Small cell size preserves the function of hematopoietic stem cells (HSCs); HSC enlargement during aging causes their dysfunction.

Chronic pain patients enter treatment with different problem profiles making careful assessment a necessity for more individualized treatment plans. In this cross-sectional study we assigned 320 patients entering tertiary multidisciplinary pain treatment into four categories based on whether they scored low or high on the activity and the affective pain interference dimensions of the Brief Pain Inventory (BPI). To determine whether this categorization system delineates issues that should be assessed further, the categories were compared with ANOVA and MANOVA analyses on three domains: variables affecting physical well-being (body mass index, exercise, substance use), psychological resources (mood), and pain-specific psychological factors (pain anxiety, pain acceptance). The results indicated that subjects who scored low on both interference dimensions compared similarly in weight: mean Body Mass Index (BMI) 27.0 (SD 6.0) kg/m 2 , and exercise: mean of 2.4 (SD 1.7) exercising sessions over 20 minutes per week, to the general population, had no depressive symptoms on average: mean Beck Depression Index II (BDI-II) score 11.7 (SD 7.5), and had the most favorable psychological reactions to pain relative to the other categories: mean total Pain Anxiety Symptoms Scale-20 (PASS-20) score 36.4 (SD 17.9). In contrast, when interference was high on activity, more physical well-being problems were evident e.g. weight: mean BMI 31.0 (SD 7.3) kg/m 2 , diminished exercise: mean of 1.5 (SD 1.6) exercising sessions per week, and avoidance behavior: mean PASS-20 Escape/Avoidance subscale 3.7 (95% CI: 1.7 to 5.8) scores higher in comparison to activity interference remaining low. With high affective interference, more depressive symptoms: mean BDI-II score 17.7 (SD 7.3), and more cognitive pain anxiety: mean PASS-20 Cognitive Anxiety subscale 2.8 (95% CI 0.7 to 4.8) scores higher in comparison to affective interference remaining low, emerged. Having high interference on both dimensions indicated accumulated risks for reduced physical well-being: mean BMI 29.9 (SD 6.1) kg/m 2 , mean of 1.2 (SD 1.7) exercising sessions per week, mood problems: mean BDI-II 20.3 (SD 10.6), and negative psychological reactions to pain: mean total PASS-20 score 53.2 (18.4). The results suggest that low interference on both dimensions may allow assessment with only physician consultations, while high interference on either dimension may call attention to distinct issues to be addressed with the help of a physiotherapist or a psychologist, whereas high interference on both dimensions highlights the need for a full multidisciplinary assessment.

Patients with chronic pain have complex pain profiles and associated problems. Subgroup analysis can help identify key problems. We used a data-based approach to define pain phenotypes and their most relevant associated problems in 320 patients undergoing tertiary pain management. Unsupervised machine learning analysis of parameters "pain intensity", "number of pain areas", "pain duration", "activity pain interference" and "affective pain interference", implemented as emergent selforganizing maps, identified three patient phenotype clusters. Supervised analyses, implemented as different types of decision rules, identified "affective pain interference" and the "number of pain areas" as most relevant for cluster assignment. These appeared 698 and 637 times, respectively, in 1000 cross-validation runs among the most relevant characteristics in an item categorization approach in a computed ABC analysis. Cluster assignment was achieved with a median balanced accuracy of 79.9%, a sensitivity of 74.1%, and a specificity of 87.7%. In addition, among 59 demographic, pain etiology, comorbidity, lifestyle, psychological, and treatment-related variables, sleep problems appeared 638 and 439 times among the most important characteristics in 1000 cross-validation runs where patients were assigned to the two extreme pain phenotype clusters. Also important were the parameters "fear of pain", "self-rated poor health", and "systolic blood pressure". Decision trees trained with this information assigned patients to the extreme pain-phenotype with an accuracy of 67%. Machine learning suggested sleep problems as key factors in the most difficult pain presentations, therefore deserving priority in the treatment of chronic pain.In this study, we used a data-driven approach in a cohort of patients with persistent pain of various causes to identify the most important attributes or problems specific to different pain phenotypes. In addition to pain phenotype-related factors, 59 further variables, representing factors recognized to be, and others possibly, associated with pain phenotypes were identified, including demographic factors, pain etiology, comorbidities, lifestyle factors, psychological variables, and treatment-related factors. These data were analyzed using unsupervised and supervised machine learning methods with the aim of (i) identifying and interpreting patterns arising from different pain phenotypic parameters and (ii) selecting among the further parameters those that were informative in associating a patient with a particular pain phenotype. Methods Study setting and designThis was an observational cohort study in chronic pain patients treated in three multidisciplinary tertiary pain clinics in Finland. A multi-center "KROKIETA" study was used to collect patient data on a broad scale, including socioeconomic factors, lifestyle factors, psychological variables, previous treatments, and biochemical indicators [98]. The study adheres to the STROBE guidelines