscores the importance of aberrant salience in early psychosis research and advocates for further studies on intervention strategies to mitigate progression to psychosis among UHR individuals. Full article

Autism Spectrum Disorder (ASD) poses significant challenges in diagnosis due to its diverse symptomatology and the complexity of early detection. Atypical gait and gesture patterns, prominent behavioural markers of ASD, hold immense potential for facilitating early intervention and optimising treatment outcomes. These patterns can be efficiently and non-intrusively captured using modern computational techniques, making them valuable for ASD recognition. Various types of research have been conducted to detect ASD through deep learning, including facial feature analysis, eye gaze analysis, and movement and gesture analysis. In this study, we optimise a dual-stream architecture that combines image classification and skeleton recognition models to analyse video data for body motion analysis. The first stream processes Skepxels—spatial representations derived from skeleton data—using ConvNeXt-Base, a robust image recognition model that efficiently captures aggregated spatial embeddings. The second stream encodes angular features, embedding relative joint angles into the skeleton sequence and extracting spatiotemporal dynamics using Multi-Scale Graph 3D Convolutional Network(MSG3D), a combination of Graph Convolutional Networks (GCNs) and Temporal Convolutional Networks (TCNs). We replace the ViT model from the original architecture with ConvNeXt-Base to evaluate the efficacy of CNN-based models in capturing gesture-related features for ASD detection. Additionally, we experimented with a Stack Transformer in the second stream instead of MSG3D but found it to result in lower performance accuracy, thus highlighting the importance of GCN-based models for motion analysis. The integration of these two streams ensures comprehensive feature extraction, capturing both global and detailed motion patterns. A pairwise Euclidean distance loss is employed during training to enhance the consistency and robustness of feature representations. The results from our experiments demonstrate that the two-stream approach, combining ConvNeXt-Base and MSG3D, offers a promising method for effective autism detection. This approach not only enhances accuracy but also contributes valuable insights into optimising deep learning models for gesture-based recognition. By integrating image classification and skeleton recognition, we can better capture both global and detailed motion patterns, which are crucial for improving early ASD diagnosis and intervention strategies. Full article

Eating disorders (EDs) in children and adolescents pose significant diagnostic and therapeutic challenges due to their early onset, developmental complexity, and frequent psychiatric comorbidities. This narrative review identifies key clinical and systemic challenges, including difficulties in early detection, overlapping symptomatology, limited pharmacological options, and unequal access to specialized care. Recent progress includes revisions in diagnostic criteria (e.g., DSM-5 and ICD-11), advancements in psychometric assessment tools tailored for pediatric populations, and increasing evidence supporting psychotherapeutic interventions such as cognitive behavioral therapy, family-based therapy, and digital health approaches. Future directions involve long-term outcome studies on treatment efficacy, developing culturally sensitive and personalized care models, and implementing integrated multidisciplinary treatment frameworks. By synthesizing empirical literature from 2018 to 2024, this review underscores the urgent need for developmentally informed, evidence-based strategies to enhance the early detection, treatment, and recovery outcomes for young individuals affected by EDs. Full article

Background: This study investigated the pathways through which various psychological problems occurring across the perinatal period influence mother–child bonding within the first 18 months postpartum, with a particular focus on the relationship between perinatal and postpartum depression and childbirth-related posttraumatic stress symptomatology. Methods: A multi-stage longitudinal design included three assessment points: recruitment and initial assessment in the third trimester of pregnancy (T1), a second assessment at 8 months postpartum (T2), and a final assessment at 18 months postpartum (T3). A total of N = 51 mothers completed all three waves (total follow-up period per participant: approximately 21 months). Sociodemographic data were collected, and all assessments were completed online. Results: Our findings revealed significant indirect effects, linking higher levels of perinatal depressive symptoms in the third trimester with mother–child bonding difficulties at 18 months postpartum. This association was mediated by both greater childbirth-related posttraumatic stress symptoms and elevated postpartum depressive symptoms at 8 months. Conclusions: These preliminary results highlight how complex perinatal factors at different stages (i.e., during pregnancy and early postpartum) influence mother–child bonding at 18 months postpartum. Understanding these pathways is essential in order to inform targeted interventions and to promote optimal maternal mental health and bonding outcomes. Full article