Explore the vast range of titles available.

Ultrasonic sensing may become a useful technique for distance measurement and object detection when optical visibility is not available. However, the research on detecting multiple target objects and locating their coordinates is limited. This makes it a valuable topic. Reflection signal data obtained from a single ultrasonic sensor may be just enough for the measurements of distance and reflection strength. On the other hand, if extracted properly, a scanned set of signal data by the same sensor holds a significant amount of information about the surrounding geometries. Evaluating this dataset from a single sensor scanning can be a perfect application for convolutional neural networks (CNNs). This study proposes an imaging technique based on a scanned dataset obtained by a single low-cost ultrasonic sensor. So that images are suitable for desired outputs in a CNN, a 3D printer is converted to an ultrasonic image scanner and automated to perform as a data acquisition system for the desired datasets. A deep learning model demonstrated by this work extracts object features using convolutional neural networks (CNNs) and performs coordinate estimation using regression layers. With the proposed solution, by training a reasonable amount of obtained data, 90% accuracy was achieved in the classification and position estimation of multiple objects with the CNN algorithm as a result of converting the signals obtained from ultrasonic sensors into images.

Background and Objectives: Preoperative systemic inflammation and nutritional status are known to affect prognosis and length of hospital stay (LoS) in patients undergoing transcatheter aortic valve replacement (TAVR). The Naples Prognostic Score (NPS) is a simple and effective scoring system that assesses both nutrition and inflammation, and has been shown to predict prognosis in various clinical settings. We aimed to determine the effect of NPS on LoS in patients undergoing TAVR. Materials and Methods: A total of 405 patients who underwent TAVR were retrospectively divided into two groups based on length of stay: early discharge (LoS ≤ 3 days) and late discharge (LoS > 3 days). The NPS was calculated prior to the TAVR procedure. Results: In the late discharge group (n = 164, 40.6%), patients were found to have significantly higher values for age, NYHA functional class, STS risk score, systolic pulmonary artery pressure, white blood cell count, neutrophil count, monocyte count, creatinine, glucose, NLR, rate of surgical access site, incidence of major/minor access site and structural complications, myocardial infarction, cerebrovascular events, acute kidney injury, major bleeding, blood transfusion, pacemaker implantation, and elevated NPS (p < 0.05). When independent risk factors for late discharge were evaluated, in addition to reduced eGFR, surgical closure of the access site, history of cerebrovascular events, need for pacemaker implantation, and blood transfusion, a high Naples Prognostic Score was also identified as an independent risk factor for prolonged hospital stay after TAVR in the multivariate logistic regression analysis (p < 0.05). Conclusions: A high Naples Prognostic Score (NPS), which reflects systemic inflammation and nutritional status, is associated with delayed hospital discharge and is an independent risk factor in patients undergoing TAVR.

Background/Objectives: Acute decompensated heart failure (ADHF) presents a significant clinical challenge characterized by frequent hospitalizations, high mortality rates, and substantial healthcare costs. The united index of hemoglobin, albumin, lymphocytes and platelets (HALP) is a new indicator that reflects systemic inflammation and nutritional status. This study aimed to investigate the prognostic utility of the HALP score and hematological parameters in predicting short-term mortality among ADHF patients admitted to the coronary care unit (CCU). Methods: This investigation adopts a retrospective observational design, encompassing a cohort of patients with ADHF who were followed in the CCU at our medical institution between January 2019 and April 2024. Results: The cohort of 227 individuals was dichotomized into two subsets based on the presence or absence of short-term mortality in the hospital, resulting in 163 (71.8%) and 64 (28.2%) individuals in the survivor and exitus groups, respectively. Age was significantly higher in the exitus group (p-value = 0.004). Hemoglobin, lymphocyte count, platelet count, albumin, and HALP score were significantly higher in the survivor group (all p-values < 0.001). No significant difference was observed between the groups in terms of gender, diabetes mellitus (DM), coronary artery disease (CAD), or ejection fraction (EF), although hypertension (HT) prevalence was significantly higher in the exitus group (p-value = 0.038). ROC analysis demonstrated that hemoglobin, lymphocyte, albumin, and HALP score had significant discriminative power, with albumin showing the highest AUC (0.814). Conclusions: In conclusion, the HALP score and hematological parameters represent valuable prognostic feature for short-term mortality prediction in ADHF patients admitted to the CCU. These findings underscore the importance of early risk stratification and targeted interventions guided by comprehensive biomarker assessments in optimizing patient outcomes.

Background and Objectives: Acute coronary syndrome (ACS) is a life-threatening cardiovascular condition with high mortality rates, necessitating accurate and early risk assessment to optimize patient outcomes. While traditional lipid markers, such as low-density lipoprotein-cholesterol (LDL-C) and high-density lipoprotein-cholesterol (HDL-C), are widely used, non-traditional lipid indices, including the lipoprotein combined index (LCI), atherogenic index of plasma (AIP), atherogenic index (AI), Castelli risk indices (CRI-I, CRI-II), and atherogenic combined index (ACI) may offer additional prognostic insights by reflecting the underlying atherogenic and inflammatory processes. This study aimed to assess the prognostic value of these non-traditional lipid indices, along with traditional lipid and biochemical markers, for in-hospital mortality in ACS patients. Materials and Methods: This retrospective observational study analyzed data from ACS patients admitted to the coronary care unit (CCU) between January 2019 and September 2024. A cohort of 920 patients was divided into survivor (n = 823, 89.46%) and non-survivor (n = 97, 10.54%) groups based on in-hospital mortality outcomes. Demographic, hematological, biochemical, and lipid profile data, including traditional and non-traditional lipid indices, were collected. Separate logistic regression models were developed for each index, adjusting for demographic and clinical variables in order to assess the independent predictive power of each non-traditional lipid index. Results: Significant differences were observed between survivor and non-survivor groups in terms of age, c-reactive protein (CRP), white blood cell count (WBC), hemoglobin (HGB), and creatinine levels (all p-values < 0.05). While traditional lipid markers, such as LDL-C and HDL-C, showed limited predictive value, non-traditional lipid indices demonstrated stronger associations. The highest Exp (Beta) values were observed for the CRI-II, AI, and CRI-I. An ROC analysis further confirmed that the CRI-II, AI, and CRI-I had the highest AUC values, with pairwise comparisons underscoring the CRI-II’s superior accuracy. These findings suggest that non-traditional lipid indices predict atherogenic risk better than traditional markers alone. Conclusions: Non-traditional lipid indices, particularly the CRI-I and II, AI, LCI, ACI, and AIP, were found to be significantly associated with in-hospital mortality in ACS patients. These indices may provide additional prognostic value beyond traditional lipid parameters; however, further prospective studies are needed to confirm their clinical utility. These results underscore the importance of integrating non-traditional lipid indices into routine risk assessments to improve mortality predictions and inform targeted interventions in high-risk ACS patients.

ObjectiveTo evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning. MethodsWe used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning. We used the area under the receiver operating characteristic (AUROC) curve to evaluate patient-level performance in detecting csPCa.ResultsThe PI-CAI training data had 425 scans with csPCa, while the in-house testing and fine-tuning data had 288 and 50 scans with csPCa, respectively. The nnU-Net model achieved an AUROC of 0.888 and 0.889 on the hidden validation and testing data. The model performed with an AUROC of 0.886 on the in-house testing data, with a slight decrease in performance to 0.870 using transfer learning.ConclusionsThe state-of-the-art deep learning method using prostate masks trained on large-scale bi-parametric MRI data provides high performance in detecting csPCa in internal and external testing data with different characteristics, demonstrating the robustness and generalizability of deep learning within and across datasets.Clinical relevance statementA self-adapting deep network, utilizing prostate masks and trained on large-scale bi-parametric MRI data, is effective in accurately detecting clinically significant prostate cancer across diverse datasets, highlighting the potential of deep learning methods for improving prostate cancer detection in clinical practice.Key pointsWe trained a self-adapting deep network on large-scale bi-parametric prostate MRI scans.The model provided a high performance at detecting csPCa on in-distribution tests.The performance did not drop on the external multi-center & multi-vendor data.Transfer learning did not improve the performance in the external test.

Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have emerged in the field of MNs for their time- and cost-efficient manufacturing. Tuning 3D printing parameters with artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is an emerging multidisciplinary field for optimization of manufacturing biomedical devices. Herein, we presented an AI framework to assess and predict 3D-printed MN features. Biodegradable MNs were fabricated using fused deposition modeling (FDM) 3D printing technology followed by chemical etching to enhance their geometrical precision. DL was used for quality control and anomaly detection in the fabricated MNAs. Ten different MN designs and various etching exposure doses were used create a data library to train ML models for extraction of similarity metrics in order to predict new fabrication outcomes when the mentioned parameters were adjusted. The integration of AI-enabled prediction with 3D printed MNs will facilitate the development of new healthcare systems and advancement of MNs’ biomedical applications.

The science of microrobots is accelerating towards the creation of new functionalities for biomedical applications such as targeted delivery of agents, surgical procedures, tracking and imaging, and sensing. Using magnetic properties to control the motion of microrobots for these applications is emerging. Here, 3D printing methods are introduced for the fabrication of microrobots and their future perspectives are discussed to elucidate the path for enabling their clinical translation.

: Premature ejaculation (PE) is one of the most common sexual problems in men. Autonomic nervous system (ANS), which is an important determinant of circadian changes in blood pressure (BP), also has a mechanism that controls ejaculation. We aimed to investigate the relationship between PE and BP variability. : This cross-sectional study included 80 normotensive patients with PE and 80 healthy volunteers. All the participants underwent 24-h ambulatory BP measurement. Participants were categorized into two groups based on the percentage of nocturnal BP dipping: the dipper BP (DBP), and non-dipper BP (NDBP) groups. : The frequency of the NDBP pattern was significantly higher in the PE group compared to the control group (48% vs. 28%, = 0.009). In the multivariate logistic regression analysis, the NDBP pattern remained significantly associated with PE [odds ratio: 0.399, 95% confidence interval: (0.207-0.770), = 0.006]. Within the PE group premature ejaculation diagnostic tool (PEDT) scores were significantly higher in individuals with NDBP than individuals with DBP (15.62 ± 2.85 vs. 14.32 ± 2.65, = 0.038). : The frequency of the NDBP pattern was significantly higher in the PE group among normotensive individuals. Additionally, within the PE group, PEDT scores were significantly higher in individuals with the NDBP pattern. A multidisciplinary approach and large-scale prospective studies are necessary to fully elucidate the relationship between PE and the cardiovascular system.

Purpose: Silent cerebral infarctions (SCI), as determined by neuron-specific enolase (NSE) elevations, may develop after the transcatheter aortic valve implantation (TAVI) procedure. Our aim in this study was to compare the SCI rates between patients who underwent routine pre-dilatation balloon aortic valvuloplasty (pre-BAV) and patients who underwent direct TAVI without pre-BAV. Methods: A total of 139 consecutive patients who underwent TAVI in a single center using the self-expandable Evolut-R valve (Medtronic, Minneapolis, Minnesota, USA) were included in the study. The first 70 patients were included in the pre-BAV group, and the last 69 patients were included in the direct TAVI group. SCI was detected by serum NSE measurements performed at baseline and 12 h after the TAVI. New NSE elevations > 12 ng/mL after the procedure were counted as SCI. In addition, SCI was scanned by MRI (magnetic resonance imaging) in eligible patients. Results: TAVI procedure was successful in all of the study population. Post-dilatation rates were higher in the direct TAVI group. Post-TAVI NSE positivity (SCI) was higher in the routine pre-BAV group (55(78.6%) vs. 43(62.3%) patients, p = 0.036) and NSE levels were also higher in this group (26.8 ± 15.0 vs. 20.5 ± 14.8 ng/ml, p = 0.015). SCI with MRI was found to be significantly higher in the pre-BAV group than direct TAVI group (39(55.1%) vs. 31(44.9%) patients). The presence of atrial fibrillation and diabetes mellitus (DM), total cusp calcification volume, calcification at arcus aorta, routine pre-BAV and failure at first try of the prosthetic valve implantation were significantly higher in SCI (+) group. In the multivariate analysis, presence of DM, total cusp calcification volume, calcification at arcus aorta, routine pre-BAV and failure at first try of the prosthetic valve implantation were significantly associated with new SCI development. Conclusions: Direct TAVI procedure without pre-dilation seems to be an effective method and avoidance of pre-dilation decreases the risk of SCI development in patients undergoing TAVI with a self-expandable valve.

Although various risk stratification models are available and currently being used, the performance of these models in different populations is still controversial. We aimed to investigate the relation between the Framingham and SCORE models and the presence and severity of coronary artery disease, which is detected using the SYNTAX score. The observational cross-sectional study population consisted of 227 patients with a mean age of 63.3±9.2 years. The patients were classified into low- and high-risk groups in the Framingham and SCORE models separately. Following coronary angiography, the patients were classified into SYNTAX=0 (SYNTAX score 0), low SYNTAX (SYNTAX score 1-22), and high SYNTAX (SYNTAX score>22) groups. The relation between the risk models and SYNTAX score was evaluated by student t test, Mann-Whitney U test or Kruskal-Wallis test and Receiver operating characteristic analysis were used to detect the discrimination ability in the prediction of SYNTAX score>0 and a high SYNTAX score. Both the Framingham and SCORE models were found to be effective in predicting the presence of coronary artery disease, and neither of the two models had superiority over each other [AUC=0.819 (0.767, 0.871) vs. 0.811 (0.757, 0.861), p=0.881]. Furthermore, both models were also effective in predicting the extent and severity of coronary artery disease [AUC=0.724 (0.656, 0.798) vs. 0.730 (0.662, 0.802), p=0.224]. When the subgroups were analyzed, the SCORE model was found to be better in predicting coronary artery disease extent and severity in subgroups of men and diabetics {[AUC=0.737 (0.668, 0.844) vs. 0.665 (0.560, 0.790), p=0.019], [AUC=0.733 (0.684, 0.798) vs. 0.680 (0.654, 0.750) p=0.029], respectively). In addition to their role in predicting cardiovascular events, the use of the Framingham and SCORE models may also have utility in predicting the extent and severity of coronary artery disease. The SCORE risk model has a slightly better performance than the Framingham risk model.