The utility of PET imaging in depression.

This educational review article aims to discuss growing evidence from PET studies in the diagnosis and treatment of depression. PET has been used in depression to explore the neurotransmitters involved, the alterations in neuroreceptors, non-neuroreceptor targets (e.g., microglia and astrocytes), the severity and duration of the disease, the pharmacodynamics of various antidepressants, and neurobiological mechanisms of non-pharmacological therapies like psychotherapy, electroconvulsive therapy, and deep brain stimulation therapy, by showing changes in brain metabolism and receptor and non-receptor targets. Studies have revealed alterations in neurotransmitter systems such as serotonin, dopamine, GABA, and glutamate, which are linked to the pathophysiology of depression. Overall, PET imaging has furthered the neurobiological understanding of depression. Despite these advancements, PET findings have not yet led to significant changes in evidence-based practices. Addressing the reasons behind inconsistencies in PET imaging results, conducting large sample size studies with a more standardized methodological approach, and investigating further the genetic and neurobiological aspects of depression may better leverage PET imaging in future studies.

Effectiveness of primary care interventions in conjointly treating comorbid chronic pain and depression: a systematic review and meta-analysis.

BACKGROUND: Chronic pain and depression are highly comorbid, but the lack of consensus on the best treatment strategies puts patients at high risk of suboptimal care-coordination as well as health and social complications. Therefore, this study aims to quantitatively assesses how effective different primary care interventions have been in treating the comorbid state of chronic pain and depression. In particular, this study evaluates both short-term outcomes-based specifically on measures of chronic pain and depression during an intervention itself-and long-term outcomes or measures of pain and depression in the months after conclusion of the formal study intervention. METHODS: This study is a systematic review and meta-analysis of randomised-controlled trials (RCTs) enrolling patients with concurrent chronic pain and depression. Intensity and severity of pain and depression symptoms were the primary outcomes. The main inclusion criteria were RCTs that: (i) enrolled patients diagnosed with depression and chronic pain, (ii) occurred in primary care settings, (iii) reported baseline and post-intervention outcomes for chronic pain and depression, (iv) lasted at least 8 weeks, and (v) used clinically validated outcome measures. Risk of bias was appraised with the Risk of Bias 2 tool, and GRADE guidelines were used to evaluate the quality of evidence. RESULTS: Of 692 screened citations, 7 multicomponent primary care interventions tested across 891 patients were included. Meta-analyses revealed significant improvements in depression at post-intervention (SMD = 0.44, 95% CI [0.17, 0.71], P = 0.0014) and follow-up (SMD = 0.41, 95% CI [0.01, 0.81], P = 0.0448). Non-significant effects were observed for chronic pain at post-intervention (SMD = 0.27, 95% CI [-0.08, 0.61], P = 0.1287) and follow-up (SMD = 0.13, 95% CI [-0.3, 0.56], P = 0.5432). CONCLUSIONS: Based on the results of the meta-analysis, primary care interventions largely yielded small to moderate positive effects for depressive symptoms and no significant effects on pain. In one study, stepped-care to be more effective in treatment of comorbid chronic pain and depression than other interventions both during the intervention and upon post-intervention follow-up. As such, depression appears more amenable to treatment than pain, but the number of published RCTs assessing both conditions is limited. More research is needed to further develop optimal treatment strategies.

Personal and clinical characteristics associated with immunotherapy effectiveness in stage IV non-small cell lung cancer.

Background: Immunotherapy response rates in metastatic non-small cell lung cancer (NSCLC) are low and survival varies significantly. Factors like age, sex, race, and histology may modulate immunotherapy response. Existing analyses are limited to clinical trials, with limited generalizability, and meta-analyses where adjustment for potential confounders cannot be performed. Here, we conduct a cohort study with patient-level analysis to explore how personal and clinical characteristics moderate chemoimmunotherapy effectiveness in metastatic NSCLC. Methods: Stage IV NSCLC patients diagnosed in 2015 were drawn from Surveillance Epidemiology, and End Results-Medicare linked data. Receipt of chemoimmunotherapy and overall survival (OS) were the primary predictor and outcome of interest respectively. Multivariable Cox-proportional hazards regression and propensity-score matching were performed to evaluate the effectiveness of immunotherapy addition to chemotherapy. Results: From a total of 1,471 patients, 349 (24%) received chemoimmunotherapy and 1,122 (76%) received chemotherapy alone. Survival was significantly better among those treated with chemoimmunotherapy compared to those receiving chemotherapy alone [adjusted hazard ratio (HRadj) =0.72, 95% confidence interval (CI): 0.63-0.83]. Males saw significantly better OS from chemoimmunotherapy (HRadj =0.62, 95% CI: 0.51-0.75) than females (HRadj =0.81, 95% CI: 0.65-1.01, Pinteraction=0.0557). After propensity-score matching, the effect of chemoimmunotherapy was borderline significant according to sex (Pinteraction =0.0414), but not age or histology. Conclusions: Males may benefit more from chemoimmunotherapy, but there is limited evidence suggesting age, histology, race, and comorbidities contribute to differences in effectiveness. Future research should elucidate who responds best to chemoimmunotherapy, and further analyses of characteristics like race can inform how to tailor different treatment regimens to distinct patient subpopulations.

Machine learning in the positron emission tomography imaging of Alzheimer's disease.

The utilization of machine learning techniques in medicine has exponentially increased over the last decades due to innovations in computer processing, algorithm development, and access to big data. Applications of machine learning techniques to neuroimaging specifically have unveiled various hidden interactions, structures, and mechanisms related to various neurological disorders. One application of interest is the imaging of Alzheimer's disease, the most common cause of progressive dementia. The diagnoses of Alzheimer's disease, mild cognitive impairment, and preclinical Alzheimer's disease have been difficult. Molecular imaging, particularly via PET scans, holds tremendous value in the imaging of Alzheimer's disease. To date, many novel algorithms have been developed with great success that leverage machine learning in the context of Alzheimer's disease. This review article provides an overview of the diverse applications of machine learning to PET imaging of Alzheimer's disease.

Stroke and molecular imaging: a focus on FDG-PET.

Stroke is the leading cause of disability worldwide, the second most common cause of dementia and the third leading cause of death. Though the etiology of stroke has been explored extensively, there remains open questions in the scientific and clinical study of stroke. Traditional imaging techniques, such as magnetic resonance imaging and computed tomography, have been applied extensively and remain mainstays in clinical practice. Nevertheless, positron emission tomography has proven to be a powerful molecular imaging tool in exploring the scientific aspects of neurological disease, and stroke remains an area of great interest. This review article examines the role of positron emission tomography in the study of stroke including its contributions to elaborating related pathophysiology and delving into possible clinical applications.

Aligned nanofibers of decellularized muscle extracellular matrix for volumetric muscle loss.

Volumetric muscle loss (VML) is a traumatic loss of muscle tissue that results in chronic functional impairment. When injured, skeletal muscle is capable of small-scale repair; however, regenerative capacities are lost with VML due to a critical loss stem cells and extracellular matrix (ECM). Consequences of VML include either long-term disability or delayed amputations of the affected limb. While the prevalence of VML is substantial, currently a successful clinical therapy has not been identified. In a previous study, an electrospun composed of polycaprolactone (PCL) and decellularized-ECM (D-ECM) supported satellite cell-mediated myogenic activity in vitro. In this study, we investigate the extent to which this electrospun scaffold can support functional muscle regeneration in a murine model of VML. Experimental groups included no treatment, pure PCL treated, and PCL:D-ECM (50:50 blend) treated VML defects. The PCL:D-ECM scaffold treated VML muscles supported increased activity of anti-inflammatory M2 macrophages (arginase+ ) at Day 28, compared to other experimental groups. Increased myofiber (MHC+ ) regeneration was observed histologically at both Days 7 and 28 post-trauma in blend scaffold treated group compared to PCL treated and untreated groups. However, improvements in muscle weights and force production were not observed. Future studies would evaluate muscle function at longer time-points post-VML injury to allow sufficient time for reinnervation of regenerated muscle fibers.

Aligned nanofibers of decellularized muscle ECM support myogenic activity in primary satellite cells in vitro.

Volumetric muscle loss (VML) is a loss of over ∼10% of muscle mass that results in functional impairment. Although skeletal muscle possesses the ability to repair and regenerate itself following minor injuries, VML injuries are irrecoverable. Currently, there are no successful clinical therapies for the treatment of VML. Previous studies have treated VML defects with decellularized extracellular matrix (D-ECM) scaffolds derived from either pig urinary bladder or small intestinal submucosa. These therapies were unsuccessful due to the poor mechanical stability of D-ECM leading to quick degradation in vivo. To circumvent these issues, in this manuscript aligned nanofibers of D-ECM were created using electrospinning that mimicked native muscle architecture and provided topographical cues to primary satellite cells. Additionally, combining D-ECM with polycaprolactone (PCL) improved the tensile mechanical properties of the electrospun scaffold. In vitro testing shows that the electrospun scaffold with aligned nanofibers of PCL and D-ECM supports satellite cell growth, myogenic protein expression, and myokine production.