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Objective: Recent studies suggest that boys enter puberty at a younger age, and the incidence of male central precocious puberty (CPP) is increasing. In this study, we explore the incidence of male CPP and identify key clinical and auxological indicators for organic CPP (OCPP)., Design: A retrospective registry-based study., Methods: The medical records of 43 boys treated with CPP at the Helsinki University Hospital between 1985 and 2014 were reviewed. Clinical, auxological, and endocrine data of the CPP patients were included in the analyses., Results: Based on brain MRI, 26% of patients had OCPP. Between 2010 and 2014, the CPP incidence in boys was 0.34 per 10 000 (95% CI 0.20-0.60). Between 1990 and 2014, the male CPP incidence increased (incidence rate ratio [IRR] 1.10, P = .001). This increase was driven by rising idiopathic CPP (ICPP) incidence (IRR 1.11, 95% CI 1.05-1.19, P < .001), while OCPP incidence remained stable (P = .41). Compared with the patients with ICPP, the patients with OCPP were younger (P = .006), were shorter (P = .003), and had higher basal serum testosterone levels (P = .038). Combining 2 to 4 of these readily available clinical cues resulted in good to excellent (all, area under the curve 0.84-0.97, P < .001) overall performance, differentiating organic etiology from idiopathic., Conclusions: The estimated incidence of CPP in boys was 0.34 per 10 000, with 26% of cases associated with intracranial pathology. The increase in CPP incidence was driven by rising ICPP rates. Patients with OCPP were characterized by shorter stature, younger age, and higher basal testosterone levels, providing valuable cues for differentiation in addition to brain MRI. Utilizing multiple cues could guide diagnostic decision-making., Competing Interests: Conflict of interest: The authors have no conflict of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Endocrinology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

A combined pretreatment based on alkaline deep eutectic solvent (DES) of K 2 CO 3 and glycerol and sequential acid fractionation was developed to extract reactive lignin from wheat straw biomass. This process exhibited excellent purification performance in lignin isolation, and the lignin fractionated at low pH displayed high reactivity, having hydroxyl and carboxyl groups up to 9.60 and 2.52 mmol/g, respectively. Silica was selectively separated and removed during the precipitation stage, avoiding the "silica interference". Moreover, DES-lignin nanospheres created by self-assembly using lignin fractions obtained by acid precipitation possessed a high zeta potential, large particle size and high content of hydrophilic groups. Overall, the findings related to the dissociation mechanism and fractionation of reactive lignin during alkaline DES pretreatment and the acid sequence precipitation are crucial for facilitating lignin valorization in high-added value products., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background and Objective: Emergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown., Methods: We extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric., Results: Calendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant., Conclusions: Our study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables., (© 2022. The Author(s).)