Role of the repeat expansion size in predicting age of onset and severity in RFC1 disease.

Currò R ，Dominik N ，Facchini S ，Vegezzi E ，Sullivan R ，Galassi Deforie V ，Fernández-Eulate G ，Traschütz A ，Rossi S ，Garibaldi M ，Kwarciany M ，Taroni F ，Brusco A ，Good JM ，Cavalcanti F ，Hammans S ，Ravenscroft G ，Roxburgh RH ，RFC1 repeat expansion study group ，Parolin Schnekenberg R ，Rugginini B ，Abati E ，Manini A ，Quartesan I ，Ghia A ，Lòpez de Munaìn A ，Manganelli F ，Kennerson M ，Santorelli FM ，Infante J ，Marques W ，Jokela M ，Murphy SM ，Mandich P ，Fabrizi GM ，Briani C ，Gosal D ，Pareyson D ，Ferrari A ，Prados F ，Yousry T ，Khurana V ，Kuo SH ，Miller J ，Troakes C ，Jaunmuktane Z ，Giunti P ，Hartmann A ，Basak N ，Synofzik M ，Stojkovic T ，Hadjivassiliou M ，Reilly MM ，Houlden H ，Cortese A ... -  《-》

Diabetes as a risk factor for tuberculosis disease.

Franco JV ，Bongaerts B ，Metzendorf MI ，Risso A ，Guo Y ，Peña Silva L ，Boeckmann M ，Schlesinger S ，Damen JA ，Richter B ，Baddeley A ，Bastard M ，Carlqvist A ，Garcia-Casal MN ，Hemmingsen B ，Mavhunga F ，Manne-Goehler J ，Viney K ... -  《Cochrane Database of Systematic Reviews》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Undernutrition as a risk factor for tuberculosis disease.

Tuberculosis (TB) is a leading cause of mortality due to an infectious disease, with an estimated 1.6 million deaths due to TB in 2022. Approximately 25% of the global population has TB infection, giving rise to 10.6 million episodes of TB disease in 2022. Undernutrition is a key risk factor for TB and was linked to an estimated 2.2 million TB episodes in 2022, as outlined in the World Health Organization (WHO) Global Tuberculosis Report. To determine the prognostic value of undernutrition in the general population of adults, adolescents, and children for predicting tuberculosis disease over any time period. We searched the literature databases MEDLINE (via PubMed) and WHO Global Index Medicus, as well as the WHO International Clinical Trials Registry Platform (ICTRP) on 3 May 2023 (date of last search for all databases). We placed no restrictions on the language of publication. We included retrospective and prospective cohort studies, irrespective of publication status or language. The target population comprised adults, adolescents, and children from diverse settings, encompassing outpatient and inpatient cohorts, with varying comorbidities and risk of exposure to tuberculosis. We used standard Cochrane methodology and the Quality In Prognosis Studies (QUIPS) tool to assess the risk of bias of the studies. Prognostic factors included undernutrition, defined as wasting, stunting, and underweight, with specific measures such as body mass index (BMI) less than two standard deviations below the median for children and adolescents and low BMI scores (< 18.5) for adults and adolescents. Prognostication occurred at enrolment/baseline. The primary outcome was the incidence of TB disease. The secondary outcome was recurrent TB disease. We performed a random-effects meta-analysis for the adjusted hazard ratios (HR), risk ratios (RR), or odds ratios (OR), employing the restricted maximum likelihood estimation. We rated the certainty of the evidence using the GRADE approach. We included 51 cohort studies with over 27 million participants from the six WHO regions. Sixteen large population-based studies were conducted in China, Singapore, South Korea, and the USA, and 25 studies focused on people living with HIV, which were mainly conducted in the African region. Most studies were in adults, four in children, and three in children and adults. Undernutrition as an exposure was usually defined according to standard criteria; however, the diagnosis of TB did not include a confirmatory culture or molecular diagnosis using a WHO-approved rapid diagnostic test in eight studies. The median follow-up time was 3.5 years, and the studies primarily reported an adjusted hazard ratio from a multivariable Cox-proportional hazard model. Hazard ratios (HR) The HR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. We present 95% confidence intervals (CI) and prediction intervals, which present between-study heterogeneity represented in a measurement of the variability of effect sizes (i.e. the interval within which the effect size of a new study would fall considering the same population of studies included in the meta-analysis). Undernutrition may increase the risk of TB disease (HR 2.23, 95% CI 1.83 to 2.72; prediction interval 0.98 to 5.05; 23 studies; 2,883,266 participants). The certainty of the evidence is low due to a moderate risk of bias across studies and inconsistency. When stratified by follow-up time, the results are more consistent across < 10 years follow-up (HR 2.02, 95% CI 1.74 to 2.34; prediction interval 1.20 to 3.39; 22 studies; 2,869,077 participants). This results in a moderate certainty of evidence due to a moderate risk of bias across studies. However, at 10 or more years of follow-up, we found only one study with a wider CI and higher HR (HR 12.43, 95% CI 5.74 to 26.91; 14,189 participants). The certainty of the evidence is low due to the moderate risk of bias and indirectness. Odds ratio (OR) Undernutrition may increase the odds of TB disease, but the results are uncertain (OR 1.56, 95% CI 1.13 to 2.17; prediction interval 0.61 to 3.99; 8 studies; 173,497 participants). Stratification by follow-up was not possible as all studies had a follow-up of < 10 years. The certainty of the evidence is very low due to the high risk of bias and inconsistency. Contour-enhanced funnel plots were not reported due to the few studies included. Risk ratio (RR) Undernutrition may increase the risk of TB disease (RR 1.95, 95% CI 1.72 to 2.20; prediction interval 1.49 to 2.55; 4 studies; 1,475,867 participants). Stratification by follow-up was not possible as all studies had a follow-up of < 10 years. The certainty of the evidence is low due to the high risk of bias. Contour-enhanced funnel plots were not reported due to the few studies included. Undernutrition probably increases the risk of TB two-fold in the short term (< 10 years) and may also increase the risk in the long term (> 10 years). Policies targeted towards the reduction of the burden of undernutrition are not only needed to alleviate human suffering due to undernutrition and its many adverse consequences, but are also an important part of the critical measures for ending the TB epidemic by 2030. Large population-based cohorts, including those derived from high-quality national registries of exposures (undernutrition) and outcomes (TB disease), are needed to provide high-certainty estimates of this risk across different settings and populations, including low and middle-income countries from different WHO regions. Moreover, studies including children and adolescents and state-of-the-art methods for diagnosing TB would provide more up-to-date information relevant to practice and policy. World Health Organization (203256442). PROSPERO registration: CRD42023408807 Protocol: https://doi.org/10.1002/14651858.CD015890.

Franco JV ，Bongaerts B ，Metzendorf MI ，Risso A ，Guo Y ，Peña Silva L ，Boeckmann M ，Schlesinger S ，Damen JA ，Richter B ，Baddeley A ，Bastard M ，Carlqvist A ，Garcia-Casal MN ，Hemmingsen B ，Mavhunga F ，Manne-Goehler J ，Viney K ... -  《Cochrane Database of Systematic Reviews》

Dynamic Field Theory of Executive Function: Identifying Early Neurocognitive Markers.

In this Monograph, we explored neurocognitive predictors of executive function (EF) development in a cohort of children followed longitudinally from 30 to 54 months of age. We tested predictions of a dynamic field model that explains development in a benchmark measure of EF development, the dimensional change card sort (DCCS) task. This is a rule-use task that measures children's ability to switch between sorting cards by shape or color rules. A key developmental mechanism in the model is that dimensional label learning drives EF development. Data collection began in February 2019 and was completed in April 2022 on the Knoxville campus of the University of Tennessee. Our cohort included 20 children (13 female) all of whom were White (not Hispanic/Latinx) from an urban area in southern United States, and the sample annual family income distribution ranged from low to high (most families falling between $40,000 and 59,000 per year (note that we address issues of generalizability and the small sample size throughout the monograph)). We tested the influence of dimensional label learning on DCCS performance by longitudinally assessing neurocognitive function across multiple domains at 30 and 54 months of age. We measured dimensional label learning with comprehension and production tasks for shape and color labels. Simple EF was measured with the Simon task which required children to respond to images of a cat or dog with a lateralized (left/right) button press. Response conflict was manipulated in this task based on the spatial location of the stimulus which could be neutral (central), congruent, or incongruent with the spatial lateralization of the response. Dimensional understanding was measured with an object matching task requiring children to generalize similarity between objects that matched within the dimensions of color or shape. We first identified neural measures associated with performance and development on each of these tasks. We then examined which of these measures predicted performance on the DCCS task at 54 months. We measured neural activity with functional near-infrared spectroscopy across bilateral frontal, temporal, and parietal cortices. Our results identified an array of neurocognitive mechanisms associated with development within each domain we assessed. Importantly, our results suggest that dimensional label learning impacts the development of EF. Neural activation in left frontal cortex during dimensional label production at 30 months of age predicted EF performance at 54 months of age. We discussed these results in the context of efforts to train EF with broad transfer. We also discussed a new autonomy-centered EF framework. The dynamic field model on which we have motivated the current research makes decisions autonomously and various factors can influence the types of decisions that the model makes. In this way, EF is a property of neurocognitive dynamics, which can be influenced by individual factors and contextual effects. We also discuss how this conceptual framework can generalize beyond the specific example of dimensional label learning and DCCS performance to other aspects of EF and how this framework can help to understand how EF unfolds in unique individual, cultural, and contextual factors. Measures of EF during early childhood are associated with a wide range of development outcomes, including academic skills and quality of life. The hope is that broad aspects of development can be improved by implementing interventions aimed at facilitating EF development. However, this promise has been largely unrealized. Previous work on EF development has been limited by a focus on EF components, such as inhibition, working memory, and switching. Similarly, intervention research has focused on practicing EF tasks that target these specific components of EF. While performance typically improves on the practiced task, improvement rarely generalizes to other EF tasks or other developmental outcomes. The current work is unique because we looked beyond EF itself to identify the lower-level learning processes that predict EF development. Indeed, the results of this study identify the first learning mechanism involved in the development of EF. Although the work here provides new targets for interventions in future work, there are also important limitations. First, our sample is not representative of the underlying population of children in the United States under the age of 5. This is a problem in much of the existing developmental cognitive neuroscience research. We discussed challenges to the generalizability of our findings to the population at large. This is particularly important given that our theory is largely contextual, suggesting that children's unique experiences with learning labels for visual dimensions will impact EF development. Second, we identified a learning mechanism to target in future intervention research; however, it is not clear whether such interventions would benefit all children or how to identify children who would benefit most from such interventions. We also discuss prospective lines of research that can address these limitations, such as targeting families that are typically underrepresented in research, expanding longitudinal studies to examine longer term outcomes such as school-readiness and academic skills, and using the dynamic field (DF) model to systematically explore how exposure to objects and labels can optimize the neural representations underlying dimensional label learning. Future work remains to understand how such learning processes come to define the contextually and culturally specific skills that emerge over development and how these skills lay the foundation for broad developmental trajectories.

McCraw A ，Sullivan J ，Lowery K ，Eddings R ，Heim HR ，Buss AT ... -  《-》