Project description:Mitochondria have long been controversial organelles in cancer. Early discoveries in cancer metabolism placed much emphasis on cytosolic contributions. Initial debate focused on if mitochondria had a role in cancer formation and progression at all. More recently the contributions of mitochondria to cancer development and progression have become firmly established. This has led to the identification of novel targets and inhibitors being studied as new therapeutic approaches. This review will summarize the role of mitochondria in cancer and highlight several agents under development.
Project description:Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.
Project description:Chemistry is among the last of the core natural sciences to embrace preprints, namely, the publication of non peer-reviewed scientific articles on the Internet. After a brief insight into the origins and the purpose of preprints in science, we conducted a concrete analysis of the concrete situation, aiming at providing an answer to several questions. Why has the chemistry community been late in embracing preprints? Is this in relation with the slow acceptance of open-access publishing by the same community? Will preprints become a common habit also for chemistry scholars?
Project description:The ability to exploit the immune system as a weapon against cancer has revolutionised the treatment of cancer patients, especially through immune checkpoint inhibitors (ICIs). However, ICIs demonstrated a modest benefit in treating breast cancer (BC), with the exception of certain subsets of triple-negative BCs. An immune-suppressive tumour microenvironment (TME), typically present in BC, is an important factor in the poor response to immunotherapy. After almost two decades of poor clinical trial results, cancer vaccines (CVs), an active immunotherapy, have come back in the spotlight because of some technological advancements, ultimately boosted by coronavirus disease 2019 pandemic. In particular, neoantigens are emerging as the preferred targets for CVs, with gene-based and viral vector-based platforms in development. Moreover, lipid nanoparticles proved to be immunogenic and efficient delivery vehicles. Past clinical trials investigating CVs focused especially on the metastatic disease, where the TME is more likely compromised by inhibitory mechanisms. In this sense, favouring the use of CVs as monotherapy in premalignant or in the adjuvant setting and establishing combination treatments (i.e. CV plus ICI) in late-stage disease are promising strategies. This review provides a full overview of the past and current breast cancer vaccine landscape.
Project description:BackgroundA large proportion of admissions to psychiatric hospitals happen as emergency admissions and many of them occur out of core working hours (during the weekends, on public holidays and during night time). However, very little is known about what determines admission times and whether the information of admission time bears any relevance for the clinical course of the patients. In other words, do admission times correlate with diagnostic groups? Can accumulations of crises be detected regarding circadian or weekly rhythms? Can any differences between workdays and weekends/public holidays be detected? May it even be possible to use information on admission times as a predictor for clinical relevance and severity of the presented condition measured by the length of stay?MethodsIn the present manuscript we analyzed data derived from 37'705 admissions to the Psychiatric District Hospital of Regensburg located in the Southern part of Germany covering the years 2013 to 2018 with regard to ICD-10 diagnostic groups and admission times. The hospital provides 475 beds for in-patient treatment in all fields of clinical psychiatry including geriatrics and addiction medicine.ResultsSeveral core questions could be answered based on our analysis: 1st Our analysis confirms that there is a high percentage of unheralded admissions out of core time showing broad variation. 2nd In contrary to many psychiatrists' misconceptions the time of admission has no relevant impact on the length of stay in the hospital. 3rd The predictive value of admission time regarding the allocation to ICD-10 diagnostic groups is low explaining only 1% of variability.ConclusionsTaken together, our data reveal the enormous variation of admission times of psychiatric patients accounting for the need of adequate and consistent provision of personnel and spatial resources.
Project description:For patients with hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) or arrhythmogenic cardiomyopathy (ACM), screening for pathogenic variants has become standard clinical practice. Genetic cascade screening also allows the identification of relatives that carry the same mutation as the proband, but disease onset and severity in mutation carriers often remains uncertain. Early detection of disease onset may allow timely treatment before irreversible changes are present. Although plasma biomarkers may aid in the prediction of disease onset, monitoring relies predominantly on identifying early clinical symptoms, on imaging techniques like echocardiography (Echo) and cardiac magnetic resonance imaging (CMR), and on (ambulatory) electrocardiography (electrocardiograms (ECGs)). In contrast to most other cardiac diseases, which are explained by a combination of risk factors and comorbidities, genetic cardiomyopathies have a clear primary genetically defined cardiac background. Cardiomyopathy cohorts could therefore have excellent value in biomarker studies and in distinguishing biomarkers related to the primary cardiac disease from those related to extracardiac, secondary organ dysfunction. Despite this advantage, biomarker investigations in cardiomyopathies are still limited, most likely due to the limited number of carriers in the past. Here, we discuss not only the potential use of established plasma biomarkers, including natriuretic peptides and troponins, but also the use of novel biomarkers, such as cardiac autoantibodies in genetic cardiomyopathy, and discuss how we can gauge biomarker studies in cardiomyopathy cohorts for heart failure at large.
Project description:Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.
Project description:Acute myeloid leukemia (AML) is a complex disease characterized by genetic and clinical heterogeneity and high mortality. After 40 years during which the standard of care for patients evolved very little, the therapeutic landscape has recently seen rapid changes, with the approval of eight new drugs by the Food and Drug Administration (FDA) within the last 2 years, providing new opportunities, as well as new challenges, for treating clinicians. These therapies include FLT3 inhibitors midostaurin and gilteritinib, CPX-351 (liposomal cytarabine and daunorubicin), gemtuzumab ozogamicin (GO, anti-CD33 monoclonal antibody conjugated with calicheamicin), IDH1/IDH2 inhibitors ivosidenib and enasidenib, Hedgehog inhibitor glasdegib, and BCL-2 inhibitor venetoclax. In this review, we summarize currently available data on these new drugs and discuss the rapidly evolving therapeutic armamentarium for AML, focusing on targeted therapies.