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Background: Laser ablation (LA) is a minimally invasive treatment. It has been widely used since the early 2000s to induce volume reduction of symptomatic benign thyroid nodules. Up to 40% of laser-treated nodules have been reported to achieve a volume reduction of <50% (technique inefficacy) at 12 months and tend to regrow over time., Objective: This study aimed to assess the optimal baseline volume and energy to be delivered to minimize technique inefficacy., Methods: This was a retrospective study. Data were collected, including baseline volume, energy delivered, and 12-month volume reduction ratio (VRR) of spongiform nodules (EU-TIRADS 2) treated with LA between 2010 and 2020. Based on these data, the optimal baseline volume and energy to be delivered were calculated to maximize the rate of nodules with technique efficacy (VRR ≥ 50% at 12-month follow-up)., Results: A total of 205 patients with spongiform nodules were included in this study. The energy delivered was positively associated with VRR. However, no association was observed between baseline volume and VRR. Delivering energy ≥500 J/mL to nodules with a mean baseline volume of 11.4 ± 4 mL resulted in technique efficacy in 83% of cases., Conclusion: Treating spongiform nodules with a baseline volume of ≤15 mL and delivering energy ≥500 J/mL are key factors for achieving a relevant rate of technique efficacy., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure the similarity between the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Perera-Lago J et al.)

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002-03/08/2007. All data regarding patients' medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934-0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896-0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911-0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients' level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation., (© 2021. The Author(s).)

Background: Radiofrequency (RF) is a therapeutic modality for reducing the volume of large benign thyroid nodules. If thermal therapies are interpreted as an alternative strategy to surgery, critical issues in their use are represented by the extent of nodule reduction and by the durability of nodule reduction over a long period of time., Objective: To assess the ability of machine learning to discriminate nodules with volume reduction rate (VRR) < or ≥50% at 12 months following RF treatment., Methods: A machine learning model was trained with a dataset of 402 cytologically benign thyroid nodules subjected to RF at six Italian Institutions. The model was trained with the following variables: baseline nodule volume, echostructure, macrocalcalcifications, vascularity, and 12-month VRR., Results: After training, the model could distinguish between nodules having VRR <50% from those having VRR ≥50% in 85% of cases (accuracy: 0.85; 95% confidence interval [CI]: 0.80-0.90; sensitivity: 0.70; 95% CI: 0.62-0.75; specificity: 0.99; 95% CI: 0.98-1.0; positive predictive value: 0.95; 95% CI: 0.92-0.98; negative predictive value: 0.95; 95% CI: 0.92-0.98)., Conclusions: This study demonstrates that a machine learning model can reliably identify those nodules that will have VRR < or ≥50% at 12 months after one RF treatment session. Predicting which nodules will be poor or good responders represents valuable data that may help physicians and patients decide on the best treatment option between thermal ablation and surgery or in predicting if more than one session might be necessary to obtain a significant volume reduction., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2019 by European Thyroid Association Published by S. Karger AG, Basel.)

Objective: Laser ablation is a therapeutic modality used to reduce the volume of large benign thyroid nodules. Unsatisfactory reduction and regrowth are observed in some treated nodules. The aim of the study was to evaluate the long-term outcomes of laser treatment for solid nodules during a 5-year follow-up period, the regrowth rate, and the predictive risk factors of nodule regrowth., Materials and Methods: We retrospectively evaluated patients with benign, solid, cold thyroid nodules who underwent laser ablation and were followed-up for 5 years. According to the selection criteria, 104 patients were included (median baseline nodule volume, 12.5 mL [25.0-75.0%, 8-18 mL]; median energy delivered, 481.5 J/mL [25.0-75.0%, 370-620 J/mL]). Nodule volume, thyroid function test results, and ultrasound were evaluated at baseline and then annually after the procedure., Results: Of 104 patients, 31 patients (29.8%) had a 12-month volume reduction ratio (VRR) < 50.0% and 39 (37.5%) experienced nodule regrowth. Of these 39 patients, 17 (43.6%) underwent surgery and 14 (35.9%) underwent a second laser treatment. The rate of nodule regrowth was inversely related to the 12-month VRR, i.e., the lower the 12-month VRR, the higher the risk of regrowth ( p < 0.001). The mean time for nodule regrowth was 33.5 ± 16.6 months. The 12-month VRR was directly related to time to regrowth, i.e., the lower the 12-month VRR, the shorter the time to regrowth ( p < 0.001; R² = 0.3516). Non-spongiform composition increased the risk of regrowth with an odds ratio of 4.3 (95% confidence interval [CI] 1.8-10.2; p < 0.001); 12-month VRR < 50.0% increased the risk of regrowth with an odds ratio of 11.7 (95% CI 4.2-32.2; p < 0.001)., Conclusion: The VRR of thyroid nodules subjected to similar amounts of laser energy varies widely and depends on the nodule composition; non-spongiform nodules are reduced to a lesser extent and regrow more frequently than spongiform nodules. A 12-month VRR < 50.0% is a predictive risk factor for regrowth and correlates with the time to regrowth., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

The role of microbial dysbiosis in scalp disease has been recently hypothesized. However, little information is available with regards to the association between microbial population on the scalp and hair diseases related to hair growth. Here we investigated bacterial communities in healthy and Alopecia areata (AA) subjects. The analysis of bacterial distribution at the genus level highlighted an increase of Propionibacterium in AA subjects alongside a general decrease of Staphylococcus. Analysis of log Relative abundance of main bacterial species inhabiting the scalp showed a significant increase of Propionibacterium acnes in AA subjects compared to control ones. AA scalp condition is also associated with a significant decrease of Staphylococcus epidermidis relative abundance. No significant changes were found for Staphylococcus aureus. Therefore, data from sequencing profiling of the bacterial population strongly support a different microbial composition of the different area surrounded hair follicle from the epidermis to hypodermis, highlighting differences between normal and AA affected the scalp. Our results highlight, for the first time, the presence of a microbial shift on the scalp of patients suffering from AA and gives the basis for a larger and more complete study of microbial population involvement in hair disorders., Competing Interests: DP, BM are employed by Giuliani S.p.A.; FR serves as consultant for Giuliani S.p.A; GG is part of Board of Directors of Giuliani S.p.A. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Benefit deriving from the use of light is known since ancient time, but, only in the last decades of twentieth century, we witnessed the rapid expansion of knowledge and techniques. Light-emitted diode (LED)-based devices represent the emerging and safest tool for the treatment of many conditions such as skin inflammatory conditions, aging, and disorders linked to hair growth. The present work reviews the current knowledge about LED-based therapeutic approaches in different skin and hair disorders. LED therapy represents the emerging and safest tool for the treatment of many conditions such as skin inflammatory conditions, aging, and disorders linked to hair growth. The use of LED in the treatment of such conditions has now entered common practice among dermatologists. Additional controlled studies are still needed to corroborate the efficacy of such kind of treatment.

Objective: An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals., Results: The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to [Formula: see text] while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.

Objectives: To explore the role of diffusion tensor imaging (DTI)-based histogram analysis and functional diffusion maps (fDMs) in evaluating structural changes of low-grade gliomas (LGGs) receiving temozolomide (TMZ) chemotherapy., Methods: Twenty-one LGG patients underwent 3T-MR examinations before and after three and six cycles of dose-dense TMZ, including 3D-fluid-attenuated inversion recovery (FLAIR) sequences and DTI (b = 1000 s/mm(2), 32 directions). Mean diffusivity (MD), fractional anisotropy (FA), and tensor-decomposition DTI maps (p and q) were obtained. Histogram and fDM analyses were performed on co-registered baseline and post-chemotherapy maps. DTI changes were compared with modifications of tumour area and volume [according to Response Assessment in Neuro-Oncology (RANO) criteria], and seizure response., Results: After three cycles of TMZ, 20/21 patients were stable according to RANO criteria, but DTI changes were observed in all patients (Wilcoxon test, P ≤ 0.03). After six cycles, DTI changes were more pronounced (P ≤ 0.005). Seventy-five percent of patients had early seizure response with significant improvement of DTI values, maintaining stability on FLAIR. Early changes of the 25th percentiles of p and MD predicted final volume change (R(2) = 0.614 and 0.561, P < 0.0005, respectively). TMZ-related changes were located mainly at tumour borders on p and MD fDMs., Conclusions: DTI-based histogram and fDM analyses are useful techniques to evaluate the early effects of TMZ chemotherapy in LGG patients., Key Points: • DTI helps to assess the efficacy of chemotherapy in low-grade gliomas. • Histogram analysis of DTI metrics quantifies structural changes in tumour tissue. • Functional diffusion maps (fDMs) spatially localize the changes of DTI metrics. • Changes in DTI histograms and fDMs precede changes in conventional MRI. • Early changes in DTI histograms and fDMs correlate with seizure response.

Background: Arrhythmias are frequent in Systemic Sclerosis (SSc) and portend a bad prognosis, accounting alone for 6% of total deaths. Many of these patients die suddenly, thus prevention and intensified risk-stratification represent unmet medical needs. The major goal of this study was the definition of ECG indexes of poor prognosis., Methods: We performed a prospective cohort study to define the role of 24h-ECG-Holter as an additional risk-stratification technique in the identification of SSc-patients at high risk of life-threatening arrhythmias and sudden cardiac death (SCD). One-hundred SSc-patients with symptoms and/or signs suggestive of cardiac involvement underwent 24h-ECG-Holter. The primary end-point was a composite of SCD or need for implantable cardioverter defibrillator (ICD)., Results: Fifty-six patients (56%) had 24h-ECG-Holter abnormalities and 24(24%) presented frequent ventricular ectopic beats (VEBs). The number of VEBs correlated with high-sensitive cardiac troponin T (hs-cTnT) levels and inversely correlated with left-ventricular ejection fraction (LV-EF) on echocardiography. During a mean follow-up of 23.1±16.0 months, 5 patients died suddenly and two required ICD-implantation. The 7 patients who met the composite end-point had a higher number of VEBs, higher levels of hs-cTnT and NT-proBNP and lower LV-EF (p = 0.001 for all correlations). All these 7 patients had frequent VEBs, while LV-EF was not reduced in all and its range was wide. At ROC curve, VEBs>1190/24h showed 100% of sensitivity and 83% of specificity to predict the primary end-point (AUROC = 0.92,p<0.0001). Patients with VEBS>1190/24h had lower LV-EF and higher hs-cTnT levels and, at multivariate analysis, the presence of increased hs-cTnT and of right bundle branch block on ECG emerged as independent predictors of VEBs>1190/24h. None of demographic or disease-related characteristics emerged as predictors of poor outcome., Conclusions: VEBS>1190/24h identify patients at high risk of life-threatening arrhythmic complications. Thus, 24h-ECG-Holter should be considered a useful additional risk-stratification test to select SSc-patients at high-risk of SCD, in whom an ICD-implantation could represent a potential life-saving intervention.

Objective: To evaluate the serum levels of tumour-associated antigens (TAAs) in patients with SSc and interstitial lung disease (ILD) and to define whether their levels mirror the severity and the progression of lung damage., Methods: Data from 80 SSc patients with ILD were collected at baseline and after 2 years as well as from 40 SSc controls without ILD. The occurrence of any malignancy was recorded., Results: At baseline, an increase of at least one TAA was present in 35 SSc patients with ILD compared with 6 SSc patients without ILD (P < 0.0001); this was associated with lower forced vital capacity (FVC) and higher interstitial and alveolar scores. Levels of carbohydrate antigen 15-3 and carcinoembryonic antigen inversely correlated with FVC and directly correlated with alveolar and interstitial scores and their levels were higher in patients who presented a progression of lung damage after 2 years. During 4 years of follow-up, a malignancy was detected in seven patients who already had an increase of at least one TAA. Values of TAAs increased over time in patients who developed cancer, while their trend remained stable in the others. At multivariate analysis, to have three or more TAAs emerged as a strong independent predictor of the development of malignancies [relative risk 24.1 (95% CI 1.8, 315.0), P = 0.02]., Conclusion: TAAs can be elevated in the sera of SSc patients and correlate with the degree of lung damage, suggesting a role as severity biomarkers. Close follow-up is necessary in SSc patients because of the increased cancer risk overall in patients with increased TAAs., (© The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal., Results: Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital "Ospedali Riuniti di Ancona". Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94% of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space)., Conclusion: In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis.

Objectives: To assess the long-term efficacy and safety of single and multiple courses of rituximab therapy in systemic sclerosis (SSc) patients with and without lung disease., Methods: A total of 20 SSc patients with a diffuse disease were treated with rituximab. At baseline and during follow-up the lung involvement was evaluated with pulmonary function tests (FVC and DLCO) and with lung high-resolution computed tomography (HRCT)., Results: The skin score, activity, and severity indices improved significantly after 12 months and at final follow-up compared to baseline. After 12 months, there was a significant increase of FVC and TLC compared to baseline (p = 0.024 and p = 0.005, respectively), while the mean DLCO value remained stable. Considering the last available follow-up in six patients with restrictive lung disease at baseline, two patients (33.3%) experienced an increase of more than 10% of FVC, one patient had a decrease of FVC >10%, while in three patients FVC remained stable (50%). After the mean follow-up of 48.5 ± 20.4 months, among the patients with normal lung parameters at baseline, FVC remained stable in 12 (85.7%) and in one patient (14.3%) it increased by more than 10%. At the final follow-up, the alveolar and interstitial HRCT scores remained stable in more than 80% of patients, both in patients with and without restrictive lung disease at baseline., Conclusions: Anti-CD20 B cell depletion therapy is effective on skin involvement but seems also to preserve the pulmonary function, as supported by a stable or improved FVC and stable interstitial score, suggesting a possible role of rituximab as a modifying therapy overall in early diffuse SSc., (Copyright © 2014 Elsevier Inc. All rights reserved.)

gamma-Glutamyltranspeptidase (gamma GT) appears in serum in multiple forms; their significance and clinical utility in hepatobiliary and pancreatic diseases are still a matter of controversy. Electrophoretic separation of the multiple forms of gamma GT on agarose gel was performed in 20 alcoholic patients (six with cirrhosis and 14 with fatty liver) and the results compared with those obtained in 50 healthy volunteers, 43 patients affected with chronic hepatitis C, 36 patients with posthepatitic cirrhosis and in 52 epileptic patients on long-term anti-epileptic medication. Multiple forms of gamma GT were separated into several bands (up to 11), labelled 0a, 0b, 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4a, 4b. In the alcoholic patients nine fractions were detected, and the electrophoretic pattern observed was significantly different from that observed in healthy volunteers and in patients with chronic hepatitis C or posthepatitic cirrhosis. No differences were observed in the electrophoretic patterns in the alcohol abusers and epileptic patients. In alcoholic patients significant differences were observed in the electrophoretic patterns in relation to the degree of liver injury; the electrophoretic patterns in patients with alcohol-related cirrhosis and posthepatitic cirrhosis differed significantly. The separation of multiple forms of gamma GT has high sensitivity and good reproducibility. It may be proposed as a complementary test in the diagnosis of alcoholic liver disease.