Medicina narrativa enfocada a la investigación empírica social: el contexto ruso

Este artículo presenta los resultados de un análisis empírico llevado a cabo entre 2012 y 2016, que buscó entender si las narrativas de los pacientes están presentes en la comunicación médico-paciente y si esta historia subjetiva es significativa para ambos lados de la comunicación médica en la medicina somática rusa. La investigación se realizó en cuatro etapas y combinó métodos cualitativos y cuantitativos, analizando las perspectivas de pacientes, médicos y estudiantes de medicina a través de encuestas y entrevistas e indagando además en la comunicación médico-paciente en foros virtuales. En las cuatro etapas, los resultados de la investigación mostraron que se otorga poco valor a la experiencia subjetiva de la enfermedad en las interacciones entre médicos y pacientes. El tema de la medicina narrativa es inexplorado en los estudios sociales rusos, por lo que los resultados de esta investigación constituyen una contribución importante en pos de establecer la medicina narrativa como una idea global que promueve el valor universal en términos terapéuticos y éticos de las historias de enfermedad en la “sociedad de remisión”, en el cual dominan las patologías crónicas.This article contains the results of the empirical analysis carried out in 2012- 2016 which sought to examine whether patients’ narratives of their illness were present in doctor-patient communication and whether this subjective story was significant to both sides of the medical communication in Russian somatic disease medicine. The research was carried out in four stages and combined qualitative and quantitative methods, analyzing the perspectives of patients, doctors and medical students through surveys and interviews as well as looking at online doctor-patient communication in health forums. In all four stages, the results of the research showed that little value was placed on the subjective experience of disease in doctor-patient interactions. The topic of narrative medicine is new to Russian social studies, making the results of this research an important contribution to the establishment of narrative medicine as a global idea advocating the universal therapeutic and ethical value of patients’ stories of illness in the “remission society,” in which chronic pathologies dominate

Prevalence of PgR, ER and HER2+ receptors among women with breast cancer by age in Poland

Introduction. Prevalence of estrogen (ER), progesterone (PgR) and human epidermal growth factor receptors (HER2) vary by age among women with breast cancer (BC). Such variation has a large significance for the prognosis and treatment process. This study characterizes the prevalence of breast cancer subtypes by age in a hospital sample in Poland. Material and methods. The study population included 735 women with BC aged 27–91 years old (ICD-10: C50) and treated in the years 2009–2011 in the Military Institute of Medicine in Warsaw. Subjects were divided into 2 age categories — 27–44 and 45+ — which included 66 (9%) and 669 (91%) women respectively. In each group prevalence of PgR, ER and HER2+ receptors was investigated. Results. In both age groups the most frequent BC subtype was luminal A (hormone dependent BC — with PgR and ER receptor expression) — 27–44 years old — 44% and 45+ years old — 56%. The lowest number of cases in the age group 27–44 was luminal B (triple positive breast cancer) — about 15% of cases and for 45+ age group — HER2+ BC — about 11%. Discussion. Performed research shows relationship between growing age of diagnosis and presence of more desirable features of BC among women aged 55 and more, such as expression of steroid receptors or lack of expression of HER2 receptors, which is a good prognostic indicator for treatment outcomes. In the same time, many studies suggest that more aggressive types of breast cancer (basal-like — triple negative) are more characteristic for younger age groups (under 45 years old and younger women in perimenopausal age). Same results have been obtained in own study. Conclusions. A high incidence of BC in older age groups (45+) and more frequent occurrence of aggressive types of BC among younger women (27–44 years old) indicate the need to educate women from both age groups about risk factors and early symptoms of the disease. As we still have not recognized all BC risk factors, education about well-known agents, such as alcohol intake, overweight and obesity, play significant role in decrease of BC incidence and mortality

Produkty tytoniowe oparte na podgrzewaniu tytoniu (heat-not-burn) a zdrowie pacjentów — opinia grupy ekspertów

Smoking, especially of cigarettes, is one of the best-recognised and most widely studied risk factors for cardiovascular disease and death. Despite the availability of many methods of counteracting tobacco addiction, their effective- ness is still insufficient. An alternative to smoking cigarettes could be tobacco products based on heating tobacco (‘heat-not-burn’), the use of which may be associated with a lower cardiovascular risk. this article presents a summary of the current state of knowledge and the recommendations of Polish experts regarding the safety of these products.Nikotynizm jest jednym z uznanych i szeroko zbadanych czynników ryzyka chorób układu sercowo-naczyniowego oraz zgonu. Palenie papierosów stanowi również jeden z najistotniej- szych czynników ryzyka zachorowań oraz zgonu z powodu chorób serca i naczyń krwionośnych. Mimo dostępności wielu metod wspomagających leczenie uzależnienia od tytoniu ich skuteczność pozostaje niewystarczająca. Alternatywę dla palenia papierosów mogą stanowić produkty tytoniowe oparte na podgrzewaniu tytoniu, których stosowanie może się łączyć z niższym ryzykiem sercowo-naczyniowym. W niniejszym artykule przedstawiono podsumowanie aktualnego stanu wiedzy i zaleceń polskich ekspertów w zakresie bezpieczeństwa stosowania tych produktów

Long-term survival can be achieved in a significant fraction of older patients with core binding factor acute myeloid leukemia treated with intensive chemotherapy

Acute Myeloid Leukemia is mainly a disease of the elderly: however, the knowledge on the outcomes of treatment in core binding factor AML (CBFAML) in older population, is limited. We retrospectively collected data on 229 patients with CBF- AML followed long-term in the last two decades. A 5-year overall survival (OS) of 44.2% (95%CI, 39.9-47.5) and a 5-year event - free survival (EFS) of 32.9% (95%CI, 25.5-40.1) was observed. In a subgroup of >70-year patients who completed intensive therapy (induction + >3 courses of consolidation including autologous stem cell transplant: 10 patients) the median EFS was 11.8 months (95%CI, 9.4 - 15.2) and OS was 40.0% (95%CI, 36.4 - 44.1) at 5yr. In univariate analysis, age >70 (hazard ratio (HR) 1.78, [95%CI, 1.15 - 2.54], p=.008), failure to achieve remission following induction (HR, 8.96 [95%CI, 5.5 - 13.8], p=<.0001), no consolidation therapy (HR, 0.75 [95%CI, 0.47 - 1.84], p=.04) and less than 3 cycles of consolidation (HR, 1.48 [95%CI, 0.75 - 3.2], p=.0004), predicted poorer EFS. Our study shows that intensive therapy, in selected older CBF-AML patients, leads to longer survival. Achieving a CR seems to be the most important first step and at least 3 cycles of consolidation, an important second one. The analysis suggests that these patients should not be excluded from studies with intensive therapies

Poor mental health and sexual risk behaviours in Uganda: A cross-sectional population-based study

<p>Abstract</p> <p>Background</p> <p>Poor mental health predicts sexual risk behaviours in high-income countries, but little is known about this association in low-income settings in sub-Saharan Africa where HIV is prevalent. This study investigated whether depression, psychological distress and alcohol use are associated with sexual risk behaviours in young Ugandan adults.</p> <p>Method</p> <p>Household sampling was performed in two Ugandan districts, with 646 men and women aged 18-30 years recruited. Hopkins Symptoms Checklist-25 was used to assess the presence of depression and psychological distress. Alcohol use was assessed using a question about self-reported heavy-episodic drinking. Information on sexual risk behaviour was obtained concerning number of lifetime sexual partners, ongoing concurrent sexual relationships and condom use.</p> <p>Results</p> <p>Depression was associated with a greater number of lifetime partners and with having concurrent partners among women. Psychological distress was associated with a greater number of lifetime partners in both men and women and was marginally associated (p = 0.05) with having concurrent partners among women. Psychological distress was associated with inconsistent condom use among men. Alcohol use was associated with a greater number of lifetime partners and with having concurrent partners in both men and women, with particularly strong associations for both outcome measures found among women.</p> <p>Conclusion</p> <p>Poor mental health is associated with sexual risk behaviours in a low-income sub-Saharan African setting. HIV preventive interventions should consider including mental health and alcohol use reduction components into their intervention packages, in settings where depression, psychological distress and alcohol use are common.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

What can inactivity (in its various forms) reveal about affective states in non-human animals? A review

Captive/domestic animals are often described as inactive, with the implicit or explicit implication that this high level of inactivity is a welfare problem. Conversely, not being inactive enough may also indicate or cause poor welfare. In humans, too much inactivity can certainly be associated with either negative or positive affective states. In non-human animals, however, the affective states associated with elevated or suppressed levels of inactivity are still not well understood. Part of the complexity is due to the fact that there are many different forms of inactivity, each likely associated with very different affective states. This paper has two aims. One is to identify specific forms of inactivity that can be used as indicators of specific affective states in animals. The other is to identify issues that need to be resolved before we could validly use the remaining, not yet validated forms of inactivity as indicators of affective state. We briefly discuss how inactivity is defined and assessed in the literature, and then how inactivity in its various forms relates to affective (either negative or positive) states in animals, basing our reasoning on linguistic reports of affective states collected from humans displaying inactivity phenotypically similar to that displayed by animals in similar situations, and, when possible, on pharmacological validation. Specific forms of inactivity expressed in response to perceived threats (freezing, tonic immobility, and hiding) appear to be, to date, the best-validated indicators of specific affective states in animals. We also identify a number of specific forms of inactivity likely to reflect either negative (associated with ill-heath, boredom-like, and depression-like conditions), or positive states (e.g. ‘sun-basking’, post-consummatory inactivity), although further research is warranted before we could use those forms as indicators of the affective states. We further discuss the relationship between increased inactivity and affective states by presenting misleading situations likely to yield wrong conclusions. We conclude that more attention should be paid to inactivity in animal welfare studies: specific forms of inactivity identified in this paper are, or have the potential to be, useful indicators of affective (welfare) states in animals

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables