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Inpatient treatment for severe obesity: A retrospective cohort study in Brazil, comparing exposure variables in a secondary data analysis

Introduction: Very low-calorie diets with hospitalization have demonstrated promise as a viable therapeutic option for severe obesity and its associated comorbidities. However, large studies providing a comprehensive longitudinal observation of patients undergoing this therapy are lacking. We evaluated the effectiveness of treating severe obesity in hospitalized patients, using very low-calorie diets and clinical support to develop lifestyle changes. Methods: This study was a retrospective cohort comparing exposure variables in a secondary data analysis with a pre-post treatment design. Data were obtained from medical records of patients with severe obesity (grade II or III) treated in a Brazilian obesity specialist hospital from 2016 to 2022. The patients underwent a very low-calorie diet (500–800 kCal/day) and immersive changes in lifestyle habits, monitored by a multidisciplinary team. At 3 months, 777 patients presented complete data and 402 presented complete data at 6 months. The study compared changes in bioimpedance and laboratory tests, between men and women and age groups. Results: Three months of hospitalization yielded significant reductions in weight, body mass index (BMI), body fat, skeletal muscle mass, glucose, inflammatory, and lipid parameters. These reductions were more pronounced after 6 months, nearly doubling those observed at 3 months. In women, BMI and fat mass reduced by 10.4% and 15.2% at 3 months and 20.4% and 31.3% at 6 months, respectively. In men, BMI and fat mass decreased by 12.9% and 25.3 at 3 months and 23.6% and 45.3% at 6 months, respectively. Elderly individuals (aged ≥ 60 years) had smaller reductions in BMI and fat mass than non-elderly individuals (aged < 60 years) but still presented significant improvements. Conclusion: This study suggests the viability of treating severe obesity by hospitalization with low-calorie diets and immersive lifestyle changes. This treatment modality significantly improves anthropometric measurements, glucose, lipids, and inflammatory markers, thereby reducing cardiovascular risk.

Contribution of paranasal sinus, chest, and abdomen/pelvis computed tomography in patients with febrile neutropenia

Objective Febrile neutropenia (FN) is a serious clinical event, associated with significant morbidity and mortality. Imaging has a central role in the identification of the fever cause. The study objectives were to assess abnormalities of potential infectious origin on paranasal sinus, chest and abdomen/pelvis CT scans performed during an episode of FN, in patients with or without specific clinical signs, and to evaluate their impact on the frequency of changes in treatment. Materials and methods This retrospective study included 306 patients with FN from a single academic center between October 2018 and December 2019. Clinical and biological data, final clinical diagnosis for the FN episode, and change in treatment following CT scans were collected from medical records. CT images were reviewed for positive findings in each anatomical region. Results Overall, 188 patients (61.4%) had at least one positive CT finding on paranasal sinus, chest and/or abdomen/pelvis regions, and 153 patients (50.0%) received a final clinical diagnosis of infection, based on microbiological and/or radiological findings. There were more chest and abdomen/pelvis CT positive findings in patients with specific clinical signs than in patients without (65.7% vs. 25.8%, and 59.0% vs. 22.7% respectively, p <0.001), with a higher frequency of therapeutic modifications in positive CTs. There were significantly more paranasal sinus CT abnormalities in symptomatic patients (33.3% vs. 8.5%, p = 0.03), but without any impact on treatment. Conclusion These results support recommendations to perform systematic chest CT in patients with FN and may encourage the widespread use of abdomen/pelvis CT in this indication, even without symptoms. However, the actual impact of their use on the prognosis of the patients remains to be evaluated.

Exploring happiness factors with explainable ensemble learning in a global pandemic

Happiness is a state of contentment, joy, and fulfillment, arising from relationships, accomplishments, and inner peace, leading to well-being and positivity. The greatest happiness principle posits that morality is determined by pleasure, aiming for a society where individuals are content and free from suffering. While happiness factors vary, some are universally recognized. The World Happiness Report (WHR), published annually, includes data on ‘GDP per capita’, ‘social support’, ‘life expectancy’, ‘freedom to make life choices’, ‘generosity’, and ‘perceptions of corruption’. This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. We also show the impact of COVID-19 pandemic on the happiness features. We design two ensemble ML and DL models using blending and stacking ensemble techniques, namely, Blending RGMLL, which combines Ridge Regression (RR), Gradient Boosting (GB), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Linear Regression (LR), and Stacking LRGR, which combines LR, Random Forest (RF), GB, and RR. Among the trained models, Blending RGMLL demonstrates the highest predictive accuracy with R2 of 85%, MSE of 0.15, and RMSE of 0.38. We employ Explainable Artificial Intelligence (XAI) techniques to uncover changes in happiness indices, variable importance, and the impact of the COVID-19 pandemic on happiness. The study utilizes an open dataset from the WHR, covering 156 countries from 2018 to 2023. Our findings indicate that ‘GDP per capita’ is the most critical indicator of happiness score (HS), while ‘social support’ and ‘healthy life expectancy’ are also important features before and after the pandemic. However, during the pandemic, ‘social support’ emerged as the most important indicator, followed by ‘healthy life expectancy’ and ‘GDP per capita’, because social support is the prime necessity in the pandemic situation. The outcome of this research helps people understand the impact of these features on increasing the HS and provides guidelines on how happiness can be maintain during unwanted situations. Future research will explore advanced methods and include other related features with real-time monitoring for more comprehensive insights.

The progression of basaltic–rhyolitic melt storage at Yellowstone Caldera

Predicting learning achievement using ensemble learning with result explanation

Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning achievement based on ensemble learning techniques. Specifically, six distinct machine learning models are utilized to establish a base learner, with logistic regression serving as the meta learner to construct an ensemble model for predicting learning achievement. The SHapley Additive exPlanation (SHAP) model is then employed to explain the prediction results. Through the experiments on XuetangX dataset, the effectiveness of the proposed model is verified. The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. The results demonstrate that the ensemble learning-based predictive framework significantly outperforms traditional machine learning methods. Through feature importance analysis, the SHAP method enhances model interpretability and improves the reliability of the prediction results, enabling more personalized interventions to support students.

High-resolution genomic history of early medieval Europe

AbstractMany known and unknown historical events have remained below detection thresholds of genetic studies because subtle ancestry changes are challenging to reconstruct. Methods based on shared haplotypes1,2 and rare variants3,4 improve power but are not explicitly temporal and have not been possible to adopt in unbiased ancestry models. Here we develop Twigstats, an approach of time-stratified ancestry analysis that can improve statistical power by an order of magnitude by focusing on coalescences in recent times, while remaining unbiased by population-specific drift. We apply this framework to 1,556 available ancient whole genomes from Europe in the historical period. We are able to model individual-level ancestry using preceding genomes to provide high resolution. During the first half of the first millennium ce, we observe at least two different streams of Scandinavian-related ancestry expanding across western, central and eastern Europe. By contrast, during the second half of the first millennium ce, ancestry patterns suggest the regional disappearance or substantial admixture of these ancestries. In Scandinavia, we document a major ancestry influx by approximately 800 ce, when a large proportion of Viking Age individuals carried ancestry from groups related to central Europe not seen in individuals from the early Iron Age. Our findings suggest that time-stratified ancestry analysis can provide a higher-resolution lens for genetic history.

Prognostic significance of total choline on in-vivo proton MR spectroscopy for prediction of late recurrence in patients with hormone receptor-positive, HER2-negative early breast cancer

Purpose In-vivo proton magnetic resonance spectroscopy (MRS) is a non-invasive method of analyzing choline metabolism that has been used to predict breast cancer prognosis. A strong choline peak may be a surrogate for aggressive tumor biology but its clinical relevance is unclear. The present study assessed whether total choline (tCho), as measured by proton MRS, can predict late recurrence in patients with hormone receptor (HR)-positive, HER2-negative early breast cancer. Methods The study cohort included 261 HR+/HER2- breast cancer patients who underwent diagnostic single-voxel proton MRS (3.0T scanner) prior to first-line surgery from March 2011 to July 2014. The relationships between tCho compound peak integral (tChoi) values and others prognostic factor were analyzed, as were the effects of tChoi on 10-year disease-free survival (DFS) and overall survival (OS). The clinical significance of tChoi was also analyzed using Harrell’s C-index. Results Mean tChoi in HR+/HER2- study group was 15.47 and we set the cut-off for tChoi at 15 for survival analysis. 10-year DFS differed significantly between tChoi <15 and ≥15 (p = 0.017), with differences differing significantly for late (5–10 years; p = 0.02) but not early (0–5 years; p = 0.323) recurrence. Cox regression analysis showed that tChoi was significantly predictive of 10-year DFS (p = 0.046, OR 2.69) and tended to be predictive of late recurrence (HR 4.36, p = 0.066). Harrell’s C-index showed that the Ki-67 index (AUC = 0.597) and lymphovascular invasion (AUC = 0.545) were also predictive of survival, with the addition of normalized tChoi improving the AUC to 0.622 (p = 0.014), indicating better predictive power. Conclusion tChoi determined by in vivo MRS was predictive of prognosis in patients with HR+/HER2- early breast cancer. This parameter may serve as a valuable, non-invasive tool to predict prognosis when combined with other known prognostic factors.

Reply to: Limitations of ice cores in reconstructing temperature seasonality

The social welfare effect of e-commerce product reputation information asymmetry from the perspective of network externality

Reputation is the most important intangible asset of merchants. In the e-commerce platform market, reputation information has become an important signal of product quality. However, with increasingly fierce competition among merchants on these platforms, violations of reputation information, such as “click farming,” “cash rebate for favorable comments,” and “pay per click,” have caused information asymmetry and adverse selection. Based on the network externality perspective, considering the duopoly e-commerce platform market, this paper uses game theory to construct a theoretical model to compare and analyze the changes in consumers, merchants, platforms, and social total welfare when the reputation information of e-commerce products is symmetric and asymmetric. The research results show that when the reputation information of e-commerce products is symmetrical, the reputation mechanism of the e-commerce platform can play a positive role, the platform income decreases, and the consumer surplus and the total social welfare level increase. The increment increases with the increase in consumer-side network externality, and the e-commerce platform transfers part of the surplus value to consumers. Due to the influence of network externality, reputation information asymmetry, and violation penalty cost, reputation asymmetry decreases consumer, merchant, and total social welfare and increases platform profits, indicating that the e-commerce platform lacks the economic motivation to govern the violation of reputation information. We recommend that the healthy development of e-commerce platforms proceeds from three aspects: building a reputation mechanism for e-commerce platforms that is jointly supervised by e-commerce platforms, third-party institutions, and social organizations; increasing the cost of punishment for violations; and exerting platform network effects to enhance the competitiveness of enterprises.

Upconverting microgauges reveal intraluminal force dynamics in vivo

Measuring four facets of emotion beliefs in Germany: A German-language adaptation of the EBQ and its comparability across gender and different emotion abilities

Adaptive emotion regulation, involving the modulation of positive and negative emotions based on goals, is a crucial function for a person’s mental health and general well-being. Factors influencing successful emotion regulation include beliefs about emotions, such as the controllability and usefulness of emotions. The Emotion Beliefs Questionnaire (EBQ) was developed to assess these beliefs and has shown promise in predicting emotion regulation and psychopathology across different countries. This study aims to advance EBQ’s generalizability in measuring emotion beliefs by examining the scale’s different validities for a developed German version. In a German sample of 348 respondents, we show the scale’s factorial and broader construct validity as well as its factors’ reliability. Notably, we demonstrate that the German EBQ is mostly strictly measurement invariant across central sociodemographic variables like age and gender. Interestingly, we also find the scale to be robust across different levels of other psychological constructs such as emotional reactivity and efficacy.

Direct fabrication of lasers on silicon suggests solution to chip-production problem

Global prioritization schemes vary in their impact on the placement of protected areas

In response to global declines in biodiversity, many global conservation prioritization schemes were developed to guide effective protected area establishment. Protected area coverage has grown dramatically since the introduction of several high-profile biodiversity prioritization schemes, but the impact of such schemes on protected area establishment has not been evaluated. We used matching methods and a Before-After Control-Impact causal analysis to evaluate the impact of two key prioritization schemes—Biodiversity Hotspots and Last of the Wild—representing examples of the reactive and proactive ends of the prioritization spectrum. We found that Last of the Wild had a positive impact on the rate of protection in its identified priority areas, but Biodiversity Hotspots did not. Because Biodiversity Hotspots are in or near human-dominated landscapes, this scheme may have been unable to overcome biases towards protecting areas with little human pressure. In contrast, Last of the Wild aligned with the tendency to protect areas far from high human use and thus with lower implementation costs, and so received greater uptake. Stronger links between large-scale prioritizations and more locally driven implementation of area-based conservation, as well as other forms of conservation action, are needed to overcome practical constraints and effectively protect biodiversity on an increasingly human-dominated planet.

A pulsar-like polarization angle swing from a nearby fast radio burst

An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks

The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks. This study proposed a novel approach using the AWID 3 dataset for cyberattack detection. The retained features were analyzed to assess their transferability, creating a lightweight and cost-effective model. A decision tree with a recursive feature elimination method was implemented for the extraction of the reduced features subset, and an additional feature wlan_radio.signal_dbm was used in combination with the extracted feature subset. Several deep learning and machine learning models were implemented, where DT and CNN achieved promising classification results. Further, feature transferability and generalizability were evaluated, and their detection performance was analyzed across different network versions where CNN outperformed other classification models. The practical implications of this research are crucial for the secure automation of wireless intrusion detection frameworks and tools in personal and enterprise paradigms.

Magnetospheric origin of a fast radio burst constrained using scintillation

Correction: Psychosocial and sociodemographic factors associated with gestational blood glucose levels in women attending public hospitals: Results from baseline of MAASTHI cohort

GaAs nano-ridge laser diodes fully fabricated in a 300-mm CMOS pilot line

Impact of posterior septectomy on olfaction in endoscopic endonasal transsphenoidal surgery

Background Endoscopic endonasal transsphenoidal surgery is widely used to resect pituitary adenomas, yet its impact on olfactory function after resection of the posterosuperior nasal septum remains a concern. To optimize surgical techniques to preserve olfactory function, it is essential to understand the relationship between the extent of septal resection and olfactory outcomes. Methods This retrospective study analyzed 295 patients who underwent pituitary adenoma surgery. The extent of nasal septum resection was quantified and its impact on olfactory function was assessed using the Cross-Cultural Smell Identification Test (CCSIT), Sino-Nasal Outcome Test-22 (SNOT-22), and a Visual Analog Scale (VAS) for olfactory loss. Preoperative and 6-month postoperative scores were compared to evaluate changes in olfactory function. Results There was a significant correlation between larger septal resections and greater reductions in CCSIT scores, indicating a decline in olfactory function. Furthermore, patients with more extensive septal resections reported increased discomfort and olfactory loss, as evidenced by higher SNOT-22 and VAS scores. These findings highlight the importance of the nasal septum in maintaining laminar airflow and its role in olfactory function. Conclusion Study underscores the adverse effects of extensive posterior septectomy on olfactory outcomes. Minimizing the extent of septal resection may help preserve olfactory function, suggesting a need for surgical strategies that maintain septum integrity to reduce the risk of postoperative olfactory impairment.

Predicting prosthetic gait and the effects of induced stiff-knee gait

Prosthetic gait differs considerably from the unimpaired gait. Studying alterations in the gait patterns could help to understand different adaptation mechanisms adopted by these populations. This study investigated the effects of induced stiff-knee gait (SKG) on prosthetic and healthy gait patterns and the capabilities of predictive simulation. Self-selected speed gait of two participants was measured: one healthy subject and one knee disarticulation subject using a variable-damping microprocessor controlled knee prosthesis. Both performed unperturbed gait and gait with restricted knee flexion. Experimental joint angles and moments were computed using OpenSim and muscle activity was measured using surface electromyography (EMG). The differences between the conditions were analyzed using statistical parametric mapping (SPM). Predictive models based on optimal control were created to represent the participants. Additionally, a hypothetical unimpaired predictive model with the same anthropometric characteristics as the amputee was created. Some patterns observed in the experimental prosthetic gait were predicted by the models, including increased knee flexion moment on the contralateral side caused by SKG in both participants, which was statistically significant according to SPM. With the exception of the rectus femoris muscle, we also found overall good agreement between measured EMG and predicted muscle activation. We predicted more alterations in activation of the hip flexors than other muscle groups due to the amputation and in the activation of the biceps femoris short head, quadratus femoris, and tibialis anterior due to SKG. In summary, we demonstrated that the method applied in this study could predict gait alterations due to amputation of the lower limb or due to imposed SKG.