Predictors of transition to schizophrenia and other long-lasting non-affective psychoses in first-episode patients with acute and transient psychotic disorders: A validation study

Background and objectives Almost half of the individuals with a first-episode of psychosis who initially meet criteria for acute and transient psychotic disorder (ATPD) will have had a diagnostic revision during their follow-up, mostly toward schizophrenia. This study aimed to determine the proportion of diagnostic transitions to schizophrenia and other long-lasting non-affective psychoses in patients with first-episode ATPD, and to examine the validity of the existing predictors for diagnostic shift in this population. Methods We designed a prospective two-year follow-up study for subjects with first-episode ATPD. A multivariate logistic regression analysis was performed to identify independent variables associated with diagnostic transition to persistent non-affective psychoses. This prediction model was built by selecting variables on the basis of clinical knowledge. Results Sixty-eight patients with a first-episode ATPD completed the study and a diagnostic revision was necessary in 30 subjects at the end of follow-up, of whom 46.7% transited to long-lasting non-affective psychotic disorders. Poor premorbid adjustment and the presence of schizophreniform symptoms at onset of psychosis were the only variables independently significantly associated with diagnostic transition to persistent non-affective psychoses. Conclusion Our findings would enable early identification of those inidividuals with ATPD at most risk for developing long-lasting non-affective psychotic disorders, and who therefore should be targeted for intensive preventive interventions. (AU)

Factors associated with neurofeedback and mindfulness-based combination therapy for patients with substance use disorder: A multicenter study

Background and objectives Substance use disorder (SUD) has become a major concern in public health globally, and there is an urgent need to develop an integrated psychosocial intervention. The aims of the current study are to test the efficacy of the integrated treatment with neurofeedback and mindfulness-based therapy for SUD and identify the predictors of the efficacy. Methods This study included 110 participants with SUD into the analysis. Outcome of measures includes demographic characteristics, severity of dependence, quality of life, symptoms of depression, and anxiety. Independent t test is used to estimate the change of scores at baseline and three months follow-up. Generalized estimating equations are applied to analyze the effect of predictors on the scores of dependence severity over time by controlling for the effects of demographic characteristics. Results A total of 22 (20 %) participants were comorbid with major mental disorder (MMD). The decrement of the severity in dependence, anxiety, and depression after treatment are identified. Improved scores of qualities of life in generic, psychological, social, and environmental domains are also noticed. After controlling for the effects of demographic characteristics, the predictors of poorer outcome are comorbid with MMD, lower quality of life, and higher level of depression and anxiety. Conclusion The present study implicates the efficacy of integrated therapy. Early identification of predictors is beneficial for healthcare workers to improve the treatment efficacy. (AU)

Next Directions in the Neuroscience of Cancers Arising outside the CNS.

SUMMARY: The field of cancer neuroscience has begun to define the contributions of nerves to cancer initiation and progression; here, we highlight the future directions of basic and translational cancer neuroscience for malignancies arising outside of the central nervous system.

Validating claims-based definitions for deprescribing: Bridging the gap between clinical and administrative data.

BACKGROUND: Limited research has evaluated the validity of claims-based definitions for deprescribing. OBJECTIVES: Evaluate the validity of claims-based definitions of deprescribing against electronic health records (EHRs) for deprescribing of benzodiazepines (BZDs) after a fall-related hospitalization. METHODS: We used a novel data linkage between Medicare fee-for-service (FFS) and Part D with our health system's EHR. We identified patients aged ≥66 years with a fall-related hospitalization, continuous enrollment in Medicare FFS and Part D for 6 months pre- and post-hospitalization, and ≥2 BZD fills in the 6 months pre-hospitalization. Using a standardized EHR abstraction tool, we adjudicated deprescribing for a sub-sample with a fall-related hospitalization at UNC. We evaluated the validity of claims-based deprescribing definitions (e.g., gaps in supply, dosage reductions) versus chart review using sensitivity and specificity. RESULTS: Among 257 patients in the overall sample, 44% were aged 66-74 years, 35% had Medicare low-income subsidy, 79% were female. Among claims-based definitions using gaps in supply, the prevalence of BZD deprescribing ranged from 8.2% (no refills) to 36.6% (30-day gap). When incorporating dosage, the prevalence ranged from 55.3% to 65.8%. Among the validation sub-sample (n = 47), approximately one-third had BZDs deprescribed in the EHR. Compared to EHR, gaps in supply from claims had good sensitivity, but poor specificity. Incorporating dosage increased sensitivity, but worsened specificity. CONCLUSIONS: The sensitivity of claims-based definitions for deprescribing of BZDs was low; however, the specificity of a 90-day gap was >90%. Replication in other EHRs and for other low-value medications is needed to guide future deprescribing research.

Blood donation projections using hierarchical time series forecasting: the case of Zimbabwe's national blood bank.

BACKGROUND: The discrepancy between blood supply and demand requires accurate forecasts of the blood supply at any blood bank. Accurate blood donation forecasting gives blood managers empirical evidence in blood inventory management. The study aims to model and predict blood donations in Zimbabwe using hierarchical time series. The modelling technique allows one to identify, say, a declining donor category, and in that way, the method offers feasible and targeted solutions for blood managers to work on. METHODS: The monthly blood donation data covering the period 2007 to 2018, collected from the National Blood Service Zimbabwe (NBSZ) was used. The data was disaggregated by gender and blood groups types within each gender category. The model validation involved utilising actual blood donation data from 2019 and 2020. The model's performance was evaluated through the Mean Absolute Percentage Error (MAPE), uncovering expected and notable discrepancies during the Covid-19 pandemic period only. RESULTS: Blood group O had the highest monthly yield mean of 1507.85 and 1230.03 blood units for male and female donors, respectively. The top-down forecasting proportions (TDFP) under ARIMA, with a MAPE value of 11.30, was selected as the best approach and the model was then used to forecast future blood donations. The blood donation predictions for 2019 had a MAPE value of 14.80, suggesting alignment with previous years' donations. However, starting in April 2020, the Covid-19 pandemic disrupted blood collection, leading to a significant decrease in blood donation and hence a decrease in model accuracy. CONCLUSIONS: The gradual decrease in future blood donations exhibited by the predictions calls for blood authorities in Zimbabwe to develop interventions that encourage blood donor retention and regular donations. The impact of the Covid-19 pandemic distorted the blood donation patterns such that the developed model did not capture the significant drop in blood donations during the pandemic period. Other shocks such as, a surge in global pandemics and other disasters, will inevitably affect the blood donation system. Thus, forecasting future blood collections with a high degree of accuracy requires robust mathematical models which factor in, the impact of various shocks to the system, on short notice.

CLPREM: A real-time traffic prediction method for 5G mobile network.

Network traffic prediction is an important network monitoring method, which is widely used in network resource optimization and anomaly detection. However, with the increasing scale of networks and the rapid development of 5-th generation mobile networks (5G), traditional traffic forecasting methods are no longer applicable. To solve this problem, this paper applies Long Short-Term Memory (LSTM) network, data augmentation, clustering algorithm, model compression, and other technologies, and proposes a Cluster-based Lightweight PREdiction Model (CLPREM), a method for real-time traffic prediction of 5G mobile networks. We have designed unique data processing and classification methods to make CLPREM more robust than traditional neural network models. To demonstrate the effectiveness of the method, we designed and conducted experiments in a variety of settings. Experimental results confirm that CLPREM can obtain higher accuracy than traditional prediction schemes with less time cost. To address the occasional anomaly prediction issue in CLPREM, we propose a preprocessing method that minimally impacts time overhead. This approach not only enhances the accuracy of CLPREM but also effectively resolves the real-time traffic prediction challenge in 5G mobile networks.

Revisiting the IOM Reports and Envisioning a Promising Future for Public Health.

Two public health reports from the National Academies of Sciences, Engineering, and Medicine published in 1988 and 2003 by the Institute of Medicine continue to resonate. The COVID-19 pandemic highlighted the need for a robust and adequately funded public health system that has political and public support as well as strong connections to health care and other sectors. However, a spate of recent assessments of the nation's public health infrastructure shows continuing gaps in funding, workforce, capacity, and other dimensions. There are reasons for optimism and opportunities for progress in public health in the third decade of the 21st century. There is great promise in cross-sector partnerships and in embracing the "public" in public health by building power with communities in health improvement efforts and in decision-making. (Am J Public Health. 2024;114(5):495-500. https://doi.org/10.2105/AJPH.2024.307584).

The evolution of robotics: research and application progress of dental implant robotic systems.

The use of robots to augment human capabilities and assist in work has long been an aspiration. Robotics has been developing since the 1960s when the first industrial robot was introduced. As technology has advanced, robotic-assisted surgery has shown numerous advantages, including more precision, efficiency, minimal invasiveness, and safety than is possible with conventional techniques, which are research hotspots and cutting-edge trends. This article reviewed the history of medical robot development and seminal research papers about current research progress. Taking the autonomous dental implant robotic system as an example, the advantages and prospects of medical robotic systems would be discussed which would provide a reference for future research.

Psychological flexibility and cognitive-affective processes in young adults' daily lives.

Psychological flexibility plays a crucial role in how young adults adapt to their evolving cognitive and emotional landscapes. Our study investigated a core aspect of psychological flexibility in young adults: adaptive variability and maladaptive rigidity in the capacity for behavior change. We examined the interplay of these elements with cognitive-affective processes within a dynamic network, uncovering their manifestation in everyday life. Through an Ecological Momentary Assessment design, we collected intensive longitudinal data over 3 weeks from 114 young adults ages 19 to 32. Using a dynamic network approach, we assessed the temporal dynamics and individual variability in flexibility in relation to cognitive-affective processes in this sample. Rigidity exhibited the strongest directed association with other variables in the temporal network as well as highest strength centrality, demonstrating particularly strong associations to other variables in the contemporaneous network. In conclusion, the results of this study suggest that rigidity in young adults is associated with negative affect and cognitions at the same time point and the immediate future.

Unlocking the potential of forage fish to reduce the global burden of disease.

Red meat consumption is associated with an elevated risk of mortality from non-communicable diseases (NCDs). In contrast, forage fish, as highly nutritious, environmentally friendly, affordable, and the most abundant fish species in the ocean, are receiving increasing interest from a global food system perspective. However, little research has examined the impact of replacing red meat with forage fish in the global diet on diet-related NCDs. METHODS: We based our study on datasets of red meat projections in 2050 for 137 countries and forage fish catches. We replaced the red meat consumption in each country with forage fish (from marine habitats), without exceeding the potential supply of forage fish. We used a comparative risk assessment framework to investigate how such substitutions could reduce the global burden of diet-related NCDs in adults. RESULTS: The results of our study show that forage fish may replace only a fraction (approximately 8%) of the world's red meat due to its limited supply, but it may increase global daily per capita fish consumption close to the recommended level. Such a substitution could avoid 0.5-0.75 million deaths and 8-15 million disability-adjusted life years, concentrated in low- and middle-income countries. Forage fish as an alternative to red meat could double (or more) the number of deaths that could be avoided by simply reducing red meat consumption. CONCLUSIONS: Our analysis suggests that forage fish is a promising alternative to red meat. Policies targeting the allocation of forage fish to regions where they are needed, such as the Global South, could be more effective in maximising the potential of forage fish to reduce the global burden of disease.

Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC.

The intensification of intervention activities against the fatal vector-borne disease gambiense human African trypanosomiasis (gHAT, sleeping sickness) in the last two decades has led to a large decline in the number of annually reported cases. However, while we move closer to achieving the ambitious target of elimination of transmission (EoT) to humans, pockets of infection remain, and it becomes increasingly important to quantitatively assess if different regions are on track for elimination, and where intervention efforts should be focused. We present a previously developed stochastic mathematical model for gHAT in the Democratic Republic of Congo (DRC) and show that this same formulation is able to capture the dynamics of gHAT observed at the health area level (approximately 10,000 people). This analysis was the first time any stochastic gHAT model has been fitted directly to case data and allows us to better quantify the uncertainty in our results. The analysis focuses on utilising a particle filter Markov chain Monte Carlo (MCMC) methodology to fit the model to the data from 16 health areas of Mosango health zone in Kwilu province as a case study. The spatial heterogeneity in cases is reflected in modelling results, where we predict that under the current intervention strategies, the health area of Kinzamba II, which has approximately one third of the health zone's cases, will have the latest expected year for EoT. We find that fitting the analogous deterministic version of the gHAT model using MCMC has substantially faster computation times than fitting the stochastic model using pMCMC, but produces virtually indistinguishable posterior parameterisation. This suggests that expanding health area fitting, to cover more of the DRC, should be done with deterministic fits for efficiency, but with stochastic projections used to capture both the parameter and stochastic variation in case reporting and elimination year estimations.

Rigidez carotídea y riesgo de eventos vasculares y mortalidad: el estudio REGICOR / Carotid artery stiffness and risk of vascular events and mortality: the REGICOR study

Introducción y objetivos: Los objetivos son analizar la relación dosis-respuesta de la rigidez de la arteria carótida y la mortalidad y evaluar su capacidad predictiva. Métodos: Estudio de cohorte poblacional que incluyó a 6.468 participantes, con una mediana de seguimiento de 6,5 años. Se evaluaron 6 índices de rigidez. Se identificaron los eventos coronarios y cerebrovasculares y la mortalidad. Resultados: La rigidez carotídea, el coeficiente de Peterson y la velocidad de la onda de pulso (VOP) se asociaron de manera lineal y directa con los eventos cerebrovasculares: aumento del 8% (IC95%, 1-16%) por unidad de rigidez, del 7% (IC95%, 2-13%) cada 10 unidades del coeficiente de Peterson y del 26% (IC95%, 8-48%) por unidad de la VOP. La tensión carotídea se asoció de modo no lineal con el riesgo de enfermedad coronaria: en valores <0,09 unidades, cada aumento de 0,01 unidades se asoció con una disminución de un 16% del riesgo (IC95%, –33 a +6%); por encima de 0,09 unidades, cada incremento de 0,01 unidades se asoció con un aumento de un 16% del riesgo (IC95%, 6-27%). La inclusión de estos índices no mejoró la capacidad predictiva de las funciones de riesgo. Conclusiones: La rigidez carotídea, el coeficiente de elasticidad de Peterson y la VOP tienen una relación lineal y directa con el riesgo de enfermedad cerebrovascular. La tensión (strain) carotídea tiene una relación en U con el riesgo de enfermedad coronaria. Estos índices no contribuyen a mejorar la capacidad predictiva de las funciones de riesgo. (AU)

Introduction and objectives: The aims of this study were to determine the dose-response association of carotid arterial stiffness with vascular outcomes and overall mortality, and to assess their added predictive capacity. Methods: Population-based cohort study including 6468 individuals, with a median follow-up of 6.5 years. Six carotid artery stiffness indices were assessed: strain, stiffness, Peterson elasticity coefficient, compliance coefficient, distensibility coefficient, and pulse wave velocity (PWV). Incident coronary, cerebrovascular, global vascular, and total fatal events were identified. Results: Carotid compliance and distensibility coefficients were not associated with any of the outcomes. Carotid stiffness, Peterson elasticity coefficient, and PWV showed a direct linear relationship to cerebrovascular disease: the risk increased by 8% (95%CI, 1-16) per stiffness unit increase, by 7% (95%CI, 2-13) per 10-unit Peterson elasticity coefficient increase, and by 26% (95%CI, 8-48) per PWV unit increase. Carotid strain showed a nonlinear association with ischemic heart disease. When strain was ≤ 0.09 units, each 0.01-unit increase was associated with a 15% lower risk of coronary events (95%CI,−33 to 6); above 0.09 units, each 0.01 increase in strain was associated with a 16% higher risk of coronary events (95%CI, 6-27). The addition of the stiffness indices did not improve the predictive capacity of validated risk functions. Conclusions: Carotid stiffness, Peterson elasticity coefficient, and PWV have a direct linear association with cerebrovascular disease risk. Carotid strain is not linearly related to U-shaped ischemic heart disease risk. The inclusion of these indexes does not improve the predictive capacity of risk functions. (AU)

Brain activity of professional investors signals future stock performance.

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.