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Classical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice. Quantum Neural Networks (QNNs) have emerged as a promising alternative. This paper presents a comparative study between NNs and QNNs for heart disease prediction, addressing the limitations of current models in low-data regimes. We systematically evaluate 460 QNNs (using 11-13 qubits) and 4,480 NN architectures, analyzing key design parameters: encoding schemes, re-uploading strategies, circuit depth, and dropout (for QNNs), as well as hidden layers, neurons per layer, and dropout (for classical NNs). Top-performing models are selected for a direct comparison in terms of accuracy and sample complexity. Our results show QNNs achieve comparable accuracy and demonstrate potential advantages in data-scarce settings. Our study presents a structured and reproducible methodology for evaluating QNNs in clinical contexts, thereby supporting the broader investigation of quantum machine learning in applied healthcare domains.

Aims A multicentre trial, ICOS‐ONE, showed increases above the upper limit of normality of cardiac troponin (cTn) in 27% of patients within 12 months after the end of cancer chemotherapy (CT) with anthracyclines, whether cardiac protection with enalapril was started at study entry in all (prevention arm) or only upon first occurrence on supra‐normal cTn (troponin‐triggered arm). The aims of the present post hoc analysis were (i) to assess whether anthracycline‐based treatment could induce cardiotoxicity over 36 month follow‐up and (ii) to describe the time course of three cardiovascular biomarkers (i.e. troponin I cTnI‐Ultra, B‐type natriuretic peptide BNP, and pentraxin 3 PTX3) and of left ventricular (LV) function up to 36 months. Methods and results Eligible patients were those prescribed first‐in‐life CT, without evidence of cardiovascular disease, normal cTn, LV ejection fraction (EF) >50%, not on renin‐angiotensin aldosterone system antagonists. Patients underwent echocardiography and blood sampling at 24 and 36 months. No differences were observed in biomarker concentration between the two study arms, ‘prevention' vs. ‘troponin‐triggered'. During additional follow‐up 13 more deaths occurred, leading to a total of 23 (9.5%), all due to a non‐cardiovascular cause. No new occurrences of LV‐dysfunction were reported. Two additional patients were admitted to the hospital for cardiovascular causes, both for acute pulmonary embolism. No first onset of raised cTnI‐Ultra was reported in the extended follow‐up. BNP remained within normal range: at 36 months was 23.4 ng/L, higher (N.S.) than at baseline, 17.6 ng/L. PTX3 peaked at 5.2 ng/mL 1 month after CT and returned to baseline values thereafter. cTnI‐Ultra peaked at 26 ng/L 1 month after CT and returned to 3 ng/L until the last measurement at 36 months. All echocardiographic variables remained stable during follow‐up with a median LVEF of 63% and left atrial volume index about 24 mL/m2. Conclusions First‐in‐life CT with median cumulative dose of anthracyclines of 180 mg/m2 does not seem to cause clinically significant cardiac injury, as assessed by circulating biomarkers and echocardiography, in patients aged 51 years (median), without pre‐existing cardiac disease. This may suggest either a 100% efficacy of enalapril (given as preventive or troponin‐triggered) or a reassuringly low absolute cardiovascular risk in this cohort of patients, which may not require intensive cardiologic follow‐up.

Finding the solution to linear systems is at the heart of many applications in science and technology. Over the years a number of algorithms have been proposed to solve this problem on a digital quantum device, yet most of these are too demanding to be applied to the current noisy hardware. In this work, an original algorithmic procedure to solve the Quantum Linear System Problem (QLSP) is presented, which combines ideas from Variational Quantum Algorithms (VQA) and the framework of classical shadows. The result is the Shadow Quantum Linear Solver (SQLS), a quantum algorithm solving the QLSP avoiding the need for large controlled unitaries, requiring a number of qubits that is logarithmic in the system size. In particular, our heuristics show an exponential advantage of the SQLS in circuit execution per cost function evaluation when compared to other notorious variational approaches to solving linear systems of equations. We test the convergence of the SQLS on a number of linear systems, and results highlight how the theoretical bounds on the number of resources used by the SQLS are conservative. Finally, we apply this algorithm to a physical problem of practical relevance, by leveraging decomposition theorems from linear algebra to solve the discretized Laplace Equation in a 2D grid for the first time using a hybrid quantum algorithm.

This work introduces a novel NISQ-friendly procedure for estimating spectral gaps in quantum systems. By leveraging Adiabatic Thermalization, we are able to create the Spectral Gap Superposition state, a newly defined quantum state exhibiting observable fluctuations in time that allow for the accurate estimation of any energy gap. Our method is tested by estimating the energy gap between the ground and the first excited state for the 1D and 2D Ising model, the Hydrogen molecule H2 and Helium molecule He2. Despite limiting our circuit design to have at most 40 Trotter steps, our numerical experiments of both noiseless and noisy devices for the presented systems give relative errors in the order of \\(10^{-2}\\) and \\(10^{-1}\\). Further experiments on the IonQ Aria device lead to spectral gap estimations with a relative error of \\(10^{-2}\\) for a 4-site Ising chain, demonstrating the validity of the procedure for NISQ devices and charting a path towards a new way of calculating energy gaps.

Chemotherapy and cyclooxygenase inhibitors (COXi) are primary treatments for canine urethral transitional cell carcinoma (uTCC), a tumor known for its aggressiveness and poor prognosis. This retrospective study aimed to evaluate the clinico-pathological characteristics, treatment modalities, and prognostic factors of 35 dogs with confirmed uTCC that received chemotherapy and COXi. Upon admission, urethral obstruction (UO) and urinary tract infection (UTI) were observed in seven (20%) dogs each. Gemcitabine (n = 20; 57.1%) and vinblastine (n = 10; 28.6%) were commonly used as first-line therapies, with four dogs also receiving radiation therapy. Based on RECIST, one (2.9%) dog achieved complete remission, nine (25.7%) partial remission, 20 (57.14%) showed stable disease, and five (14.3%) progressed. Among dogs with UO, six (85.7%) showed resolution or improvement after the first chemotherapy dose. The median time to local progression was 171 days (range: 107–235), and the median survival time was 333 days (range: 158–508). Dogs with UO upon admission had a higher risk of local progression, while both UO and UTI were associated with an increased risk of overall disease progression and tumor-related death. Additionally, gemcitabine significantly improved metastatic control. This study identified UO and UTI as negative prognostic factors, highlighting the importance of a multimodal approach in managing uTCC.

Background Optimal cytoreduction (macroscopic Residual Tumor, RT = 0) is the best survival predictor factor in epithelial ovarian cancer (EOC). It doesn’t exist a consolidated criteria to predict optimal surgical resection at interval debulking surgery (IDS). The aim of this study is to develop a predictive model of complete cytoreduction at IDS. Methods We, retrospectively, analyzed 93 out of 432 patients, with advanced EOC, underwent neoadjuvant chemotherapy (NACT) and IDS from January 2010 to December 2016 in two referral cancer centers. The correlation between clinical-pathological variables and residual disease at IDS has been investigated with univariate and multivariate analysis. A predictive score of cytoreduction (PSC) has been created by combining all significant variables. The performance of each single variable and PSC has been reported and the correlation of all significant variables with progression free survival (PFS) has been assessed. Results At IDS, 65 patients (69,8%) had complete cytoreduction with no residual disease ( R  = 0). Three criteria independently predicted R  > 0: age ≥ 60 years ( p  = 0.014), CA-125 before NACT > 550 UI/dl ( p  = 0.044), and Peritoneal Cancer Index (PCI) > 16 ( p  < 0.001). A PSC ≥ 3 has been associated with a better accuracy (85,8%), limiting the number of incomplete surgeries to 16,5%. Moreover, a PCI > 16, a PSC ≥ 3 and the presence of R > 0 after IDS were all significantly associated with shorter PFS ( p  < 0.001, p  < 0.001 and p  = 0.004 respectively). Conclusions Our PSC predicts, in a large number of patients, complete cytoreduction at IDS, limiting the rate of futile extensive surgeries in case of presence of residual tumor (R > 0). The PSC should be prospectively validated in a larger series of EOC patients undergoing NACT-IDS.

Background: There is compelling need for novel biomarkers to predict response to PARP inhibitors (PARPi) in BRCA wild-type (WT) ovarian cancer (OC). Methods: MITO 37 is a multicenter retrospective study aiming at correlating Ki67 expression at diagnosis with a clinical outcome following platinum treatment and PARPi maintenance. Clinical data were collected from high grade serous or endometroid BRCAWT OC treated with niraparib or rucaparib maintenance between 2010–2021 in 15 centers. Ki67 expression was assessed locally by certified pathologists on formalin-fixed paraffin embedded (FFPE) tissues. Median Ki67 was used as a cut-off. Results: A total of 136 patients were eligible and included in the analysis. Median Ki67 was 45.7% (range 1.0–99.9). The best response to platinum according to median Ki67 was 26.5% vs. 39.7% complete response (CR), 69.1% vs. 58.8% partial response (PR), 4.4% vs. 1.5% stable disease (SD). The best response to PARPi according to median Ki67 was 19.1% vs. 36.8% CR, 26.5% vs. 26.5% PR, 26.5 vs. 25% SD, 27.9% vs. 16.2% progressive disease (PD). No statistically significant differences in progression free survival (PFS) and overall survival (OS) were identified between low and high Ki67. PFS and OS are in line with registration trials. Conclusions: Ki67 at diagnosis did not discriminate responders to PARPi.