Journal of Personalized Medicine最新文献

Background: The management of complex hepato-pancreato-biliary (HPB) pathologies demands exceptional surgical precision. Traditional two-dimensional imaging has limitations in depicting intricate anatomical relationships, potentially complicating preoperative planning. This review explores the synergistic application of three-dimensional (3D) reconstruction and artificial intelligence (AI) to support surgical decision-making in complex HPB cases. Methods: This narrative review synthesized the existing literature on the applications, benefits, limitations, and implementation challenges of 3D reconstruction and AI technologies in HPB surgery. Results: The literature suggests that 3D reconstruction provides patient-specific, interactive models that significantly improve surgeons' understanding of tumor resectability and vascular anatomy, contributing to reduced operative time and blood loss. Building upon this, AI algorithms can automate image segmentation for 3D modeling, enhance diagnostic accuracy, and offer predictive analytics for postoperative complications, such as liver failure. By analyzing large datasets, AI can identify subtle risk factors to guide clinical decision-making. Conclusions: The convergence of 3D visualization and AI-driven analytics is contributing to an emerging paradigm shift in HPB surgery. This combination may foster a more personalized, precise, and data-informed surgical approach, particularly in anatomically complex or high-risk cases. However, current evidence is heterogeneous and largely observational, underscoring the need for prospective multicenter validation before routine implementation.

Objective: This study aimed to measure nasal airway resistance (NAR) in obstructive sleep apnea (OSA) patients with nasal obstruction using active anterior rhinomanometry (AAR) and to evaluate whether NAR can predict the indication to include septoplasty as an additional procedure alongside drug-induced sleep endoscopy (DISE) and inferior turbinoplasty. Methods: We performed a retrospective observational study in OSA patients with nasal obstruction. According to nasal endoscopy and CT findings, patients were planned for either DISE with inferior turbinoplasty alone or DISE with inferior turbinoplasty and septoplasty. All patients underwent preoperative AAR, carried out under baseline and post-decongestion conditions. To test the ability of NAR to predict septoplasty indication, receiver operating characteristic (ROC) curves were generated for baseline and post-decongestion values. Logistic regression combined inspiratory/expiratory and unilateral/total NAR. The Area Under the Curve (AUC) was used to evaluate diagnostic accuracy, and optimal cut-offs were identified using Youden's index (J). Results: Forty-eight patients were included. Baseline NAR showed low accuracy (median AUC: 0.540 unilateral, 0.562 total) and no valid cut-offs were identified (median J: 0.213 unilateral, 0.233 total). Post-decongestion NAR performed better (median AUC: 0.649 unilateral, 0.738 total). Inspiratory and expiratory unilateral values merged with binary regression improved prediction (AUC 0.677 and 0.709). The highest accuracy was achieved when all rhinomanometric parameters were integrated into one logistic model (AUC = 0.947). Conclusions: Preoperative AAR may help refine nasal surgical planning during DISE in OSAS patients, supporting a personalized approach and potentially reducing the need for staged nasal procedures.

Aim: Preoperative planning and in-office patient education are essential elements of clinical management in patients afflicted with hand injuries. Three-dimensional (3D) printing aims to tackle these challenges by converting feedstock material into solid replicas. The purpose of this study was to review the clinical uses for 3D printing in hand surgery to identify approaches for delivering more personalized treatment strategies. Method: A systematic review was completed following PRISMA guidelines using Medline, Embase, and CINAHL databases, identifying studies published between 2013 and January 2025. A two-stage screening process, involving title, abstract, and full text reviews, was performed independently by two reviewers. Eligible studies included those involving patients with hand or wrist injuries (up to the distal radius) where 3D printing was utilized for diagnosis, surgical intervention, or rehabilitation. Results: The review included 751 patients (mean age: 38 years, range: 5-81 years) across 58 studies. The distal radius was the most commonly studied anatomical region (47%, N = 27), followed by the scaphoid (19%, N = 11). Key applications of 3D printing included preoperative planning (19%, N = 11), patient education (5%, N = 3), medical training (7%, N = 4), intra-operative assistance (38%, N = 22), splinting and casting (19%, N = 11), and prothesis and functional reconstruction (12%, N = 7). Conclusions: Despite its early stage of adoption in hand surgery, 3D printing has shown advantages, especially in enabling more personalized treatment strategies by improving intra-operative assistance, preoperative planning, and patient education. Further research is required to determine whether it positively affects postoperative outcomes, to calculate the cost-benefit ratio, and to compare its usage against standards of care.

Background/Objectives: Recurrent pregnancy loss (RPL) is one of the most challenging conditions in reproductive medicine, particularly when no identifiable cause can be determined after diagnostic evaluation. Although the role of immunological dysregulation has been hypothesized, the implementation of immunotherapies in clinical practice is controversial due to inconsistent findings and methodological heterogeneity across studies. This systematic review aims to provide an overview of the main characteristics of existing research on the role of immunological interventions in relation to In Vitro Fertilization (IVF) outcomes in women with RPL. Given the marked inter-individual variability in immunological mechanisms among affected women, evaluating these treatments may help identify future directions for personalized reproductive medicine. Methods: A comprehensive bibliographic search was systematically conducted from inception to October 2025 across databases, including Medline, Embase, Scopus, the Cochrane Database of Systematic Reviews, and ClinicalTrials.gov. Studies were included if they evaluated the efficacy of immunological treatments in women with unexplained RPL, comparing IVF outcomes between case and control groups. Results: Six cohort studies were included, four retrospective and two prospective. The immunological treatments investigated were granulocyte colony-stimulating factor (G-CSF), intravenous intralipid (with or without prednisolone), and lymphocyte immunization therapy (LIT). Despite some promising results, particularly for G-CSF and LIT, the studies were limited by small sample sizes, heterogeneous diagnostic criteria for RPL, and inconsistent treatment protocols. Furthermore, not all IVF outcomes, such as implantation and biochemical pregnancy rates, were reported. Conclusions: Current evidence is insufficient to support the use of immunotherapy in clinical practice for improving IVF outcomes in women with unexplained RPL. The variability in study design, patient selection, and immunotherapy regimens hinders the ability to draw firm conclusions. Well-designed randomized controlled trials with standardized definitions and outcome measures are needed to determine whether and for whom immunological treatments may offer clinical benefit.

Background/Objectives: Perinatal depression poses significant risks to maternal and fetal health, yet biomarkers for treatment response in the field remain limited. Given the overlap in symptoms with major depressive disorder (MDD) and the comparatively more vast MDD literature, identifying promising MDD biomarkers for treatment response and examining corresponding perinatal depression biomarkers can reveal translational opportunities. Methods: PUBMED searches were conducted for individual biomarkers and MDD and perinatal depression, as well as with treatment response to antidepressant pharmacological treatment and neuromodulation treatments. When available, evidence from meta-analyses and systematic reviews were preferentially summarized. Review: This narrative review presents the current evidence on MDD and perinatal depression treatment response biomarkers, including brain-derived neurotrophic factor (BDNF), S100 calcium-binding protein B (S100B), electroencephalography, event-related potentials, metabolomics, hypothalamic-pituitary-adrenal axis hormones, neuroimaging markers, inflammatory markers, and neuroactive steroids. Conclusions: Biomarker research in MDD yields insights on promising biomarkers for treatment response, including BDNF, S100B, theta band density and cordance, inflammatory markers IL-8, CRP, and TNF- α, and neuroactive steroids.

Background/Objectives: Computational studies using drift diffusion models on go/no-go escape tasks consistently show that individuals with suicidal ideation (SI) preferentially engage in active escape from negative emotional states. This study extends these findings by examining how individuals with SI update beliefs about action-outcome contingencies and uncertainty when trying to escape an aversive state. Methods: Undergraduate students with (n = 58) and without (n = 62) a lifetime history of SI made active (go) or passive (no-go) choices in response to stimuli to escape or avoid an unpleasant state in a laboratory-based negative reinforcement task. A Hierarchical Gaussian Filter (HGF) was used to estimate trial-by-trial trajectories of contingency and volatility beliefs, along with their uncertainties, prediction errors (precision-weighted), and dynamic learning rates, as well as fixed parameters at the person level. Bayesian mixed-effects models were used to examine the relationship between trial number, SI history, trial type, and all two-way interactions on HGF parameters. Results: We did not find an effect of SI history, trial type, or their interactions on perceived volatility of reward contingencies. At the trial level, however, participants with a history of SI developed progressively stronger contingency beliefs while simultaneously perceiving the environment as increasingly stable compared to those without SI experiences. Despite this rigidity, they maintained higher uncertainty during escape trials. Participants with an SI history had higher dynamic learning rates during escape trials compared to those without SI experiences. Conclusions: Individuals with an SI history showed a combination of cognitive inflexibility and hyper-reactivity to prediction errors in escape-related contexts. This combination may help explain difficulties in adapting to changing environments and in regulating responses to stress, both of which are relevant for suicide risk.

Introduction: Inherited retinal dystrophies (IRDs) are genetically determined conditions leading to progressive vision loss. Developments in gene therapy are creating new treatment options for IRD, but require precise imaging diagnosis and monitoring. According to recent studies, artificial intelligence, especially deep neural networks, could become an important tool for analyzing imaging data. Material and Methods: A systematic literature review was conducted in accordance with PRISMA guidelines, using PubMed, Scopus, and Web of Science databases to identify publications from 2015 to 2025 on the application of artificial intelligence in diagnosing inherited retinal dystrophies and monitoring the effects of gene therapy. The included articles passed a two-stage selection process and met the methodological quality criteria. Results: Among all the included studies it can be noticed that the use of artificial intelligence in diagnostics and therapy of IRDs is rather effective. The most common method was deep learning with its subtype convolutional neural networks (CNNs). However, there is still a place for improvement due to various limitations occurring in the studies. Conclusions: The review points to the growing potential of AI models in optimizing the diagnostic and therapeutic pathway in IRDs, while noting current limitations such as low data availability, the need for clinical validation, and the interpretability of the models. AI may play a key role in personalized ophthalmic medicine in the near future, supporting both clinical decisions and interventional study design.

Despite advances in staged protocols and fixation techniques, treatment of pilon fractures remains a significant challenge in orthopedic trauma, with up to 21% of patients requiring revision surgery. Management of a pilon fracture that has failed initial treatment involves navigating a myriad of complicating variables, including infection, bone loss, malalignment, and nonunion. Although no single surgical approach can be aptly applied to the broad range of pathology and severity spanned by these patients, this narrative review provides a systematic framework for developing a revision pilon reconstruction plan. We present a protocol for pre-operative assessment and review current techniques for infection eradication, bone defect management, deformity correction, and joint-preserving versus joint-sparing surgery. These fundamental strategies form the foundation of a successful salvage plan and can be personalized to address specific fracture morphology, host factors, and goals of care.

Drug-resistant epilepsy affects nearly one-third of individuals with epilepsy and remains a major cause of neurological morbidity worldwide. Surgical intervention offers a potential cure, but its success critically depends on the precise identification of the epileptogenic zone and the preservation of eloquent cortical and subcortical regions. This review aims to provide a comprehensive synthesis of current evidence on the role of multimodal neuroimaging in the personalized presurgical evaluation and planning of epilepsy surgery. We analyze how structural, functional, metabolic, and electro-physiological imaging modalities contribute synergistically to improving localization accuracy and surgical outcomes. Structural MRI remains the cornerstone of presurgical assessment, with advanced sequences, post-processing techniques, and ultra-high-field (7 T) MRI enhancing lesion detection in previously MRI-negative cases. Functional and metabolic imaging, including FDG-PET, ictal/interictal SPECT, and arterial spin labeling MRI, offer complementary insights by revealing regions of altered metabolism or perfusion associated with seizure onset. Functional MRI enables non-invasive mapping of language, memory, and motor networks, while diffusion tensor imaging and tractography delineate critical white-matter pathways to minimize postoperative deficits. Electrophysiological integration through EEG source imaging and magnetoencephalography refines localization when combined with MRI and PET data, forming the basis of multimodal image integration platforms used for surgical navigation. Our review also briefly explores emerging intraoperative applications such as augmented and virtual reality, intraoperative MRI, and laser interstitial thermal therapy, as well as advances driven by artificial intelligence, such as automated lesion detection and predictive modeling of surgical outcomes. By consolidating recent developments and clinical evidence, this review underscores how multimodal imaging transforms epilepsy surgery from a lesion-centered to a patient-centered discipline. The purpose is to highlight best practices, identify evidence gaps, and outline future directions toward precision-guided, minimally invasive, and function-preserving neurosurgical strategies for patients with drug-resistant focal epilepsy.

Background/Objectives: The aberrant subclavian artery (ASA) represents the most common congenital anomaly of the aortic arch, and is frequently associated with a Kommerell diverticulum, an aneurysmal dilation at the anomalous vessel origin. This condition carries a significant risk of rupture and dissection, and growing evidence indicates that local hemodynamic alterations may contribute to its development and progression. Computational Fluid Dynamics (CFD) provides a valuable non-invasive modality to assess biomechanical stresses and elucidate the pathophysiological mechanisms underlying these vascular abnormalities. Methods: In this study, twelve thoracic CT angiography scans were analyzed: six from patients with ASA and six from individuals with normal aortic anatomy. CFD simulations were performed using OpenFOAM, with standardized boundary conditions applied across all cases to isolate the influence of anatomical differences in flow behavior. Four key hemodynamic metrics were evaluated-Wall Shear Stress (WSS), Oscillatory Shear Index (OSI), Drag Forces (DF), and Turbulent Viscosity Ratio (TVR). The aortic arch was subdivided into Ishimaru zones 0-3, with an adapted definition accounting for ASA anatomy. For each region, time- and space-averaged quantities were computed to characterize mean values and oscillatory behavior. Conclusions: The findings demonstrate that patients with ASA exhibit markedly altered hemodynamics in zones 1-3 compared to controls, with consistently elevated WSS, OSI, DF, and TVR. The most pronounced abnormalities occurred in zones 2-3 near the origin of the aberrant vessel, where disturbed flow patterns and off-axis mechanical forces were observed. These features may promote chronic wall stress, endothelial dysfunction, and localized aneurysmal degeneration. Notably, two patients (M1 and M6) displayed particularly elevated drag forces and TVR in the distal arch, correlating with the presence of a distal aneurysm and right-sided arch configuration, respectively. Overall, this work supports the hypothesis that aberrant hemodynamics contribute to Kommerell diverticulum formation and progression, and highlights the CFD's feasibility for clarifying disease mechanisms, characterizing flow patterns, and informing endovascular planning by identifying hemodynamically favorable landing zones.