Journal of Personalized Medicine最新文献

Background: Understanding complex neurosurgical procedures and diseases, such as skull-base meningiomas, is challenging for patients due to the intricate anatomy and the involvement of critical neurovascular structures. Enhanced patient comprehension is crucial for satisfaction and improved clinical outcomes. Patient-specific 3D models have demonstrated benefits in patient education, though they are costly and time-intensive to produce. This study investigates whether the use of 3D volumetric reconstructions with anatomical segmentation, widely available via neuronavigation software, can improve patients' understanding of skull-base meningiomas, surgical procedures, and potential complications.

Materials and methods: This study included twenty patients with skull-base meningiomas. Three-dimensional volume reconstructions and anatomical segmentations were created using preoperative MRI sequences with neuronavigation software. These reconstructions were used during patient consultations where a surgeon explained key aspects of the disease, the surgical intervention, and potential complications. A questionnaire assessed the patients' perceptions of the utility of these 3D reconstructions.

Results: The majority of patients (75%) found the 3D volumetric reconstructions and anatomical segmentations to be more beneficial than MRI images for understanding their disease. Similarly, 75% reported improved comprehension of the surgical approach, and 85% felt that the reconstructions enhanced their understanding of potential surgical complications. Overall, 65% of patients considered the 3D reconstructions valuable in medical consultations.

Conclusions: Our study indicates that using accessible, cost-effective, and non-time-consuming 3D volumetric reconstructions with anatomical segmentation enhances patient understanding of skull-base meningiomas. Further research is necessary to confirm these findings, compare these reconstructions with physical 3D models and virtual reality models, and evaluate their impact on patient anxiety regarding the surgical procedure.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings.

Background/Objectives: Lumbar decompression surgery for degenerative lumbar stenosis is an intervention which addresses a degenerative condition affecting many patients. This article presents a meticulous three-phase surgical approach, derived from our clinical experiences and intertwining anatomical insights, offering a nuanced perspective tailored for the educational needs of young spinal surgeons. Methods: Six hundred and eighty-seven patients who underwent lumbar decompression surgery at a single institution were included in the present study. A retrospective analysis of patient demographics and surgical techniques was performed. All surgeries were performed by a consistent surgical team, emphasizing uniformity in approach. The surgical technique involves a meticulous three-phase process comprising exposure and skeletal visualization; microscopic identification and decompression; and undermining of the spinous process base and contralateral decompression. Results: Presenting results from 530 patients, the study examines demographic characteristics, health profiles, operative details, complications, and clinical assessments. The three-phase approach demonstrates low complication rates, absence of recurrences, and improved clinical outcomes, emphasizing its efficacy. Conclusions: The three-phase surgical approach emerges as a valuable educational tool for both novice and seasoned spinal surgeons. Rooted in anatomical insights, the structured methodology not only caters to the educational needs of young surgeons, but also ensures a standardized and safe procedure. The emphasis on tissue preservation and anatomical points aligns with current trends toward minimally invasive techniques, promising enhanced patient outcomes and satisfaction.

Background: The longitudinal study examines the effectiveness of a psychological support treatment for high-risk pregnancies using a between-groups design. It assesses the treatment's impact on depression and fear of COVID-19 at three time points, and on prenatal attachment between the 20th and 24th weeks of gestation (T0), postnatal attachment 15-20 days after birth (T1), and three months after birth (T2). Additionally, the study evaluates the treatment's effectiveness on PTSD related to childbirth and parental distress at T1 and T2.

Methods: The study involved 117 parents experiencing high-risk pregnancies from a Sicilian hospital: 84 mothers (40 in the experimental group, 44 in the control group) and 33 fathers (19 in the experimental group, 14 in the control group).

Results: ANOVA results showed that the psychological treatment was effective for maternal variables such as postnatal attachment and parental distress, and for paternal variables such as depression, prenatal attachment, PTSD symptoms, and parental distress (ANOVA, p < 0.05).

Conclusions: The study highlights the growing evidence for providing continuous psychological support to couples with high-risk pregnancies, emphasizing that this support should extend beyond childbirth to assist families through this transition.

Background: Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology.

Objective: Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors.

Methods: An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library.

Results: Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595.

Conclusions: Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient's condition.

Background: Obesity is a growing global health concern, often accompanied by dyslipidemia, contributing to cardiovascular risk. Understanding the patterns of dyslipidemia in different glycemic states is crucial for targeted interventions. This study compares dyslipidemia patterns in normoglycemic and prediabetic obesity to improve clinical management strategies. Methods: The study analyzed the complete lipid profiles of 138 subjects, comparing the medians, prevalence, diagnostic performance, and risk assessment of each lipid parameter across 54 non-obese (NO), 44 normoglycemic obese (NG-OB), and 40 pre-diabetic obese (PreDM-OB) groups. Results: Elevated total cholesterol (TC) and low-density lipoprotein (LDL) were the most prevalent forms of dyslipidemia observed in obesity (45.35% and 43.53%, respectively). Stratification by glycemic status revealed that triglyceride (TG) levels were elevated in both the NG-OB and PreDM-OB groups, with a more marked increase in the latter group (73.07 mg/dL vs. 97.87 mg/dL vs. 121.8 mg/dL, respectively). Elevated LDL showed better diagnostic performance and higher odds ratios (OR) in the NG-OB group (AUC = 0.660, p = 0.006; OR = 2.78, p = 0.022). Conversely, low high-density lipoprotein (HDL) was more common and exhibited significant diagnostic performance, with higher OR values in the PreDM-OB group (AUC = 0.687, p = 0.002; OR = 3.69, p = 0.018). Importantly, all lipid ratios were elevated in obesity, with TC/HDL showing the highest predictive ability for prediabetes (AUC = 0.7491, p < 0.001). Conclusions: These findings revealed unique and common lipid abnormalities in normoglycemic and prediabetic obesity. Future research should explore the effects of targeted lipid management on obesity-associated complications.

Objective: To analyze the clinical and cost outcomes of transcatheter edge-to-edge repair (TEER) for mitral regurgitation (MR) in heart failure (HF) patients. Methods: All 162 HF patients undergoing TEER for MR between January 2019 and March 2023 were included. A propensity-adjusted analysis was used to compare 32 systolic vs. 97 diastolic vs. 33 mixed (systolic + diastolic) HF patients. Systolic, diastolic, and mixed HF patients were defined according to AHA guidelines. The primary outcome was the long-term incidence of all-cause death and major adverse cardiovascular and cerebrovascular events (MACCEs, all-cause mortality + stroke + myocardial infarction + repeat intervention). Results: The mean age was 76.3 vs. 80.9 vs. 76 years old, and the mean ejection fraction (EF) was 39.5% vs. 59.8% vs. 39.7% in systolic vs. diastolic vs. mixed HF, respectively. Postoperatively, the diastolic vs. systolic HF group had a higher intensive care unit stay (21 vs. 0 h; HR 67.5 (23.7, 111.4)]; lower ventilation time [2 vs. 2.3 h; HR 49.4 (8.6, 90.2)]; lower EF [38% vs. 58.5%; HR 9.9 (3.7, 16.1)]. In addition, the diastolic vs. mixed HF groups had a lower incidence of EF < 50% (11 vs. 27 patients; HR 6.6 (1.6, 27.3) and a lower use of dialysis (one vs. three patients; HR 18.1 (1.1, 287.3), respectively. At a mean 1.6 years follow-up, all-cause death [HR 39.8 (26.2, 60.5)], MACCEs [HR 50.3 (33.7-75.1)], and new pacemaker implantations [HR 17.3 (8.7, 34.6)] were higher in the mixed group. There was no significant total hospital cost difference among the systolic (USD 106,859) vs. diastolic (USD 91,731) vs. mixed (USD 120,522) HF groups (p = 0.08). Conclusions: TEER for MR evidenced the worst postoperative and follow-up clinical outcomes in the mixed HF group compared to diastolic and systolic HF groups. No total hospital cost differences were observed.

Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms.

Background/objectives: Diabetic retinopathy (DR) is a common diabetes complication that can lead to blindness through vision-threatening complications like clinically significant macular edema and proliferative retinopathy. Identifying eyes at risk of progression using non-invasive methods could help develop targeted therapies to halt diabetic retinal disease progression.

Methods: A set of 82 imaging and systemic features was used to characterize the progression of nonproliferative diabetic retinopathy (NPDR). These features include baseline measurements (static features) and those capturing the temporal dynamic behavior of these static features within one year (dynamic features). Interpretable models were trained to distinguish between eyes with Early Treatment Diabetic Retinopathy Study (ETDRS) level 35 and eyes with ETDRS levels 43-47. The data used in this research were collected from 109 diabetic type 2 patients (67.26 ± 2.70 years; diabetes duration 19.6 ± 7.26 years) and acquired over 2 years.

Results: The characterization of the data indicates that NPDR progresses from an initial stage of hypoperfusion to a hyperperfusion response. The performance of the classification model using static features achieved an area under the curve (AUC) of the receiver operating characteristics equal to 0.84 ± 0.07, while the model using both static and dynamic features achieved an AUC of 0.91 ± 0.05.

Conclusion: NPDR progresses through an initial hypoperfusion stage followed by a hyperperfusion response. Characterizing and automatically identifying this disease progression stage is valuable and necessary. The results indicate that achieving this goal is feasible, paving the way for the improved evaluation of progression risk and the development of better-targeted therapies to prevent vision-threatening complications.

Background: Haptic feedback is an established method to provide sensory information (tactile or kinesthetic) about the performance of an activity that an individual can not consciously detect. After a stroke, hemiparesis usually leads to gait and balance disorders, where haptic feedback can be a promising approach to promote recovery. The aim of the present study is to understand its potential effects on gait and balance impairments, both after interventions and in terms of immediate effects. Methods: This research was carried out using the following scientific databases: Embase, Scopus, Web of Science, and Medline/PubMed from inception to May 2024. The Checklist for Measuring quality, PEDro scale, and the Cochrane collaboration tool were used to assess the methodological quality and risk of bias of the studies. Results: Thirteen articles were chosen for qualitative analysis, with four providing data for the meta-analysis. The findings did not yield definitive evidence on the effectiveness of haptic feedback for treating balance and gait disorders following a stroke. Conclusions: Further research is necessary in order to determine the effectiveness of haptic feedback mechanisms, with larger sample sizes and more robust methodologies. Longer interventions and pre-post design in gait training with haptic feedback are necessary.