Many-body correlations in a multistep variational approach

We discuss a multistep variational approach for the study of many-body correlations. The approach is developed in a boson formalism (bosons representing particle-hole excitations) and based on an iterative sequence of diagonalizations in subspaces of the full boson space. Purpose of these diagonalizations is that of searching for the best approximation of the ground state of the system. The procedure also leads us to define a set of excited states and, at the same time, of operators which generate these states as a result of their action on the ground state. We examine the cases in which these operators carry one-particle one-hole and up to two-particle two-hole excitations. We also explore the possibility of associating bosons to Tamm-Dancoff excitations and of describing the spectrum in terms of only a selected group of these. Tests within an exactly solvable three-level model are provided.Comment: 24 pages, 6 figures, to appear in Phys. Rev.

Hepatic PPARs: their role in liver physiology, fibrosis and treatment

Complex molecular and cellular mechanisms are involved in the pathway of liver fibrosis. Activation and transformation of hepatic stellate cells (HSCs) are considered the two main reasons for the cause and development of liver fibrosis. The peroxisome proliferator-activated receptors (PPARs) belonging to the family of ligand-activated transcription factors play a key role in liver homeostasis, regulating adipogenesis and inhibiting fibrogenesis in HSCs. Normal transcriptional function of PPARs contributes to maintain HSCs in quiescent phase. A reduced expression of PPARs in HSCs greatly induces a progression of liver fibrosis and an increased production of collagen. Here, we discuss role and function of PPARs and we take into consideration molecular factors able to reduce PPARs activity in HSCs. Finally, although further validations are needed, we illustrate novel strategies available from in vitro and animal studies on how some PPARs-agonists have been proved effective as antifibrotic substances in liver disease

Multipair approach to pairing in nuclei

The ground state of a general pairing Hamiltonian for a finite nuclear system is constructed as a product of collective, real, distinct pairs. These are determined sequentially via an iterative variational procedure that resorts to diagonalizations of the Hamiltonian in restricted model spaces. Different applications of the method are provided that include comparisons with exact and projected BCS results. The quantities that are examined are correlation energies, occupation numbers and pair transfer matrix elements. In a first application within the picket-fence model, the method is seen to generate the exact ground state for pairing strengths confined in a given range. Further applications of the method concern pairing in spherically symmetric mean fields and include simple exactly solvable models as well as some realistic calculations for middle-shell Sn isotopes. In the latter applications, two different ways of defining the pairs are examined: either with J=0 or with no well-defined angular momentum. The second choice reveals to be more effective leading, under some circumstances, to solutions that are basically exact.Comment: To appear in Physical Review

Beta-agonist stimulation ameliorates the phenotype of spinal and bulbar muscular atrophy mice and patient-derived myotubes

Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disease characterized by the loss of lower motor neurons. SBMA is caused by expansions of a polyglutamine tract in the gene coding for androgen receptor (AR). Expression of polyglutamine-expanded AR causes damage to motor neurons and skeletal muscle cells. Here we investigated the effect of β-agonist stimulation in SBMA myotube cells derived from mice and patients, and in knock-in mice. We show that treatment of myotubes expressing polyglutamine-expanded AR with the β-agonist clenbuterol increases their size. Clenbuterol activated the phosphatidylinositol-3-kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway and decreased the accumulation of polyglutamine-expanded AR. Treatment of SBMA knock-in mice with clenbuterol, which was started at disease onset, ameliorated motor function and extended survival. Clenbuterol improved muscle pathology, attenuated the glycolytic-to-oxidative metabolic alterations occurring in SBMA muscles and induced hypertrophy of both glycolytic and oxidative fibers. These results indicate that β-agonist stimulation is a novel therapeutic strategy for SBMA

Increased Functional Connectivity in the Default Mode Network in Mild Cognitive Impairment: A Maladaptive Compensatory Mechanism Associated with Poor Semantic Memory Performance

Semantic memory decline and changes of default mode network (DMN) connectivity have been reported in mild cognitive impairment (MCI). Only a few studies, however, have investigated the role of changes of activity in the DMN on semantic memory in this clinical condition. The present study aimed to investigate more extensively the relationship between semantic memory impairment and DMN intrinsic connectivity in MCI. Twenty-one MCI patients and 21 healthy elderly controls matched for demographic variables took part in this study. All participants underwent a comprehensive semantic battery including tasks of category fluency, visual naming and naming from definition for objects, actions and famous people, word-association for early and late acquired words and reading. A subgroup of the original sample (16 MCI patients and 20 healthy elderly controls) was also scanned with resting state functional magnetic resonance imaging and DMN connectivity was estimated using a seed-based approach. Compared with healthy elderly, patients showed an extensive semantic memory decline in category fluency, visual naming, naming from definition, words-association, and reading tasks. Patients presented increased DMN connectivity between the medial prefrontal regions and the posterior cingulate and between the posterior cingulate and the parahippocampus and anterior hippocampus. MCI patients also showed a significant negative correlation of medial prefrontal gyrus connectivity with parahippocampus and posterior hippocampus and visual naming performance. Our findings suggest that increasing DMN connectivity may contribute to semantic memory deficits in MCI, specifically in visual naming. Increased DMN connectivity with posterior cingulate and medio-temporal regions seems to represent a maladaptive reorganization of brain functions in MCI, which detrimentally contributes to cognitive impairment in this clinical population

Age-related alterations in default mode network: Impact on working memory performance

The default mode network (DMN) is a set of functionally connected brain regions which shows deactivation (task-induced deactivation, TID) during a cognitive task. Evidence shows an age-related decline in task-load-related modulation of the activity within the DMN during cognitive tasks. However, the effect of age on the functional coupling within the DMN and their relation to cognitive performance has hitherto been unexplored. Using functional magnetic resonance imaging, we investigated functional connectivity within the DMN in older and younger subjects during a working memory task with increasing task load. Older adults showed decreased connectivity and ability to suppress low frequency oscillations of the DMN. Additionally, the strength of the functional coupling of posterior cingulate (pCC) with medial prefrontal cortex (PFC) correlated positively with performance and was lower in older adults. pCC was also negatively coupled with task-related regions, namely the dorsolateral PFC and cingulate regions. Our results show that in addition to changes in canonical task-related brain regions, normal aging is also associated with alterations in the activity and connectivity of brain regions within the DMN. These changes may be a reflection of a deficit in cognitive control associated with advancing age that results in deficient resource allocation to the task at hand

New Frontiers in the Treatment of Patients with HER2+ Cancer and Brain Metastases: Is Radiotherapy Always Useful?

Brain metastases (BM) pose a significant challenge in the management of HER2+ breast cancer since almost 50% of patients with HER2+ breast cancer develop brain tumors. The complex process of brain metastases involves genetic mutations, adaptations and mechanisms to overcome the blood–brain barrier. While radiotherapy is still fundamental in local therapy, its use is associated with cognitive adverse effects and limited long-term control, necessitating the exploration of alternative treatments. Targeted therapies, including tyrosine kinase inhibitors, monoclonal antibodies, and antibody–drug conjugates, offer promising options for HER2+ breast cancer patients with BM. Clinical trials have demonstrated the efficacy of these agents in controlling tumor growth and improving patient outcomes, posing the question of whether radiotherapy is always the unique choice in treating this cancer. Ongoing research into novel anti-HER2 antibodies and innovative combination therapies holds promise for advancing treatment outcomes and enhancing patient care in this clinical scenario. This narrative review provides a comprehensive overview of traditional medical treatments, molecularly targeted therapy and investigational agents in the management of HER2+ breast cancer with BM, highlighting the evolving landscape and potential future directions in treatment strategies to improve patient survival and quality of life

Extension of random-phase approximation preserving energy weighted sum rules: an application to a 3-level Lipkin model

A limitation common to all extensions of random-phase approximation including only particle-hole configurations is that they violate to some extent the energy weighted sum rules. Considering one such extension, the improved RPA (IRPA), already used to study the electronic properties of metallic clusters, we show how it can be generalized in order to eliminate this drawback. This is achieved by enlarging the configuration space, including also elementary excitations corresponding to the annihilation of a particle (hole) and the creation of another particle (hole) on the correlated ground state. The approach is tested within a solvable 3-level model.Comment: 2 figure

Second- and Third-Line Chemotherapy in Advanced Gastric Cancer: a Real-Life Clinical Picture

n/

The Consistency and Quality of ChatGPT Responses Compared to Clinical Guidelines for Ovarian Cancer: A Delphi Approach

Introduction: In recent years, generative Artificial Intelligence models, such as ChatGPT, have increasingly been utilized in healthcare. Despite acknowledging the high potential of AI models in terms of quick access to sources and formulating responses to a clinical question, the results obtained using these models still require validation through comparison with established clinical guidelines. This study compares the responses of the AI model to eight clinical questions with the Italian Association of Medical Oncology (AIOM) guidelines for ovarian cancer. Materials and Methods: The authors used the Delphi method to evaluate responses from ChatGPT and the AIOM guidelines. An expert panel of healthcare professionals assessed responses based on clarity, consistency, comprehensiveness, usability, and quality using a five-point Likert scale. The GRADE methodology assessed the evidence quality and the recommendations’ strength. Results: A survey involving 14 physicians revealed that the AIOM guidelines consistently scored higher averages compared to the AI models, with a statistically significant difference. Post hoc tests showed that AIOM guidelines significantly differed from all AI models, with no significant difference among the AI models. Conclusions: While AI models can provide rapid responses, they must match established clinical guidelines regarding clarity, consistency, comprehensiveness, usability, and quality. These findings underscore the importance of relying on expert-developed guidelines in clinical decision-making and highlight potential areas for AI model improvement