Pediatric Neurocritical Care diagnoses account for a large proportion of intensive care admissions. Critical care survivors suffer high rates of long-term morbidity, including physical disability, cognitive impairment, and psychosocial dysfunction. To address these morbidities in Pediatric Neurocritical Care survivors, collaboration between Pediatric Neurology and Pediatric Critical Care created a multidisciplinary follow-up clinic providing specialized evaluations after discharge. Clinic referrals apply to all Pediatric Neurocritical Care patients regardless of admission severity of illness. Here, we report an initial case series, which revealed a population that is heterogenous in age, ranging from 1 month to 18 years, and in diagnoses. Traumatic brain injuries of varying severity as well as neuroinfectious and inflammatory diseases accounted for the majority of referrals. Most patients (87%) seen in the clinic had morbidities identified, requiring ongoing evaluation and expansion of the clinic. Cognitive and psychological disturbance were seen in over half of patients at the initial clinic follow-up. Sleep disturbances, daytime fatigue, headache or chronic pain, and vision or hearing concerns were also common at initial follow-up. Data from this initial population of clinic patients reiterates the need for specialized follow-up care, but also highlights the difficulties related to providing this comprehensive care and evaluating interventions to improve outcomes. Critical neurologic disease and injury, including infectious and inflammatory disease, traumatic brain injury (TBI), stroke, hypoxic-ischemic injury following cardiopulmonary arrest, and seizure, affect thousands of children annually and account for 20% of Pediatric Intensive Care Unit (PICU) admissions . These cChronic deficits have been identified in survivors of PNCC, particularly in TBI and stroke patients. Declines in functional ability are seen in a majority of severe TBI survivors , and newFollowing PNCC, many children do not receive specialized follow-up for the long-term effects of critical illness, and research has been limited for determining effective interventions to improve outcomes. Despite receiving multidisciplinary care while an inpatient, few primary care or subspecialty providers receive training regarding PICS and most clinic environments lack the resources to identify all of these morbidities. In response to the care gap faced by PNCC survivors, we created a multidisciplinary PNCC follow-up clinic capable of assessing and addressing the multitude of morbidities faced by survivors. In this paper, we present a case series of the initial clinic experience, highlighting the heterogenous population characteristics, need for ongoing care, and future directions. Through collaboration between Pediatric Neurology and Pediatric Critical Care, we created a PNCC follow-up clinic, which included automatic referrals for qualified diagnoses, standardized outcome measures for longitudinal assessments, and neuropsychology services. We planned to start clinic referrals six months in advance, allowing for the creation of a half-day per month specialty clinic with our pediatric neurologist that could accommodate all referrals within two months of discharge and allow for follow-up appointments as needed. The goals of the clinic were: (1) to identify neurologic deficits and morbidities including PICS in survivors; (2) to facilitate access to therapeutic interventions for identified deficits; (3) to assist patients, families, and primary care providers with obtaining community, school, and healthcare resources. In this paper, we present a case series of PNCC patients admitted between August 2016 and January 2017 to the PICU at Doernbecher Children’s Hospital  a tertiary children’s hospital with an average of 1300 PICU admissions per year. Data presented in this paper were ascertained by a retrospective chart review of patients admitted to the PICU with a primary neurocritical care diagnosis. Morbidities reported here were ascertained from clinician documentation during the patient’s initial visit in the Pediatric Neurocritical Care follow-up clinic. This study was approved by the Oregon Health and Science University Institutional Review Board and granted waiver of informed consent (IRB No. 16173).All PNCC survivors with TBI, neuroinfectious or inflammatory diseases, and hemorrhagic or ischemic stroke admitted to the PICU were automatically referred by a member of the PICU staff following daily census review. Survivors of status epilepticus without another established neurologist and requiring critical care for >24 h were also referred. Other patients received referrals at the discretion of attending providers. TBI is defined as a skull fracture or intracranial blood, and severity is classified based on initial hospital Glasgow Coma Scale. All patients in this study with mild TBI were diagnosed with complicated mild TBI having a skull fracture or intracranial hematoma requiring PICU admission. Patient referrals included in-person discussion with families by PICU or neurology staff regarding clinic services and potential morbidities. Referrals to the clinic were independent of inpatient Pediatric Neurology consults, but based on whether deficits were identified during hospitalization, and severity of illness. Prior to the implementation of this clinic, follow-up was variable and often based on the severity of identified symptoms during hospitalization. Some patients qualified for Pediatric Physiatry consults and inpatient rehabilitation, but many others only received follow-up through surgical subspecialties or local primary care providers. The PNCC clinic provides a standardized process to treat identified deficits and screen for subtle morbidities that arise or persist after discharge for all neurocritical care survivors. Neuropsychology evaluations were conducted while inpatient for children ≥3 years of age with the above diagnoses who are cognitively able to interact with the examiner. Children must be able to follow basic instructions and demonstrate basic communication skills  in order to receive formal testing. Inpatient evaluations included detailed assessment of premorbid status (through parent interview or school-based assessments when available) and assessment of current deficits prior to discharge. Inpatient evaluations included counselling on identified deficits, potential morbidities, and resources for families to consult for community and school re-entry. Follow-up evaluations were completed as needed in paired clinic visits with Pediatric Neurology at intervals of four to six months. Outcomes in the clinic were assessed by parent and patient report, detailed examination by a pediatric neurologist, and standardized outcome measures. As no single measure can be used to screen for all morbidities associated with PICS, a combination of measures was used. Functional Status Scale (FSS)  scores wSixty-two patients, aged 1 month to 18 years, were admitted with a qualifying diagnosis from August through January to the PICU. Forty-six (74%) patients were referred . Seven pn = 7) and moderate (n = 2) TBI patients with short admissions, mostly over weekends when staff was not available. Two of these patients were accommodated at the initial clinic visit approximately one month after discharge. During the initial referral period, 34 patients met the criteria for inpatient Neuropsychology evaluations . Twenty-n = 14, 45%) required assistance with school accommodations and accessing services through school systems. Given the high prevalence of morbidities identified at the initial follow-up visit requiring ongoing care, the clinic was quickly expanded to two half-days per month to accommodate patients. Subsequently, the clinic has grown to four half-days per month with two neurology providers and will be adding a third provider from Critical Care to accommodate the increasing patient load. Four patients, all with mild TBI, were discharged from clinic after the initial visit, while the remaining 27 (87%) clinic patients had identified needs for ongoing care. TBI accounted for the majority of our PNCC patients. Complicated mild TBI patients seen in our PNCC clinic had high rates of deficits identified on initial follow-up evaluations, with 64% requiring ongoing clinic evaluations. These rates exceed symptom rates reported in concussion studies ,22, and All of the infectious and inflammatory patients seen in clinic had morbidities identified, particularly on neuropsychology testing. Limited reports in meningitis and encephalitis patients have shown similar morbidities ,24, but Cognitive morbidities and mood disturbances were common among PNCC patients in the initial clinic visit. These symptoms, as well as headaches and balance disturbances, are known consequences of TBI , but werSystematic inpatient neuropsychology evaluations are novel in PNCC patients, but important in order to document a premorbid state for the avoidance of the “good old days bias” when seeing patients in outpatient follow-up . This prIn the future, we anticipate a further expansion of clinic access to accommodate patients with ongoing morbidities by adding neurology and critical care providers and expanding services to include physical, occupational, and speech therapy services. We are also adding neuropsychology providers to expand inpatient and outpatient access to care. The clinic was designed to assess outcomes through serial provider evaluation and longitudinal collection of standardized outcome measures, including FSS , PedsQL The post-discharge care of PNCC survivors is variable, often leaving vulnerable patients with gaps in care. We created a PNCC follow-up clinic through collaboration between Pediatric Neurology and Pediatric Critical Care to fill this gap at our institution. Neurology and neuropsychology providers target the identification of neurologic deficits and the morbidities associated with PICS, working together to address physical, emotional, cognitive, and psychosocial concerns. Initial experience with clinic referrals reiterates the need for this specialized follow-up care, but also highlights the difficulties related to providing this comprehensive care due to the heterogenous population and multitude of morbidities identified.


Despite progress in early detection and therapeutic strategies, breast cancer remains the second leading cause of cancer-related death among women globally. Due to the heterogeneity and complexity of tumor biology, breast cancer patients with similar diagnosis might have different prognosis and response to treatment. Thus, deeper understanding of individual tumor properties is necessary. Cancer cells must be able to convert nutrients to biomass while maintaining energy production, which requires reprogramming of central metabolic processes in the cells. This phenomenon is increasingly recognized as a potential target for treatment, but also as a source for biomarkers that can be used for prognosis, risk stratification and therapy monitoring. Magnetic resonance (MR) metabolomics is a widely used approach in translational research, aiming to identify clinically relevant metabolic biomarkers or generate novel understanding of the molecular biology in tumors. Ex vivo proton high-resolution magic angle spinning (HR MAS) MR spectroscopy is widely used to study central metabolic processes in a non-destructive manner. Here we review the current status for HR MAS MR spectroscopy findings in breast cancer in relation to glucose, amino acid and choline metabolism. Breast cancer is by far the most common cancer in women worldwide . In NorwBreast cancer heterogeneity is manifested at different molecular, i.e., omics levels . TranscrAt the proteomic level, differences in the expression of estrogen and progesterone receptors , human epidermal growth factor receptor 2 (HER2) and the nuclear protein Ki-67 play an important role in current breast cancer clinical decision-making in terms of prognosis and optimal treatment plan. ER and PgR are transcription factors that activate important proliferation processes and production of growth factors upon binding with their ligands  . Since EBreast tumors not expressing ER, PgR and HER2 are classified as triple negative breast cancer (TNBC). This breast cancer subtype is biologically aggressive, is associated with a poor prognosis and is considered a clinically, histologically and molecularly heterogeneous disease ,13. For Although research has contributed to the discovery of several subgroups of breast cancer as stated above, there are still numerous unresolved aspects of breast cancer heterogeneity leading to inefficient treatment of patients . Better As metabolites are downstream of genes, RNA and proteins, metabolite levels can be considered the ultimate response to genetic modifications or environmental changes including disease, drug and dietary influences. Metabolomics therefore studies information closer to the observable biological endpoints, i.e., the phenotype, when compared to the other omics levels. Depending on ongoing cellular processes and on external factors, the metabolic profile will change dynamically. By examining the metabolic portraits of breast cancer tissue, one can get a better understanding of complex biological interactions or find new biomarkers related to specific subgroups of this disease . The twoMR spectroscopy of solids and semi-solid material , is characterized by anisotropic interactions between nuclei, that arise due to the reduced mobility of molecules within these samples. This gives rise to broad peaks, possibly concealing relevant spectral information. The same does not occur in liquid solution state, as the rapid movement of the molecules averages out the anisotropic interactions. Rapid spinning of a solid sample on its axis at an angle of 54.7° with respect to the external magnetic field, referred to as the magic angle, mimics a liquid solution state in which the anisotropy of the interactions is averaged to zero . This meEx vivo HR MAS MR spectroscopy is widely used to study central metabolic processes related to cancer progression. It gives qualitative and quantitative metabolic information from biological tissue with minimal sample preparation. MS analysis, on the other hand, requires sample pretreatment, e.g., derivatization and extraction, which can affect the metabolic profile. The metabolites detected in intact, fresh frozen tissue using HR MAS MR spectroscopy can be considered less likely to be a product of changes other than the innate biological processes of the tumor. A study showed that a delay of freezing breast tumor tissue samples of up to 30 min after resection did not significantly change the levels of individual metabolites measured by HR MAS MR spectroscopy . In addiOne of the challenges when analyzing intact breast tissue using HR MAS MR spectroscopy is that tumor biopsies obtained from breast cancer patients might contain fractions of adipose tissue. Signals arising from adipose tissue cannot be separated from tumor cell lipids. Furthermore, the aliphatic side chains of fatty acids within this tissue can give rise to large and broad peaks in MR spectra, potentially overlapping with and influencing signals from important small metabolites. This effect can be limited by using an appropriate MR pulse sequence, e.g., the Carr-Purcell-Meiboom-Gill (CPMG) sequence, which filters out signals from larger molecules such as lipids. However, absolute quantification using CPMG spectra can be misleading unless proper action is taken, as metabolite peak intensities can differ slightly due to variations in T2 relaxation times . In ordeAn important limitation of MR spectroscopy is its relatively low sensitivity (micromolar range compared to picomolar range for some MS-based methods), and thus the relatively few detectable metabolites. Using proton MR spectroscopy in vivo, only a total choline (tCho) peak can be detected. Yoon et al. however, showed that metabolites measured ex vivo using HR MAS MR spectroscopy are correlated with in vivo imaging parameters obtained using MR imaging and positron emission tomography (PET)-computerized tomography (CT) . AnalyziGlucose is the major source for energy and carbon within mammalian cells and has three major metabolic fates: glycogen synthesis, glycolysis or the pentose phosphate pathway (PPP) . Glycoge2 as a byproduct, as well as NADH and the reduced form of flavin adenine dinucleotide (FADH2). The NADH and FADH2 produced during glycolysis and the TCA cycle are high-energy electron carriers that enter oxidative phosphorylation, also known as the electron transport chain. During oxidation of NADH and FADH2, electrons are transferred from these coenzymes to oxygen and free energy is released. Glycolysis, the TCA cycle and oxidative phosphorylation yield two, two and thirty-two molecules of ATP, respectively  . Pyruvatectively , and tog18Fluorodeoxyglucose (FdG) to image and quantify glucose uptake, which has been found to be correlated with poor prognosis and to be highly dependent on the glycolytic rate [When little or no oxygen is available to oxidize pyruvate and NADH produced during glycolysis, pyruvate is quickly reduced to lactate via the action of lactate dehydrogenase (LDH). The oxidized form of NAD produced through this reaction fuels glycolysis creating a positive feedback loop . Althougtic rate ,35,36.The PPP, found to be elevated in cancer , is a maA general hypothesis when analyzing glucose metabolism in tumor tissue is that decreasing levels of glucose reflects an increasing energy demand while the degree of lactate production might indicate whether the glucose is guided towards TCA cycle or used for aerobic glycolysis. The decrease in glucose has also been observed by proton HR MR-obtained spectra using tissue extracts from breast cancer . In accoAlthough metabolic prediction of treatment response prior to onset of neoadjuvant treatment has currently not been achieved ,44,45, iThe characteristic of high glycolytic activity is being tested as a target in cancer therapy using different approaches . Direct Although more than 500 different amino acids exist, only 20 commonly serve as building blocks for proteins in the human body . Amino a2, pyruvate and NADPH, the latter being a reducing equivalent and as such is important for lipid and nucleotide synthesis. Through this pathway, increased glutaminolysis in proliferative cells can fulfill an important proportion of their NADPH demands [Although glucose is considered the main energy source in human cells, amino acids such as glutamine can be utilized to produce ATP through refilling of intermediates to the TCA cycle. Glutamine is normally considered a non-essential amino acid, however studies have shown that in rapidly dividing cells, including both normal and cancer cells, it is conditionally essential . Glutami demands ,61. An oIncreased glutamine transporter activity and GLS expression have been found in several cancers ,61. In bGlutathione, a tripeptide of glutamate, cysteine and glycine, is a major cellular antioxidant , and as Taurine is another amino acid whose relevance in cancer has been investigated. However, findings for this metabolite vary with different types of cancer , and theTaurine levels have also shown potential for monitoring treatment response, where breast cancer patients with a clinical response to neoadjuvant doxorubicin treatment had a larger decrease of taurine compared to non-responders . A similGlycine is a small, non-essential amino acid with important metabolic functions in the human body. Glycine can be derived from glycolysis through its precursor serine from 3-phosphoglycerate, or from choline by oxidation of choline to betaine which is further converted to sarcosine and finally glycine. Mitochondrial serine hydroxymethyltransferase 2 (SHMT2), the primary catalyst for converting serine to glycine, is shown to be one of the most frequently over-expressed metabolic enzymes in human tumors .Glycine has been found to be tumor size-dependent, where tumors larger than 2 cm had significantly higher concentrations of glycine as well as choline compared to smaller tumors . AdditioCholine is an essential organic compound functioning as a precursor for phosphatidylcholine (PtdCho), one of the most abundant phospholipids in eukaryotic cellular membranes . PtdCho Choline-containing compounds are considered essential to sustain cell proliferation in terms of being substrates for membrane multiplication for new cells ,79. TumoCholine containing metabolite levels have been found to correlate with enzymes involved in choline phospholipid metabolism. Increased tCho and PCho in cancer has been attributed to a higher choline uptake ,82, as wCholine phospholipid metabolism has been investigated as a target for antitumor treatment, with CK-α inhibition showing antiproliferative effects in both cancer cell lines and xenograft models of human tumors . PromisiNevertheless, many of the mechanisms governing altered choline phospholipid metabolism in cancer are still not fully understood. Malignant transformation, rather than rapid proliferation, has been suggested as the main driver for abnormal choline metabolism, as non-cancer proliferating cells were found to sustain low PCho, GPC and tCho levels in cell culture . In the Changes in choline-containing compounds during neoadjuvant chemotherapy have also been detected ex vivo. Cao et al.  reportedCholine phospholipid metabolism seems to be a factor contributing to breast cancer heterogeneity determined at different molecular levels. Regarding the protein level, ER+ tumors have been associated with higher PCho and lower GPC and choline levels compared to ER- tumors . In the Distinct differences in choline metabolite profiles have been observed between luminal-like and basal-like xenografts. Moestue et al.  found thDifferences in how these choline-containing compounds are affected by treatment have also been observed between basal-like and luminal-like xenografts. Moestue et al.  found thMetabolic profiles of breast tumors can be generated from intact tissue using proton HR MAS MR spectroscopy. This technique has been applied to study metabolites involved in the cancer-relevant pathways of glucose metabolism, amino acid metabolism and choline phospholipid metabolism. This review has provided a description of the said pathways as well as an overview of the main metabolic findings within them concerning breast cancer, focusing on proton MR detectable metabolites in intact tumor tissue, supported by in vitro and MS findings. HR MAS MR spectroscopy has proved to be a valuable tool in the characterization of breast cancer, with an important benefit being its translational potential to the in vivo setting. Since metabolites may serve as phenotypic markers resulting from both genome and proteome alterations, MR metabolomics can potentially be used to provide important predictive and prognostic information. Future studies combining metabolic profiles with data from other platforms could potentially lead to a more refined stratification of breast cancer patients and improved treatment strategies targeting metabolic pathways.


The crisis of prescription opioid (PO) related harms has focused attention toward identifying and treating high-risk populations. This review aims to synthesize systematic reviews on the epidemiology and clinical management of comorbid chronic pain and PO or other substance misuse.A systematic database search was conducted to identify systematic reviews published between 2000 and 2016. Eligible studies were systematic reviews related to chronic non-cancer pain and PO or other substance misuse. Evidence from the included reviews was synthesized according to epidemiology and clinical management themes.Of 1908 identified articles, 18 systematic reviews were eligible for final inclusion. Two meta-analyses estimated the prevalence of chronic non-cancer pain in individuals using POs non-medically to be approximately 48% to 60%, which is substantially higher than the prevalence of chronic non-cancer pain in general population samples (11% to 19%). Five systematic reviews estimated the rates of PO or other opioid use in chronic pain populations with substantial variation in results (0.05% to 81%), likely due to widely varying definitions of dependence, substance use disorder, misuse, addiction, and abuse. Several clinical assessment and treatment approaches were identified, including: standardized assessment instruments; urine drug testing; medication counts; prescription drug monitoring programs; blood level monitoring; treatment agreements; opioid selection; dosing and dispensing strategies; and opioid agonist treatment. However, the reviews commonly noted serious limitations, inconsistencies, and imprecision of studies, and a lack of evidence on effectiveness or clinical utility for the majority of these strategies.Overall, current systematic reviews have found a lack of high-quality evidence or consistent findings on the prevalence, risk factors, and optimal clinical assessment and treatment approaches related to concurrent chronic pain and substance misuse. Given the role of systematic reviews in guiding evidence-based medicine and health policy, there is an urgent need for high-quality primary research to guide future systematic reviews to address the escalating epidemic of harms related to chronic pain and substance misuse.The online version of this article (doi:10.1186/s13011-017-0120-7) contains supplementary material, which is available to authorized users. Across North America, the devastating crisis of prescription opioid (PO) related addiction and overdose has led to escalating mortality rates that have surpassed national mortality rates due to motor vehicle accidents and HIV-related mortality , 2. As sThe current body of literature on concurrent chronic pain and opioid misuse presents a range of conflicting research on the prevalence, risk factors, and clinical management approaches specific to this growing sub-population. A number of systematic reviews have attempted to synthesize various aspects of the epidemiology or clinical management of comorbid chronic pain and opioid misuse. Given that systematic reviews are the foundation for evidence-based clinical and policy decision-making , this ‘rFollowing the PRISMA guideline , we searTwo reviewers independently screened the search results to identify eligible systematic reviews. Reviews were eligible for inclusion if they were: peer-reviewed; systematic reviews related to CNCP and PO or other illicit opioid misuse; focused on adult populations; and published in English. Studies were excluded if they were: non-systematic reviews; reviews of non-primary research ; specific to acute or specialized pain ; specific to non-adult or specialized settings  or populations ; or focused on evaluating a specific analgesic brand.Data was extracted and summarized for the following parameters: author name, publication year, journal name, review period, databases used, theme of the review , number of included studies, studied population, and summary of findings. Evidence synthesis was conducted using a narrative approach based on thematic findings.The quality of the systematic reviews was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR) validated scale , 9.From a total of 1908 unique hits, 1870 were removed at the title and abstract screening phase and 38 full-text papers were screened  from four general U.S. population samples reporting non-medical PO use Table , althougLusted et al. found a 58% pooled prevalence of pain (95% CI: 53%–64%) from eight samples of adult patients in substance use treatment Table  15]. Am. Am15]. Minozzi et al.  found a Morasco et al. was the only review that estimated rates of substance use disorder. Across 21 studies of CNCP patients, the authors found varying prevalence estimates for substance use disorder (not restricted to opioids), ranging from 3% to 48% for current substance use disorders, and 15% to 74% for lifetime history of substance use disorder  as well as poorer physical, personal, and social functioning compared to individuals with opioid use disorder who did not have pain [Morasco et al. found mixed and inconclusive data on relationship between psychiatric comorbidity and substance use disorder in CNCP patients . Turk etave pain .Several of the reviews found that CNCP patients with a past or present history of opioid or other substance use disorder appear to be at greater risk for PO misuse , 28, 29.Morasco et al. found that CNCP patients with comorbid substance use disorder were more likely to be prescribed opioids—and at higher doses—than CNCP patients without substance use disorder . The autA number of standardized instruments have been developed to predict or identify opioid misuse in chronic pain patients Table . Four reMost importantly, the feasibility, acceptability, and clinical impact of these instruments remain uncertain. First, the instruments may be too lengthy to realistically administer in most clinical settings , 31. SecAs urine drug testing (UDT) is the most objective method of assessing opioid misuse in chronic pain patients, it is often considered a “gold standard” monitoring approach , 29. HowThere are a variety of other assessment and monitoring strategies intended to mitigate the harms of opioid misuse in chronic pain populations. Additional strategies include medication counts, which involve visual verification to ensure that a patient’s remaining medication supply matches the expected amount remaining on their prescription; prescription drug monitoring programs, which utilize centralized databases to track the opioid prescription history of patients and prescribers; or blood level monitoring to identify opioid misuse, similar to urine drug testing. However, none of the systematic reviews found sufficient reliable data to evaluate the effectiveness of these strategies , 28, 31.Treatment agreements—also referred to as treatment contracts or controlled substance agreements—are written documents with stipulations to which the patient formally agrees to prior to initiating opioid treatment. The specific set of terms in a treatment agreement is highly variable, with no strong evidence to support the inclusion or exclusion of particular clauses . Often, The review by Argoff et al. highlighted the need for careful attention when prescribing opioids for pain management in individuals with past or present substance misuse . The autRegarding immediate-release formulations, Argoff et al. suggests these may be preferable to extended-release formulations, as there are greater amounts of opioid contained in extended-release formulations that may be associated with higher abuse potential, and there is a paucity of evidence to suggest that long-acting formulations provide superior benefit compared to short-acting formulations . HoweverTamper-deterrent formulations are another strategy designed to deter abuse by way of their physical design  or ingredients . Argoff et al. found some evidence to suggested such formulations can be effective in reducing abuse and tampering, yet other evidence suggests that these formulations are ineffective in that individuals may simply seek other opioids that do not have abuse-deterring properties .One review found weak evidence to suggest that titrating opioids using slow and small dose increases may prevent excessively high doses and promote clinical monitoring of patient responses or potential aberrant behaviors . HoweverFor individuals with CNCP who have or are at risk of opioid addiction, Eilender et al. and other recent studies have found evidence to support the effectiveness of opioid agonist treatment via buprenorphine/naloxone or methadone , 37. BupEilender et al. found a small body of evidence suggesting that psychotherapeutic interventions for concurrent pain and substance misuse using mindfulness or cognitive behavioral therapy approaches may reduce pain severity and opioid misuse . NotablyThis review has synthesized the findings from 18 systematic reviews related to the epidemiology and clinical management of concurrent CNCP and opioid misuse.Two meta-analyses estimated the prevalence of CNCP in individuals using POs non-medically to be approximately 48% to 60%, which is substantially higher than the prevalence of chronic pain in general population samples in the United States (11%) and Canada (19%) , 40. SixThere are mixed and inconsistent findings regarding demographic factors that may predict opioid misuse in chronic pain populations, perhaps due to inconsistent reporting and underrepresentation of minority demographics , 25, 26.A number of assessment and monitoring strategies exist to assess for problematic opioid use in CNCP populations, including standardized assessment instruments, urine drug testing, medication counts, prescription drug monitoring programs, and blood level monitoring , 30–32. In terms of other treatment and intervention approaches, there is evidence to support opioid agonist treatment for individuals with chronic pain at risk for problematic drug use, particularly buprenorphine/naloxone . HoweverThis review has a number of limitations, including publication bias and varying criteria for inclusion, exclusion and outcome measures across reviews. Additionally, this review excludes findings from primary studies that may have been published after these reviews; however, the intent of this review is to summarize existing syntheses and highlight areas for future systematic review. Nonetheless, to our knowledge, this is the first ‘review of reviews’ to summarize the range of synthesized evidence on various epidemiologic and clinical aspects of concurrent chronic pain and opioid misuse.In summary, the purpose of this review is to provide a synthesis of systematic reviews related to concurrent chronic pain and opioid misuse, given that this is a sub-population at very high risk for morbidity and mortality, and given that systematic reviews are the basis for evidence-based clinical and policy decision-making. Overall, there is a lack of high-quality evidence or consistent findings on the risk factors for concurrent chronic pain and opioid misuse, as well as clinical approaches to effectively reduce drug-related harms in this population. While updated clinical guidelines have recently been released in an attempt to mitigate the ongoing opioid crisis , unfortu


Skeletal muscle metabolic and contractile properties are reliant on muscle mitochondrial and myofibrillar protein turnover. The turnover of these specific protein pools is compromised during disease, aging, and inactivity. Oppositely, exercise can accentuate muscle protein turnover, thereby counteracting decay in muscle function. According to a traditional consensus, endurance exercise is required to drive mitochondrial adaptations, while resistance exercise is required to drive myofibrillar adaptations. However, concurrent practice of traditional endurance exercise and resistance exercise regimens to achieve both types of muscle adaptations is time-consuming, motivationally demanding, and contended to entail practice at intensity levels, that may not comply with clinical settings. It is therefore of principle interest to identify effective, yet feasible, exercise strategies that may positively affect both mitochondrial and myofibrillar protein turnover. Recently, reports indicate that traditional high-load resistance exercise can stimulate muscle mitochondrial biogenesis and mitochondrial respiratory function. Moreover, fatiguing low-load resistance exercise has been shown capable of promoting muscle hypertrophy and expectedly entails greater metabolic stress to potentially enhance mitochondrial adaptations. Consequently, fatiguing low-load resistance exercise regimens may possess the ability to stimulate muscle mitochondrial adaptations without compromising muscle myofibrillar accretion. However, the exact ability of resistance exercise to drive mitochondrial adaptations is debatable, not least due to some methodological challenges. The current review therefore aims to address the evidence on the effects of resistance exercise on skeletal muscle mitochondrial biogenesis, content and function. In prolongation, a perspective is taken on the specific potential of low-load resistance exercise on promoting mitochondrial adaptations. Mitochondria are intracellular organelles that play a key role in metabolism and cellular homeostasis. Consequently, mitochondrial content and function exert great influence on substrate utilization capacity and skeletal muscle health, with skeletal muscle mitochondrial abnormalities implicated in the pathology of chronic disorders such as diabetes, obesity, and peripheral arterial disease  has profound effect on mitochondrial content and function. Mitochondrial biogenesis is reported to be attenuated with aging, prolonged inactivity, and/or chronic disease  can accumulate into muscle hypertrophy ACSM, . HoweverBelow we provide a summary on the current knowledge on how resistance exercise affects skeletal muscle mitochondrial biogenesis and whether repeated resistance exercise can enhance mitochondrial content and function. As different methodological approaches have produced discrepancies in results, some important methodological issues will also be addressed. Oppositely, while the importance of mitochondrial degradation  is acknowledged, this will not be addressed. The summarized knowledge is retrieved from human studies, as animal models that render voluntary resistance exercise possible are lacking. Finally, since most studies on human resistance exercise have utilized high loads, we will discuss the perspectives of using low-load resistance exercise to stimulate mitochondrial adaptations in clinical populations that may be less able to perform high-load resistance exercise.Mitochondria are dynamic organelles residing in reticular networks and constantly adapting to accommodate new demands by processes such as biogenesis, mitophagy, fission and fusion  rate, necessary  rate increase during early recovery  after single-bout high-load resistance exercise in healthy individuals in the untrained state  , allowing these limitations to be circumvented. In accordance, D2O is orally administered, and rapidly equilibrates throughout body water compartments and facilitates intrinsic labeling of amino acids and nucleic acids , while not allowing for investigation on the synthesis rate of proteins with slower turnover rate  and/or cumulative exercise training effects  activity as a biomarker, because CS activity has been shown to exhibit a degree of association with mitochondrial volume density determined by electron microscopy, while simultaneously offering a significantly less time-consuming approach (Larsen et al., In conclusion, while these divergent results may in part adhere to the degree of muscle hypertrophy achieved (Tang et al., Utilization of high resolution respirometry in permeabillized myofibers and/or isolated mitochondria from muscle biopsies may allow for a more physiologically relevant approach to study mitochondrial function (Haller et al., Peculiarly, contrary to studies on permeabilized fibers, employment of high resolution respirometry on isolated mitochondria indicates little effect of high-load resistance training on mitochondrial function. In accordance, two recent randomized controlled studies collectively favor the contention that high-load resistance training is not able to augment mitochondrial respiratory function (Irving et al., The results of relevant resistance exercise studies on mitochondrial biogenesis, content, and function are summarized in Table Judged from the ability to increase MitoPS, single-bout low-load as well as high-load resistance exercise can stimulate human muscle mitochondrial biogenesis. However, the influence on resistance exercise-induced MitoPS of training status, resistance exercise principles, and dietary conditions inherent of experimental protocols, deserves further attention. Implementation of non-exercise control experiments and utilization of tracers, allowing for assessment of proteins with slower turnover rate, may substantially aid such investigation.With regards to accumulated effects of high-load resistance training on permanent mitochondrial adaptations, results remain more equivocal. In accordance, interpretation of studies on mitochondrial content is hampered by evaluation from surrogate markers of mitochondrial content and/or limited sample size. Utilization of electron microscopy combined with sufficient statistical power, would likely allow for more firm conclusions. As judged from the ability to increase mitochondrial respiration, high-load resistance exercise can stimulate mitochondrial function. Yet, since this has so far only been reported from studies on permeabilized fibers, but not confirmed in studies on isolated mitochondria, this specific methodological aspect also requires further attention.To recapitulate, an accumulating burden of proof favors the contention that high-load resistance training can promote mitochondrial adaptations. Yet, future studies considering some of the factors mentioned above, are warranted before firm conclusion can be made. Furthermore, virtually all previous studies have employed high-load resistance exercise, while still leaving it interesting if low-load resistance exercise can stimulate mitochondrial adaptations.From a clinical perspective, it is highly interesting that high-load resistance training possess a more pronounced ability to augment mitochondrial adaptations in older adults (Jubrias et al., However, whereas current evidence supports that high-load resistance training can stimulate mitochondrial adaptations in clinical populations, high loading may not always be feasible in specific subpopulations. It is therefore interesting that one study by Burd et al.  supportsFatiguing low-load and/or high-volume resistance exercise has been shown to produce greater ATP turnover compared to traditional high-load resistance exercise (Gorostiaga et al., While traditional low-load resistance exercise performed to volitional fatigue may offer a less mechanically strenuous alternative, it must still be regarded strenuous in terms of time and effort. In this regard, low-load blood flow restricted resistance exercise may offer an alternative in specific cohorts. In accordance, the added ischemia inherent of low-load blood flow restricted resistance exercise has been demonstrated to substantially reduce the time required to reach a point of volitional fatigue and thereby reduce the required work volume, while still stimulating muscle accretion equally much as free-flow low-load resistance exercise performed to fatigue (Farup et al., All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cardiometabolic disease poses a serious health and economic burden worldwide and its prevalence is predicted to increase. Prolonged sitting, lack of physical activity, poor diet, and short sleep duration are ubiquitous behaviors in modern society, and all are independent risk factors in the development of cardiometabolic disease. Existing evidence demonstrates that breaking up prolonged periods of sitting is beneficial for cardiometabolic health, however, studies have not controlled for prior sleep duration. This article examines how prolonged sitting and short sleep duration independently contribute to cardiometabolic risk, and how breaking up sitting and obtaining adequate sleep may reduce this risk. We suggest that as prolonged sitting and short sleep duration influence the same cardiometabolic parameters, there is potential for short sleep to attenuate the positive impact of breaking up prolonged sitting with physical activity. Likewise, breaking up prolonged sitting and obtaining adequate sleep together could improve predictors of cardiometabolic disease, i.e., the combined effect may be stronger than either alone. To explore these perspectives, we propose a research agenda to investigate the relationship between breaking up prolonged sitting with physical activity and short sleep duration. This will provide an evidence-base for informing the design of interventions to reduce the burden of cardiometabolic disease on communities worldwide. Cardiometabolic disease describes a spectrum of conditions beginning with insulin resistance, progressing to the metabolic syndrome, pre-diabetes, and finally to more severe conditions including cardiovascular disease (CVD) and type 2 diabetes (T2DM) , sleep, and prolonged sitting , while in a sitting, reclining or lying posture  with short bursts  of standing or short bouts of light-intensity physical activity  appears to benefit many aspects of cardiometabolic health. In habitually short sleeping adults (6.3 ± 0.5 h), increased time in bed (+44 ± 34 min) during a sleep extension intervention was associated with improved glucose regulation and insulin sensitivity  prior sleep duration. This is of concern because short sleep is associated with negative changes to the same cardiometabolic markers that positively change with breaking up prolonged sitting. Indeed, a recent study observed no benefit of breaking up prolonged sitting on glucose metabolism under conditions of sleep restriction  and sleep  required for cardiometabolic health benefits, and the feasibility of interventions targeting both prolonged sitting and short sleep duration.A series of rigorously-controlled laboratory protocols are required to investigate how breaking up prolonged sitting and sleep duration interact, and how this, in turn, impacts cardiometabolic health outcomes. Such protocols would involve determining how different combinations of sitting (duration and interruptions) and sleep  acutely affect markers of cardiometabolic health. Daily and postprandial measures of glucose concentration and insulin sensitivity  are required to assess the impact of these behaviors on glucose metabolism. Such studies should also attempt to determine the underlying physiological mechanisms behind observable changes in glucose metabolism through ancillary measures .There is still considerable debate concerning the optimal or minimum type, intensity, duration, and frequency of physical activity necessary to replace sitting time and to benefit cardiometabolic health . While these reasons are often multi-factorial , such investigations inform intervention development. Intervention studies targeting multiple health behaviors simultaneously, in this case breaking up sitting and improving sleep, should be conducted to determine acceptability, feasibility, and short- and long-term efficacy  to benefit health.and adequate sleep presents scope for optimization and intervention in the context of lifestyle-related chronic disease prevention. The application of the proposed research agenda will be wide-reaching and inform interventions to address the current burden of cardiometabolic disease, and will provide public health guidance by focusing on behaviors in combination.Effective prevention strategies that target physical activity, diet, sleep and sedentary behavior in combination are needed to improve cardiometabolic health. This includes an investigation into the interactions between behaviors, e.g., sleep, diet, prolonged sitting, and physical activity, to maximize health benefits and reduce cardiometabolic risk. As a first step, the behavioral duo of breaking up prolonged sitting All authors contributed in writing the manuscript. GV wrote the first draft of the manuscript. SF provided critical feedback and revised the first draft. SJ, CS, CV, and NR contributed perspectives on their specific areas of expertise.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Camellia sinensis, (L.) Kuntze) is considered as most popular drink across the world and it is widely consumed beverage for its several health-benefit characteristics. These positive traits primarily rely on its regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions are being worthwhile for reviewing the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea, the trait-specific Simple Sequence Repeat (SSR) markers, leading to be useful in marker assisted breeding technique, are yet to be identified. Micro RNAs are short, non-coding RNA molecules, involved in post transcriptional mode of gene regulation and thus effects on related phenotype. Present study deals with identification of the microsatellite motifs within the reported and predicted miRNA precursors that are effectively followed by designing of primers from SSR flanking regions in order to PCR validation. In addition to the earlier reports, two new miRNAs are predicting here from tea expressed tag sequence database. Furthermore, 18 SSR motifs are found to be in 13 of all 33 predicted miRNAs. Trinucleotide motifs are most abundant among all followed by dinucleotides. Since, miRNA based SSR markers are evidenced to have significant role on genetic fingerprinting study, these outcomes would pave the way in developing novel markers for tagging tea specific agronomic traits as well as substantiating non-conventional breeding program.Tea ( Camellia sinensis) is most popularly consumed beverage across the world and being a cash-crop, it receives much attention to the scientific community. The vibrant research interest basically stands for its massive demand to health conscious people for its antioxidant and a broad spectrum therapeutic potentiality [Tea , phosphoglycerate mutase (Target Accession: TC194811). In addition the target genes of other predicted miRNAs of tea were explored from the literature to elucidate their role in pre-miRNA SSR based polymorphism assessment. Zhu and Luo [Plant microRNAs do regulate the transcripts expression for growth, development and stress responses by altering leaf morphology and polarity, organ development, lateral root formation, hormone signalling, cell death, signal transduction, cell differentiation and proliferation, transition from juvenile to adult vegetative phase, vegetative to flowering phase, flowering time, floral organ identity and reproduction , 43–46.  and Luo  reported and Luo . MultiplMicrosatellite markers are widely used tool for estimating the genetic variation and especially used for construction linkage map, understanding of marker trait association, identification of disease resistant loci , 49. TheFinally, newly predicted microRNAs in tea would enrich the assemblage in absence of whole genomic information of tea and make easier subsequent studies for experimental validation. Some of them might be related to certain metabolic functions thereby phenotypes as well. Excavating of microsatellite motifs from predicted microRNA precursors and designing primers from SSR flanking regions would pave the way in developing novel markers for tagging tea specific agronomic traits as well as accelerating non-conventional breeding program. This can freely be followed by genetic diversity assessment of tea cultivars with varying characters.


Amyotrophic Lateral Sclerosis (ALS) is a highly debilitating disease caused by progressive degeneration of motorneurons (MNs). Due to the wide variety of genes and mutations identified in ALS, a highly varied etiology could ultimately converge to produce similar clinical symptoms. A major hypothesis in ALS research is the “distal axonopathy” with pathological changes occurring at the neuromuscular junction (NMJ), at very early stages of the disease, prior to MNs degeneration and onset of clinical symptoms. The NMJ is a highly specialized cholinergic synapse, allowing signaling between muscle and nerve necessary for skeletal muscle function. This nerve-muscle contact is characterized by the clustering of the collagen-tailed form of acetylcholinesterase (ColQ-AChE), together with other components of the extracellular matrix (ECM) and specific key molecules in the NMJ formation. Interestingly, in addition to their cholinergic role AChE is thought to play several “non-classical” roles that do not require catalytic function, most prominent among these is the facilitation of neurite growth, NMJ formation and survival. In all this context, abnormalities of AChE content have been found in plasma of ALS patients, in which AChE changes may reflect the neuromuscular disruption. We review these findings and particularly the evidences of changes of AChE at neuromuscular synapse in the pre-symptomatic stages of ALS. The vertebrate neuromuscular junction (NMJ) is a “tripartite” synapse, composed by the presynaptic motorneuron (MN), the postsynaptic muscle, and the synapse-associated glial cells  rapidly degrades ACh, thus terminating synaptic transmission . AChE is a crucial enzyme for nerve functions, hydrolyzing ACh in the synaptic cleft, while the function of BChE and its role in the regulation of AChE levels is still under discussion.AChE exists as three distinct variants coming from alternative exon splicing, each of them with a different C-terminal sequence which determines the possibility to form different oligomeric species , gave rise to a protein, lacking both the catalytic site and the C-terminal neuritogenic domain  is caused by progressive degeneration of upper and lower MNs with rapid clinical course. Denervated muscles weaken, atrophy and death usually occurs due to respiratory failure in patients within 2–5 years. Despite our knowledge above the genetic causes for this disease, a major question is still opened: Where does MN dysfunction begin? Both dying-forward and dying-back hypothesis have been considered  and slow (S)  mutant mice  mutation is conditionally expressed in MNs, the progressive degeneration is preceded by early (day p20) and selective (FF MNs) motor axons retraction. Also, the NMJs are precociously changed with a drastic reduction of synaptic vesicles and pre- and postsynaptic mitochondria, which appeared dilated and vacuolated as described in mutant hSOD1G93A and hTDP43A315T transgenic models of ALS , involved in the packaging of ACh inside the synaptic vesicles before release. This protein is localized at the synaptic terminal and considered a marker for cholinergic synapses. Immunohistochemical exams on the spinal MNs revealed a drastic depletion of VAChT immunoreactivity respect to synaptophysin, a marker for MNs synapses integrity. This discrepancy suggested a loss of cholinergic inputs as an early event of ALS neurodegeneration  with an unknown etiology, in about 10% of the cases there is a Mendelian inheritance (fALS) where more than 20 genes seems to be implicated , Spain; Atip/Avenir from Inserm, Career Integration Grant (Marie Curie Actions), Robert Packard Foundation, E-rare ERA-NET program, AFM, ARSLA, France-Alzheimer association and the program “Investissements d’avenir” ANR-10-IAIHU-06 (EK).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Major cardiovascular events including myocardial infarction (MI) continue to dominate morbidity rates in the developed world. Although multiple device therapies and various pharmacological agents have been shown to improve patient care and reduce mortality rates, clinicians and researchers alike still lack a true panacea to regenerate damaged cardiac tissue. Over the previous two to three decades, cardiovascular stem cell therapies have held great promise. Several stem cell-based approaches have now been shown to improve ventricular function and are documented in preclinical animal models as well as phase I and phase II clinical trials. More recently, the cardiac progenitor cell has begun to gain momentum as an ideal candidate for stem cell therapy in heart disease. Here, we will highlight the most recent advances in cardiac stem/progenitor cell biology in regard to both the basics and applied settings. Due to marginal improvements in heart failure treatments, a greater number of elderly patients are living longer with chronic heart failure. However, no treatment regime is capable of fully reversing pathological remodeling or completely restoring ventricular function after a major cardiovascular event, such as MI. In fact, many patients progress steadily towards New York Heart Association (NYHA) class III-IV heart failure where the only curative therapy is heart transplantation. Due to the unbalanced need of donor hearts, alternative regenerative therapeutic approaches aim to build up lost functional ventricular muscle.Cell-based therapies have been conceptualized to alleviate some of the barriers limiting cardiac regeneration. The golden objective in cell-based therapies is to repopulate parts of damaged myocardium with engrafted, functional cells that restore lost cardiac function, enabling sufficient oxygen and nutrient circulation to all the vital organs of the body. Several technological, financial, and ethical hurdles impede such a medicinal feat, yet the field continues to move forward with the collaborative efforts between stem cell biologists, who are investigating novel mechanisms of cardiac regeneration, and medical teams in cardiology.in vivo under pathological conditions, such as the ischemic and/or injured microenvironment , 47in vistigated .Regardless of the several critical issues as described above, the concept of enhancing stem cell properties through a combination of strategies could go some way in obtaining better outcomes for patients. An innovative focus that aims to synergize cell-based and cell-free therapies such as combining “ideal” CPC types with gene therapy, small molecules, and/or tissue engineering strategies should be conceptualized as a plausible clinical treatment for the enhancement of regenerative therapies in cardiovascular disease . Continu


Drawing on a new set of nationally representative, internationally comparable household surveys, this paper provides an overview of key features of structural transformation – labor allocation and labor productivity – in four African economies. New, micro-based measures of sector labor allocation and cross-sector productivity differentials describe the incentives households face when allocating their labor. These measures are similar to national accounts-based measures that are typically used to characterize structural change. However, because agricultural workers supply far fewer hours of labor per year than do workers in other sectors in all of the countries analyzed, productivity gaps shrink by half, on average, when expressed on a per-hour basis. Underlying the productivity gaps that are prominently reflected in national accounts data are large employment gaps, which call into question the productivity gains that laborers can achieve through structural transformation. Furthermore, agriculture’s continued relevance to structural change in Sub-Saharan Africa is highlighted by the strong linkages observed between rural non-farm activities and primary agricultural production. In the development context, it refers both to the reallocation of labor from one low-productivity sector to another, higher-productivity sector, and to the economic growth resulting from this shift. Structural change is a dynamic process powered by several key features – productivity levels within sectors, productivity gaps between them, and the movement of labor from low productivity to high productivity sector(s). The larger the productivity gap between agriculture and other sectors, the larger the opportunity to achieve productivity growth as labor shifts across sectors. In poor economies, agriculture is typically the sector that employs the most people and uses labor least productively. Over time, cross-sector productivity gaps tend to shrink as labor shifts out of agriculture and returns to labor across sectors are equalized through factor markets .The premise of higher returns to labor outside of agriculture is quite central to structural change. Are these productivity differentials really as high as national accounts data suggest? I use a new micro-level dataset to measure key structural change parameters – sector participation, time use, and labor productivity – from a micro perspective. This paper draws on the Integrated Surveys on Agriculture from the Living Standards Measurement Study group at the World Bank (LSMS-ISA datasets), which explicitly collect information about respondents’ time use across sectors. Particular attention is paid to farm labor, which is often neglected in large scale, multi-topic surveys because of the challenges involved in collecting detailed agricultural data. The analysis includes surveys from Ethiopia, Malawi, Tanzania and Uganda.Examining productivity gaps from a micro perspective is informative for several reasons. First, individuals and firm owners making labor allocation decisions in developing countries do so based on the micro incentives that they face. Second, micro datasets contain the variables required to address the validity of assumptions that underlie macro statistics. Third, micro datasets allow for productivity measures to be paired with relevant covariates of labor allocation decisions at the household and individual levels. This kind of micro perspective is largely absent from the literature about structural change in African economies. Demographic and Health Survey (DHS) datasets, also micro datasets, are sometimes used to calculate sector labor shares, as an alternative to measures based on population censuses or national accounts e.g., . While DI find that, in four Sub-Saharan African countries, the agricultural sector is not a bastion of low productivity but, rather, a large reservoir of underemployed workers. This result emerges when labor inputs are measured more carefully. Using the LSMS-ISA datasets, I replicate common patterns observed in macro statistics – that annual economic output per worker is lower in agriculture than in other sectors and that participation in agriculture is much higher than participation in other sectors. While national statistics suggest that workers in these four countries are 6 times as productive outside of agriculture as in it, I predict the number is closer to 3.4 times on average. This finding is consistent with those of After carefully examining labor inputs, I find that cross-sector productivity gaps observed in national accounts data reflect sectoral differences in employment levels rather than differences in returns per hour worked. Many workers are counted as agricultural because they spend at least some time working on farms. A striking pattern across household surveys is that agricultural workers work fewer hours per year – 700 hours per agricultural worker compared to 1850 hours per non-agricultural worker. Productivity in agriculture is a lot closer to productivity outside of it when one accounts for systematic differences in labor inputs. On a per-hour basis, labor is only 1.4 times as productive outside of agriculture.These results suggest that the forces pulling labor into the industry and service sectors may be weaker than is commonly believed. It also casts doubt on the notion that agriculture is intrinsically less productive than other sectors. Because time inputs in agriculture are generally low, possibly due to biophysical constraints, participation outside of agriculture presents the opportunity to supply more hours of labor per year. It is important to better understand the reasons for low labor supply by agricultural workers in order to identify opportunities to increase annual output per agricultural worker.2This paper focuses on Sub-Saharan Africa, the region with the lowest per capita incomes, largest shares of value added captured by agriculture, largest shares of the work force employed in agriculture, and lowest agricultural labor productivity a World . AccordiLabor productivity in an economy can be improved either within sectors  or structurally .Understanding micro level cross-sector productivity differences, and how they relate to sector allocation decisions, is crucial for understanding the forces that power agricultural labor exits. If productivity gaps are indeed as large as African macro statistics suggest, then one must wonder why so much labor remains in rural areas and why rural income diversification remains so low . One expAlternatively, national accounts may mis-measure key components of the productivity equation, namely, labor inputs or returns per worker. After examining many of the assumptions used to measure agricultural labor productivity gaps from national accounts data, Another explanation for small micro gaps and large macro gaps is that micro gaps are truly smaller than macro gaps. The cross-sector gaps that households face will be smaller than those suggested by national accounts, should the differential returns to non-agriculture sector activities accrue to owners of capital rather than labor. In capital-intensive industries like mining, wage rates are likely to be much lower than average labor productivity in mining as per national accounts data .If there is systematic measurement bias across sectors, then productivity gaps calculated from national accounts data will be biased. This paper generates micro-based productivity measures in order to highlight the productivity gaps that households face and to inform the debate about productivity mis-measurement in national accounts data.S refers to service sector output, NS refers to the number of service sector workers, and HS refers to the annual hourly input of a service sector worker. The A subscript refers to the agriculture sector. The gap in annual output per worker between the service and agriculture sectors can then be decomposed into a gap in productivity per hour worked across the sectors (PGAPS) and a gap in employment levels across the two sectors (EGAPS). If cross-sector productivity is equalized in terms of returns to hourly or daily labor at the margin, and labor productivity gaps are largely explained by cross-sector differences in labor inputs, then one can no longer argue that labor is grossly misallocated across sectors even when there is a large cross-sector gap in annual returns per worker. This has implications for the forces that drive labor exits – they relate to seeking fuller employment rather than climbing a per-hour productivity gradient.Consider the productivity gap between agriculture and services, decomposed into labor inputs (hours per year) and productivity (returns per unit of labor input):Claims of underemployment in a smallholder sector have been common, historically. Lewis’ two-sector model was premised on “unlimited supplies of labor,” positing labor surplus in subsistence agriculture and also among casual laborers and those self-employed in petty trade . Lewis aBecause agricultural labor shares are large in African countries, the potential gains from reallocating labor to higher-productivity sectors are also hypothesized to be large . A largeThough African countries seem to be following the same patterns of agricultural labor exits as those followed decades earlier in Asia and Latin America , there aGrowth in labor productivity, overall and within agriculture, has been a strong predictor of poverty reduction because of the important linkages between wages, household self-employment, and the real incomes of the poor. Though land productivity growth typically precedes labor productivity growth, the process of agricultural development is thought to begin when output per agricultural worker increases . Agricul5Labor is one of several important factors in agricultural production, which also relies on land, capital, and other inputs. Where land and capital are scarce , labor is used more intensively in farming systems. In aggregate, agricultural labor productivity grew slower than agricultural land productivity between 1961 and 2010 in Africa, which implies that African agriculture has intensified with respect to labor . However3Rural Socioeconomic Survey (2013–14), the Malawi Integrated Household Survey (2010–11), the Tanzania National Panel Survey (2010–11), and the Uganda National Panel Survey (2010–11). LSMS-ISA surveys were implemented by each country’s national statistics office, with technical support from the World Bank Development Economics Research Group. These datasets are nationally representative, including urban and rural households regardless of occupation or sector of employment. Rural and urban areas are defined by each country’s statistics office.To examine labor productivity gaps from a micro-economic perspective, I generate labor productivity measures and other key variables from the Living Standards Measurement Survey – Integrated Surveys in Agriculture (LSMS-ISA) dataset. I draw on a cross-section of recent LSMS-ISA datasets available, comprised of the Ethiopia Using the LSMS-ISA data, I construct individual level, annualized labor supply aggregates for three types of activities – household operated farm enterprises (farms), household operated nonfarm enterprises (NFEs), and wage labor market participation. Labor supply recall questions differ in the LSMS-ISA surveys by type of activity. Wage labor supply variables are generated over a twelve month recall period from individuals’ reported number of months worked over the last year, typical number of weeks worked per month, and typical number of hours worked per week. In the agriculture modules of the surveys, labor inputs by individual household members are collected for each farm plot. These inputs are aggregated for each household member to generate the annual own farm labor supply variable. For non-farm enterprises, participation by household members is flagged at the firm level. NFE labor supply collection differs slightly from country to country, as detailed in Systematic measurement error in construction of labor supply variables is particularly concerning, should respondents recall different types of activities with different errors. Differences in recall period (through questionnaire design or timing of interview) or differences in recall ability for different activities  can lead to differences in household responses . The posNext, I construct aggregates of labor demanded by household operated farms and NFEs, which include hired labor in addition to labor supplied by family members. Of interest are both the number of firm workers and the total labor inputs supplied by workers to each firm. In the case of farm enterprises, we have a good measure of labor inputs, the number of household members who work on the farm, and the total number of hours worked by household members and hired workers. We do not, however, observe the number of employees hired. It is quite common for farm households to hire in some labor (between 30% and 94% of farms do it). In order to avoid under-estimating the total number of farm workers, I predict the number of hired workers by assuming that hired workers work the same hours as own farm workers. In the case of NFEs, we universally observe the number of hired workers but not the hours that they supply to the firm. Non-farm enterprises do not commonly hire workers. In all cases, fewer than 19% of households operating an enterprise hire in any workers.Returns to labor market participation are comprised of the gross total wages received by wage workers, including in-kind payments  and gratuities. Costs of participating in wage labor markets are not measured so it is not possible to construct a net returns measure. The returns to operating a farm enterprise are based on net farm revenue, which is analogous with the “value added” concept that underlies national accounts data. The net value of farm output is derived from the Rural Income Generating Activities (RIGA) calculations and includes the value of own-consumed farm output as measured through the consumption module . For nonUsing the labor supply variables and the returns variables, I construct average labor productivity variables. These are done separately for the three types of activities – wage labor, farms, and NFEs – as a simple ratio between returns to an activity and labor inputs into the activity. Two types of average labor productivity measures are constructed. The per-worker measure is based on output per worker per year. The per-hour measure is based on output per hour of labor supplied to each activity per year. Because we do not observe how many hours hired workers supply to NFEs, I am unable to generate per-hour productivity measures for these firms.The next task involves generating sector level labor productivity measures, which aggregate, at the sector level, returns from and labor inputs to self-employment, wage employment, and farming. First, all activities are assigned to their respective sectors of the economy . Following The hourly agricultural labor supply aggregates do not include livestock and post-harvest labor. And the corresponding agricultural labor productivity measures do not include revenue from livestock in the numerator. In the per-person agricultural labor productivity measure, the numerator includes net livestock revenue (taken from the RIGA dataset), and the denominator includes workers who participate in livestock rearing. 44.1Often in the macro measures of sector productivity, individuals are constrained to one sector of participation, and it is assumed that individuals in each sector work the same number of hours and do not supply labor to secondary sectors. Usually, each sector’s labor inputs are assumed to be of the same skill and not adjusted for different levels of human capital. Initial examination of these assumptions using LSMS-ISA data suggests that they are indeed problematic and lead one to overestimate labor supplied to agriculture relative to other sectors, thereby artificially inflating estimates of the labor productivity gap between agriculture and other sectors .Several patterns are common to all of the countries depicted in Per-person productivity measures based on categorizing individuals by their primary sector of occupation implicitly ignore individuals’ contributions to secondary sectors. They also assume that participants in different sectors supply equal hours of labor. Both assumptions are problematic when individuals supply labor to secondary sectors, or when there are systematic cross-sector differences in hours supplied. Indeed, LSMS-ISA datasets suggest that both assumptions are violated.Violation of the equal hours assumption, on the other hand, leads to overestimation of agricultural labor inputs. Overall, the LSMS-ISA datasets suggest large gaps between hours supplied to agriculture and hours to industry and service sectors. By calculating sector labor inputs based on participation rather than hours worked, one over-estimates labor inputs in agriculture compared with other sectors.The bars labeled “Hours” in 4.2Cross-sector productivity gaps calculated from the LSMS-ISA datasets are indicative of the average productivity differentials that households face when allocating their labor. Much of the productivity differences observed in national accounts statistics may then be attributable to differences in hours supplied by workers in each sector rather than differences in output produced per hour worked in each sector. After further disaggregating returns to labor between self-employment and wage employment, it is clear that wage employment brings higher annual returns to participants than does self-employment. The sector-activity patterns depicted in The existence of cross-sector gaps in output per worker per year suggests there are some forces enticing smallholder farmers into industry and service sector activities. Participation in industry and service sector activities may allow for fuller levels of employment in terms of hours of labor supplied per year. It is not possible, with cross-sectional data, to determine whether agricultural workers tend to work fewer hours because of constraints to labor supply or to labor demand. Biophysical and agronomic characteristics could limit the periods during the year in which farm labor can be used productively. In this case, it might not be possible for individuals to increase their agricultural sector returns by supplying more labor to their farms. Presumably, because labor supply is so low across households and countries, low demand for labor by agriculture is a key constraint. Agriculture’s role as a low entry barrier sector could help explain both high levels of participation in farming and low per-worker labor supply. Though individuals may aspire, and even attempt, to participate in non-farm activities, they may still return to farming as the sector that can basically guarantee employment. Labor transitions back into agriculture by individuals who had exited farming has occurred in Uganda . UnderstWithin urban areas, self-employment in the service sector does not seem to serve as a sink for underemployment as does agriculture in rural areas. One might expect high rates of declaring self-employment due to possibly lower entry barriers than wage employment. Using the LSMS-ISA datasets, 5Next, I turn to showing that these productivity gap measures are robust. I am concerned with both the measurement of labor inputs and the returns to labor. The first major concern is sensitivity of labor productivity measurement to survey timing. Labor supply varies seasonally and is elicited over discrete recall periods, raising the possibility that seasonal bias enters into labor productivity measurement. The second major concern arises from questionnaire design issues. Different types of labor supply are collected through different survey modules and elicited in different ways. The goal is to show that the key insights regarding sector participation, labor supply, and productivity are fairly robust to survey design. At the end of this section, I turn to measurement of the returns to labor, showing that the same patterns hold if consumption is used instead of income as a measure of returns to labor.Annualized labor supply measures of participation and hours worked comprise the denominators of per-person-per-year and per-hour productivity measures, respectively. These aggregates are constructed from more detailed labor supply questions asked of respondents, such as the number of hours worked in the last week, or, in some cases, the number of hours worked in a typical week. One would expect these aggregates to move seasonally due to seasonal patterns in labor supply or a combination of seasonality of labor supply and recall bias in the case of a “typical week” recall approach.I demonstrate how the per-worker-per-year and per-hour labor productivity measures vary by month of survey visit in t-test for difference between the survey weighted means depicted in Section In order to address concerns that survey timing is somehow correlated with seasonal productivity patterns, I generate new population-month weights to create annually representative measures of per-person-per-year and per-hour productivity for each sector. Using the weights, I also generate annually representative measures of the different components of labor supply. On the extensive margin, this includes participation on an annual basis. On the intensive margin, this includes participation in the last week conditional on participation in the last year and hours of labor supplied per week. I conduct a Because labor supply variables for different activities are constructed from different types of survey questions, there is concern that differences in labor supply across activities could arise from different survey recall approaches rather than actual labor supply differences. In particular, downward bias in the measurement of agricultural labor supply or upward bias in self-employment or wage employment labor supply would undermine the agricultural underemployment findings. In a recent methodological experiment designed to compare different approaches to measuring farm labor inputs, Sector productivity gap estimates are sensitive to prospective measurement error of labor returns – farm and firm revenue and wage earnings. I use an alternate measure of returns to labor, household consumption, to ensure that the measurement of productivity gaps is robust to measurement of returns. Consumption can be thought of as household profits after participation costs (for wage labor) and production costs (for firms), assuming no savings or dis-savings. Because households who face stochastic income generally smooth their consumption from year to year, consumption can be a good measure of permanent income . It is aThe consumption gap estimates are a ratio in annual per-worker consumption between households participating primarily in agriculture and those participating primarily in industry and services, respectively a. I also6It is important to understand not only sectors of employment, but also the modalities by which workers supply their labor. Because of growth linkages between agriculture and non-agriculture, the specific types of activities to which workers supply labor can inform the scope for growth linkages between different sectors of the economy.Recent micro evidence suggests that, while non-agricultural sources of income bring the highest returns across the welfare distribution, the majority of households in African rural areas remain specialized in agricultural income earning activities . After cA close examination of the non-farm activities in which households are involved suggests some clear patterns across countries. Workers outside of agriculture are more educated, younger, and less female than agricultural workers. Rural non-farm activities tend to be closely related to agriculture, with strong producer and consumer linkages.6.1It is important to explore any systematic differences in characteristics of sector participants, so that they can be taken into account when interpreting sector labor supply data generated from national accounts. Indeed, the macro-economic literature is concerned with systematic differences in human capital across sectors and the implications for bias in productivity measures .Industry and service sector workers tend to be younger, on average, than agricultural workers. In all countries, the agricultural work force contains a larger share of women than men, while industry and service sector work forces contain more men than women. 10Participation in self-employment is more common than wage labor participation in all countries, with 74–89% of rural adults participating in farming. Agricultural wage labor participation is less common than farming, with fewer than 15% of rural adults participating in Ethiopia, Tanzania and Uganda, and 33% in Malawi. In all countries, the average agricultural wage laborer is much more likely to be male than the average farm worker. Agricultural wage workers have more education, on average, than farm workers in Ethiopia and Malawi, and less in Tanzania and Uganda.Behind agriculture, the services sector has the next highest overall participation rate. And in urban areas, the services sector is the most important. Workers are more likely to participate in the services sector through self-employment than wage employment in both rural and urban areas, except in urban Ethiopia where wage employment is higher than self-employment. Wage labor participants in the services sector are more likely to be male and to have higher education levels than self-employed service sector participants. Both wage and services sector participants supply similar numbers of hours per year except in Tanzania, where service sector self-employed workers supply far fewer hours than do wage laborers.Within the industry sector, rural individuals typically are more likely to participate as self-employed rather than wage workers. Participation as either self-employed or wage workers is below 6.5% everywhere except Malawi, where one third of rural adults participate in industry sector self-employment.There is always concern that differences in productivity may reflect observed and unobserved differences in the households and individuals participating in the activities rather than inherent differences in the economic productivity of the activities themselves. In an attempt to control for the effects of household level selection on productivity gap measurement, I generate within-household sector-activity productivity gap measures for households that participate in multiple activities. For households that participate both in farming and another activity, these reflect the ratio within the household between returns to the non-farm activity and farming. The conditional sector-activity gaps depicted in 6.2Next, I use respondents’ free descriptions of their self-employment and wage employment activities, along with the detailed industry codes assigned by enumerators, to examine carefully the kinds of non-farm activities in which respondents are involved. Within each sub-sector, I use text analysis to identify the words that most commonly appear in respondents’ descriptions . We do nIn rural areas of all four countries, agricultural wage labor is the largest category of wage employment. In Ethiopia, around 60% of wage employment occurs as casual or informal labor for which no sector information or job description was collected. Most likely, this labor is supplied to the agriculture sector.Within the industry sector, mining does not play an important role in rural or urban areas of any of the datasets we analyze. Manufacturing accounts for between 13% and 38% of rural NFEs, with the smallest share in Tanzania and the largest in Malawi. However, only 1–9% of wage-earning jobs occur in manufacturing. According to text analysis, manufacturing NFEs focus heavily on elementary activities such as brewing alcoholic beverages, charcoal production, milling grains, butchering and other agricultural processing, baking and other value addition activities, and the production of household goods, clothing, and other handicrafts. Manufacturing wage jobs are similar, with a focus on agri-processing for cash crops, timber, and textiles, as well as the manufacturing of bricks and other building materials. Construction accounts for between 2% and 6% of rural wage jobs and between 5% and 9% of urban wage jobs but fewer than 2% of NFEs. Construction wage employment, according to text analysis, typically involves working as a laborer on a building or road construction site.Individuals and households who participate in the industry sector are involved mainly in manufacturing activities that have strong links with primary agricultural production. Industry sector participants contribute to manufacturing raw agricultural materials into typically non-tradable goods meant for local consumption. These patterns suggest strong links between rural industry-sector activities and agriculture. In rural areas, the manufacturing industry stands to gain from productivity growth in agriculture, and rural manufacturing workers are poised to benefit from demand spurred by rising agricultural incomes in rural areas. Because the manufacturing activities reported in these surveys are so closely linked with agriculture, one would not expect to see expansion of rural industry sector activities independently, without any agricultural growth. These classic Mellor-Johnston linkages are quite prominently featured in rural households’ economic activities.Commerce is the dominant focus of self-employment in the services sector, while jobs tend to involve general services provision. Commerce comprises between 26% and 66% of both rural and urban firms. These are involved in activities such as the wholesale and retail trade of fruits and vegetables, other food items, charcoal, second hand goods, and other household goods. Commerce accounts for up to 20% of wage earning in urban areas of Tanzania, but the share is more often closer to 5–10% in urban areas, and lower in rural areas. Commerce wage earners are most commonly sales clerks and store attendants. The general services category is the most important for wage employment, accounting for 42–45% of urban jobs across all countries, and 12–31% of rural wage earning jobs. These wage workers include teachers, health, social and religious workers, public administrators, technicians, domestic service providers, as well as restaurant, hotel, and tourism employees. General services account for a smaller share of firms than of wage jobs. The most common firm descriptions include restaurants, caterers, bars, hotels, professional service providers, and repair shops. The transport sub-sector accounts for a small share of self and wage employment everywhere. Transport activities tend to focus on transportation services provided by bicycle, taxi, bus, or vehicle. Finance and real estate are almost nonexistent in rural areas and account for 1–3% of urban wage earning jobs, which are most commonly administrative in nature.Buying and selling agricultural products comprises a large share of commerce activity, with respect to both self and wage employment. As with the industry sector, the services sector activities in which rural households participate are non-tradable in nature, and very focused on local consumers. Because these service sector activities serve local consumers whose incomes are dominated by agriculture-sector activities, the Mellor-Johnston linkages are again quite prominent. One would expect agricultural productivity growth to spur demand for increased local service sector labor. Given the nature of service sector activities, it would be hard to imagine strong growth in the services sector absent agricultural growth.7Micro level cross-sector labor productivity gaps are smaller than those generated using national accounts data. Inter-sectoral differences in annual earnings per worker arise from differences in employment volume (hours per worker of labor supplied) rather than wages or productivity per hour of labor supplied. At least half of these per-worker productivity gaps can be explained by differences in hours worked across sectors. The tendency is for individuals participating in agriculture to supply fewer hours to agriculture, on average, than individuals participating in other sectors. Returns to an hour of labor supplied outside of agriculture are about 1.4 times as high as returns to an hour of agricultural labor, on average, in the four countries analyzed.Generally, the micro evidence seems consistent with the idea that there is some scope for achieving productivity gains by shifting labor from agriculture to industry or services. Households expect industry and service sector wage workers to earn higher returns per year than farm workers. Self-employment brings higher annual returns to participants than farming but lower than wage employment. Since micro gaps are smaller than macro gaps, workers, who are the owners of labor, may not stand to reap the large benefits of labor exiting agriculture that are expected in the economy as a whole . Small per-worker-per-year micro gaps also suggest that agriculture-sector workers do not feel as strong a “pull” from industry and services as one might expect based on national accounts data. Small per-hour gaps do not undermine agriculture’s role in structural transformation. Despite low per-hour gaps in agriculture, it appears that workers have an excess of labor that could be absorbed productively in other sectors. This requires growth in demand for labor within or outside of agriculture.Though underemployment in agriculture has been observed in the developing country context, it is not a well understood phenomenon. Widespread underemployment could erode the benefits of using agricultural labor more productively. The existence of large employment gaps across sectors raises the question of what limits the supply of hours in agriculture and what role technology, infrastructure and policies might play in addressing agricultural underemployment. Smallholders could be operating at high levels of technical efficiency, yet face environmental production constraints, such as limiting in-season rainfall for rainfed crops . In thisBarriers to participation in non-farm self or wage employment could limit labor supply outside of agriculture by underemployed agricultural workers . These cOverall, the analysis emphasizes agriculture’s key role in Sub-Saharan African economies, while also raising questions about agricultural employment gaps, their determinants, and how they shape the opportunity to achieve economy-wide labor productivity growth. A between-sector gradient in annual output per worker remains to be exploited. Improving annual output per worker within agriculture, the highest participation sector by far, requires a better understanding of labor demand by smallholder farmers.Agriculture, and specifically the operation of household farms, remains a dominant economic activity in rural areas of Sub-Saharan Africa. And, furthermore, much of the labor supplied to industry and services sectors involves the processing and trading of agricultural and other primary goods for consumers whose incomes are dominated by agriculture-sector activities. Furthermore, the non-farm activities in which rural households are involved, across countries, are incredibly closely linked with agriculture. These strong links highlight additional benefits to achieving agricultural productivity growth, which can increase the supply of raw materials for manufacturing and increase the demand for non-tradable goods and services. These linkages are also sobering as, apart from agriculture, no engine for rural economic growth is apparent.