The radiative efficiency and global warming potential of HCFC-132b.

Hydro-chloro-fluoro-carbons (HCFCs) are potent greenhouse gases which strongly absorb the infrared (IR) radiation within the 8 - 12 µm atmospheric windows. Despite international policies schedule their phasing out by 2020 for developed countries and 2030 globally, HCFC-132b (CH2ClCClF2) has been recently detected with significant atmospheric concentration. In this scenario, detailed climate metrics are of paramount importance for understanding the capacity of anthropogenic pollutants to contribute to global warming. In this work, the radiative efficiency (RE) of HCFC-132b is experimentally measured for the first time and used to determine its global warming potential (GWP) over 20-, 100- and 500-year time horizon. Vibrational- and rotational-spectroscopic properties of this molecule are first characterized by exploiting a synergism between Fourier-transform IR (FTIR) spectroscopy experiments and quantum chemical calculations. Equilibrium geometry, rotational parameters and vibrational properties predicted theoretically beyond the double-harmonic approximation, are employed to assist the vibrational assignment  of the experimental trace. Finally, FTIR spectra measured over a range of pressures are used to determine HCFC-132b absorption cross section spectrum from 150 to 3000 cm-1, from which istantaneous and effective REs are derived and, in turn, used for GWP evaluation.

Coping with extreme heat: current exposure and implications for the future.

A preview of how effective behavioral, biological and technological responses might be in the future, when outdoor conditions will be at least 2°C hotter than current levels, is available today from studies of individuals already living in extreme heat. In areas where high temperatures are common-particularly those in the hot and humid tropics-several studies report that indoor temperatures in low-income housing can be significantly hotter than those outdoors. A case study indicates that daily indoor heat indexes in almost all the 123 slum dwellings monitored in Kolkata during the summer were above 41°C (106°F) for at least an hour. Economic constraints make it unlikely that technological fixes, such as air conditioners, will remedy conditions like these-now or in the future. People without access to air conditioning will have to rely on behavioral adjustments and/or biological/physiological acclimatization. One important unknown is whether individuals who have lived their entire lives in hot environments without air conditioning possess natural levels of acclimatization greater than those indicated by controlled laboratory studies. Answering questions about the future will require more studies of heat conditions experienced by individuals, more information on indoor versus outdoor heat conditions, and a greater understanding of the behavioral and biological adjustments made by people living today in extremely hot conditions.

Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.

This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We evaluate Bayesian Neural Networks (BNN) and the Deep Neural Network with UQ (DNN with UQ) techniques, including Monte Carlo dropout, Ensemble Bayesian Neural Network (EBNN), Ensemble Monte Carlo (EMC) dropout, across different evaluation metrics. Our analysis reveals that DNN with UQ, especially EBNN and EMC dropout, consistently outperform BNNs. For example, in Class 0 vs. All, EBNN achieved a UAcc of 92.6%, UAUC-ROC of 95.0%, and a Brier Score of 0.157, significantly surpassing BNN's performance. Similarly, EMC Dropout excelled in Class 1 vs. All with a UAcc of 83.5%, UAUC-ROC of 95.8%, and a Brier Score of 0.165. These advanced models demonstrated higher accuracy, better discriaminative capability, and more accurate probabilistic predictions. Our findings highlight the efficacy of DNN with UQ in enhancing model reliability and interpretability, making them highly suitable for critical healthcare applications like chest X-ray imageQ6 classification.

Artificial intelligence for predicting mortality in hospitalized COVID-19 patients.

Background: The global demographic situation has been significantly impacted by the COVID-19 pandemic. The objective of this study was to develop a model that predicts the risk of COVID-associated mortality using clinical and laboratory data collected within 72 h of hospital admission. Materials and methods: A total of 3024 subjects with PCR-confirmed COVID-19 were admitted to Almazov National Research Medical Center between May 2020 and August 2021. Among them, 6.25% (n = 189) of patients had a fatal outcome. Five machine learning models and the Boruta-SHAP feature selection method were utilized to assess the risk of mortality during COVID-19 hospitalization. Results: All methods demonstrated high efficacy, with ROC AUC (Receiver Operating Characteristic Area Under the Curve) values exceeding 80%. The selected Boruta-SHAP features, when incorporated into the random forest model, achieved an ROC AUC of 93.1% in the validation. Conclusion: Throughout the study, close collaboration with healthcare professionals ensured that the developed tool met their practical needs. The success of our model validates the potential of machine learning techniques as decision support systems in clinical practice.

A Bibliometric Analysis of 100 Top-cited Articles on Cleft Orthodontics.

OBJECTIVE: This bibliometric study seeks to provide a comprehensive overview of the 100 most frequently cited articles in the domain of cleft orthodontics. The analysis will reveal key influential publications, collaborative author networks, and identify prevailing research themes within the field. METHOD: The studies related to Orthodontics in the realm of cleft lip and palate (CLP) were retrieved from the Scopus database on 30th August 2024 using key terms. The results obtained were sorted in descending order of citations and the 100 top-cited articles were hand-filtered. RStudio software version 4.2.0 and Bibliometrix R-package was used for performing scientometrics involving co-citation, co-occurrence, collaboration and co-word analyses, bibliographic coupling and network mapping. RESULTS: A total of n = 3984 articles were retrieved from which top-100 cited articles were filtered. These documents were published during 1950-2019 with peak production in 1997. The United States and the Netherlands were the most prolific countries involved in the given research. The majority of the highly referenced articles pertained to alveolar bone grafting, and treatment outcomes being the second common focus followed by Infant and early orthopedics (Naso-alveolar moulding, Maxillary Protraction) and facial growth during the given period. CONCLUSIONS: Thematic mapping depicted bone grafting, alveoloplasty (infant orthopedics) and maxillofacial development as the more developed core topics than the psychology and self-perception of patients with CLP. Recent research trends have shifted towards three-dimensional assessment methods.

Long-term effects of transition milk feeding on feed intake, growth performance, feeding behavior, and oxidative status of Holstein calves.

This study investigated the long-term effects of feeding 5-d transition milk (TRANS) compared with milk replacer (MR) on parameters, including intake, growth, feeding behavior and oxidative stress. Fifty Holstein calves (30 females and 20 males) were fed either 12 L/day TRANS or MR for the first 5 d after an initial colostrum feeding of 3.5 L. Thereafter, all calves were fed with 12 L of MR/d (140 g/L) and were gradually weaned starting in wk 8 until wk 14. Throughout the 14 wk the calves had unrestricted access to concentrate (up to 9.8 kg/calf/day), hay, and water. After weaning all heifers were fed a total mixed ration for young cows. Oxidative status was assessed in blood samples from birth to first insemination. Parameters assessed included the ferric reducing ability of plasma (FRAP) for antioxidant capacity and the concentration of reactive oxygen metabolites by the dROM (detection of reactive oxygen metabolites) assay. In addition, the activity of glutathione peroxidase (GSH-Px), oxidative damage in the form of lipid peroxidation as thiobarbituric acid reactive substances (TBARS) and as advanced oxidation protein products (AOPP) were measured. An oxidative stress index (OSi) was calculated: dROM/FRAP x 100. Total protein (TP) concentration was also quantified via the Bradford assay. The only significant difference in feeding behavior between the 2 treatment groups was a higher concentrate intake by the TRANS calves during the weaning phase. Body weight and ADG did not differ significantly between the TRANS and MR groups. TRANS calves showed a trend for fewer cases of health disorders. Markers of oxidative status, including TBARS, AOPP, GSHPx, FRAP and ROM, showed no treatment effects but varied significantly over time. Of note, the oxidative stress index as ratio between pro- and antioxidants in both groups peaked during weaning and then returned to baseline, suggesting an effective response to this transition phase Overall, the results indicate that feeding TRANS during the first 5 d of life had no long-term effect on the parameters studied as compared with MR feeding under the present rearing conditions. These results provide insight into the changes of oxidative status with age and confirm that the relatively high milk feeding level, slow and late weaning enables calves to adapt well to solely solid feed.

From wearables to performance: how acceptance of IoT devices influences physical education results in college students.

With the advent of IoT technology in education, understanding its impact on physical education is crucial. This study investigates how the acceptance of wearable IoT devices influences the physical education results of college freshmen. It posits that user acceptance plays a mediating role in the effectiveness of these devices in enhancing physical performance metrics. The study enrolled 150 first-year students from Guangzhou University of Finance, divided equally into an experimental group and a control group. Participants in the experimental group were provided with 'Xiaomi 8' smart bracelets to be worn during physical education classes. The study spanned six weeks, focusing on assessing various physical performance metrics and the acceptance of the wearable technology. The data analysis involved comparing the physical performance of both groups and conducting regression analyses to evaluate the mediation effect of acceptance. Results indicated significant improvements in physical performance metrics in the experimental group, as evidenced by the Standardized Mean Differences (SMD). Notably, enhancements were observed in short-distance speed and aerobic endurance. The direct impact of wearable IoT devices on physical performance accounted for 66.4% variance, which increased to 84.1% upon incorporating acceptance as a mediator. These findings suggest that the effectiveness of wearable technology in physical education is significantly influenced by students' acceptance. The study concludes that wearable IoT devices can effectively enhance physical education outcomes among college students, with user acceptance playing a crucial mediating role. This underscores the importance of considering user acceptance in the integration of technology in educational settings. The findings provide valuable insights for educators and technologists in designing and implementing technology-integrated curricula.

Linking animal and human health burden: challenges and opportunities.

Quantifying the impact of poor animal health outcomes on human health represents a complex challenge. Using the disability-adjusted life year (DALY) metric as an endpoint, this article discusses how animal health outcomes can impact humans through three key processes: directly through zoonotic disease, indirectly via changes in yields and their impacts on nutrition and wealth, and finally, through indirect features associated with the agricultural industry, such as pharmaceuticals and climate change. For each process, the current state of the art and feasibility of global DALY-associated estimates are discussed. Existing frameworks for zoonoses already consider some key pathogens; ensuring completeness in the pathogens considered and consistency in methodological decisions is an important next step. For diet, risk factor frameworks enable a calculation of attributable DALYs; however, significant economic methodological developments are needed to ensure that local production changes are appropriately mapped to both local and global changes in dietary habits. Concerning wealth-related impacts, much work needs to be done on method development. Industry-related impacts require a focus on key research topics, such as attribution studies for animal antimicrobial resistance contributing to human outcomes. For climate change, a critical next step is identifying to what extent associated industry emissions are amenable to change should animal health outcomes improve. Allocation of finite funds to improve animal health must also consider the downstream impact on humans. Leveraging DALYs enables comparisons with other human health-related decisions and would represent a transformative way of approaching animal health decision-making should the obstacles in this article be addressed and new methods be developed.

La quantification de l'impact des problèmes de santé animale sur la santé humaine constitue un défi d'une grande complexité. En se servant de l'indicateur des années de vie ajustées sur l'incapacité (DALY) comme critère d'évaluation, les auteurs examinent trois processus essentiels illustrant l'impact que la situation zoosanitaire peut avoir sur la santé humaine : impact direct résultant des maladies zoonotiques, impact indirect résultant des mauvaises performances des animaux et de leurs conséquences sur la nutrition et la création de richesses, et enfin, effets indirects résultant de facteurs en lien avec le secteur agricole, par exemple l'utilisation de produits pharmaceutiques et le changement climatique. Pour chacun de ces processus, les auteurs font le point sur l'état actuel des connaissances et sur l'applicabilité des évaluations mondiales basées sur l'indicateur DALY. Les cadres existants relatifs aux zoonoses recouvrent déjà certains agents pathogènes majeurs ; la prochaine étape importante consistera à assurer une couverture complète des agents pathogènes et à veiller à la cohérence des décisions méthodologiques. S'agissant de l'alimentation, les cadres basés sur l'analyse des facteurs de risque permettent de calculer les DALY imputables à l'alimentation ; toutefois, d'importantes avancées méthodologiques sur les aspects économiques de cette corrélation seront nécessaires pour s'assurer que tout changement intervenant localement en matière de production animale est correctement mis en correspondance avec les modifications des habitudes alimentaires dans ce même contexte local mais aussi à l'échelle mondiale. S'agissant des impacts liés à la création de richesses, il reste beaucoup à faire dans le domaine méthodologique. La détermination des impacts liés aux filières d'élevage requiert des travaux axés sur des sujets précis, par exemple des études visant à déceler les sources de la résistance aux agents antimicrobiens qui contribuent à l'apparition d'antibiorésistances chez l'être humain. Enfin, pour ce qui concerne le changement climatique, une étape cruciale consistera à déterminer dans quelle mesure les émissions associées à l'élevage sont susceptibles de changer en cas d'amélioration de la situation zoosanitaire. Dans un contexte de ressources limitées, l'affectation de fonds à l'amélioration de la santé animale doit également prendre en compte l'impact en aval sur la santé humaine. L'utilisation de l'indicateur DALY permet des comparaisons avec d'autres décisions de santé publique et représenterait une approche transformative de la prise de décision en santé animale, dès lors que les obstacles mentionnés dans cet article sont surmontés et que de nouvelles méthodes sont mises au point.

Cuantificar el impacto de una mala sanidad animal en la salud humana es un desafío complejo. Utilizando el parámetro de años de vida ajustados en función de la discapacidad (AVAD o DALY) como criterio de valoración, en este artículo se examina cómo la sanidad animal puede repercutir en los seres humanos a través de tres procesos clave: directamente, a través de las zoonosis; indirectamente, a través de cambios en los rendimientos y sus repercusiones en la nutrición y la riqueza; y, por último, a través de factores indirectos asociados a la industria agropecuaria, como los fármacos y el cambio climático. Para cada uno de estos procesos, se examinan el estado actual y la viabilidad de estimar AVAD a escala mundial. Los marcos existentes para la zoonosis ya tienen en cuenta algunos patógenos claves; garantizar la exhaustividad de los patógenos considerados y la coherencia en las decisiones metodológicas es un próximo paso importante. En lo que respecta a la alimentación, aunque los marcos de factores de riesgo permiten calcular los AVAD atribuibles, se necesitan importantes avances metodológicos en el ámbito económico para asegurar que los cambios en la producción local se correspondan adecuadamente con los cambios locales y mundiales en los hábitos alimentarios. En cuanto a las repercusiones en la riqueza, queda mucho trabajo por hacer en el desarrollo de métodos. Para abordar las repercusiones relacionadas con la industria, es necesario centrarse en temas clave de investigación, como los estudios de atribución relativos al impacto en la salud humana de la resistencia a los antimicrobianos en los animales. En lo que se refiere al cambio climático, un próximo paso crucial es determinar en qué medida las emisiones de la industria podrían cambiar, en función de la mejora de los resultados en materia de sanidad animal. Al asignar fondos limitados para la mejora de la sanidad animal también se deben tener en cuenta las repercusiones correspondientes en los seres humanos. Utilizar los AVAD permite hacer comparaciones con otras decisiones importantes relacionadas con la salud humana y representaría una forma transformadora de enfocar la toma de decisiones en materia de sanidad animal, en caso de que se aborden los obstáculos presentados en ese artículo y se desarrollen nuevos métodos.

Cardiac Surgical Bleeding, Transfusion and Quality Metrics: Joint Consensus Statement by the Enhanced Recovery After Surgery Cardiac Society and Society for the Advancement of Patient Blood Management.

BACKGROUND: Excessive perioperative bleeding is associated with major complications in cardiac surgery, resulting in increased morbidity, mortality and cost. METHODS: An international expert panel was convened to develop consensus statements on the control of bleeding and management of transfusion, and to suggest key quality metrics for cardiac surgical bleeding. The panel reviewed relevant literature from the previous 10 years and used a modified RAND Delphi methodology to achieve consensus. RESULTS: Thirty consensus statements in 8 categories were developed, including prioritizing control of bleeding, pre-chest closure checklists and the need for additional quality indicators beyond re-exploration rate, such as time to re-exploration. Consensus was also reached on the need for a universal definition of excessive bleeding, the use of antifibrinolytics, optimal cessation of antithrombotic agents, and preoperative risk scoring based on patient and procedural factors to identify those at greatest risk of excessive bleeding. Furthermore, there is a need for an objective bleeding scale based on the volume and rapidity of blood loss accompanied by viscoelastic management algorithms and standardized, patient-centered blood management strategies reflecting an interdisciplinary approach to quality improvement. CONCLUSIONS: Prioritizing the timely control and management of bleeding is essential to improving patient outcomes in cardiac surgery. To this end, a cardiac surgical bleeding quality metric that is more comprehensive than re-exploration rate alone is needed. Similarly, interdisciplinary quality initiatives that seek to implement enhanced quality indicators will likely lead to improved patient care and outcomes.

A Volunteer Passion: A Qualitative Look at How We Measure and Reward the Work of Medical Educators.

Background  Medical educators face many challenges, including the absence of defined roles, lack of standard career paths, and limited support in systems that generally prioritize research and clinical productivity over educational activities. Providers also teach to widely varying degrees. This study was designed to specifically examine the professional rewards and obstacles experienced by physicians who have dedicated significant energy and career focus to medical education. Methodology A phenomenological approach was used in this qualitative study. Purposeful sampling was utilized to identify medical educators from different institutions and geographical areas. Participants were categorized by gender and career stage. Semi-structured interviews were conducted, and reflexive thematic analysis was used to develop themes across items and participants. Results Twenty-two medical educators were interviewed (11 males, 11 females), with an average age of 51 (range: 38-72) years. The average time from completion of training was 18 years (range: <1 to 41 years). Two main themes were constructed, which related to medical educators' career motivations and challenges: (1) Joy and purpose (subthemes: Interaction with learners, Impact, and Innovation) and (2) Everyone teaches (subthemes: Lack of recognition, Lack of reward, Malalignment of metrics) Conclusions The greatest source of motivation and satisfaction for medical educators is linked to the work itself; in addition to interactions with learners, educators derive pleasure from the innovation, collaboration, and systems thinking involved in their work. Importantly, participants also experience dissatisfaction, primarily due to a lack of recognition and reward, and metrics that do not consistently demonstrate their achievements. Participants provided examples of metrics that more accurately reflected the work of education; they identified clear benefits of academic promotion; and they highlighted significant challenges in the promotional system. The implementation of appropriate systems of measurement and reward is needed to better support the work of medical educators. Our aim should be not only to increase opportunities for satisfaction but also to reduce factors that cause frustration and limit advancement.

A meta-analysis of factors influencing the inactivation of Shiga toxin-producing Escherichia coli O157:H7 in leafy greens.

Recent advancements in modeling suggest that microbial inactivation in leafy greens follows a nonlinear pattern, rather than the simple first-order kinetics. In this study, we evaluated 17 inactivation models commonly used to describe microbial decline and established the conditions that govern microbial survival on leafy greens. Through a systematic review of 65 articles, we extracted 530 datasets to model the fate of Shiga toxin-producing Escherichia coli O157:H7 on leafy greens. Various factor analysis methods were employed to evaluate the impact of identified conditions on survival metrics. A two-parameter model (jm2) provided the best fit to most of both natural and antimicrobial-induced persistence datasets, whereas the one-parameter exponential model provided the best fit to less than 20% of the datasets. The jm2 model (adjusted R2 = .89) also outperformed the exponential model (adjusted R2 = .58) in fitting the pooled microbial survival data. In the context of survival metrics, the model averaging approach generated higher values than the exponential model for >4 log reduction times (LRTs), suggesting that the exponential model may be overpredicting inactivation at later time points. The random forest technique revealed that temperature and inoculum size were common factors determining inactivation in both natural and antimicrobial-induced die-offs.. The findings show the limitations of relying on the first-order survival metric of 1 LRT and considering nonlinear inactivation in produce safety decision-making.

Potential use of Six Sigma metrics in the quality control review of hospital glucose meters.

Background and aim: This study applied Six Sigma metrics to facilitate the quality control (QC) review for hospital glucose meters. Materials and methods: QC data from a period of six months on all hospital glucose meters were extracted from the data management system. Sigma values for each meter at two QC levels were calculated and evaluated each month by combining the imprecision, the absolute bias between the meter mean and all-meter mean, and the standards from ISO 15179:2013. The effectiveness of using Sigma values in identifying meters with possible quality problems for further Levey-Jennings QC chart review was assessed. Results: More than 80 % of the meter's Sigma values within the six months were greater than 4 at either QC level. At the high QC level, twice as many Sigma values were below 4 than the low QC level. Including Sigma values 4, 3.5 or 3 in the criteria for the QC review reduced the number of chart review to 32.8 %, 11.2 % or 3.5 %, respectively. Conclusions: The majority of the glucose meters examined in this study demonstrated optimal Sigma values. The Sigma metrics-based approach could be a valuable tool to guide an effective QC review of glucose meters for quality improvement.

The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability.

Objective.The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).Approach.The analysed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5 min sessions of controlled breathing at three different frequencies: 6, 10 and 15 breaths min-1. CO2concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including phase-rectified signal averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy, approximate entropy, sample entropy, and fuzzy entropy, as well as time and frequency-domain measures (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV).Main results.Higher breathing rates resulted in a significant decrease in end-tidal CO2concentration (p< 0.001), accompanied by increases in both ABP (p <0.001) and heart rate (HR,p <0.001). A strong, linear decline in AC and DC (p <0.001 for both) was observed with increasing breathing rate. All entropy metrics increased with breathing frequency (p <0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (p <0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (p= 0.038 andp= 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.Significance.Alterations in CO2levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.

Overnight Sleep Staging Using Chest-Worn Accelerometry.

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Pitch-Tracking Metrics as a Predictor of Future Shoulder and Elbow Injuries in Major League Baseball Pitchers: A Machine-Learning and Game-Theory Based Analysis.

Background: Understanding interactions between multiple risk factors for shoulder and elbow injuries in Major League Baseball (MLB) pitchers is important to identify potential avenues by which risk can be reduced while minimizing impact on player performance. Purpose: To apply a novel game theory-based approach to develop a machine-learning model predictive of next-season shoulder and elbow injuries in MLB pitchers and use this model to understand interdependencies and interaction effects between the most important risk factors. Study Design: Case-control study; Level of evidence, 3. Methods: Pitcher demographics, workload measures, pitch-tracking metrics, and injury data between 2017 and 2022 were used to construct a database of MLB pitcher-years, where each item in the database corresponded to a pitcher's information and metrics for that year. An extreme gradient boosting machine-learning model was trained to predict next-season shoulder and elbow injuries utilizing Shapley additive explanation values to quantify feature importance as well as interdependencies and interaction effects between predictive variables. Results: A total of 3808 pitcher-years were included in this analysis; 606 (15.9%) of these involved a shoulder or elbow injury resulting in placement on the MLB injured list. Of the >65 candidate features (including workload, demographic, and pitch-tracking metrics), the most important contributors to predicting shoulder/elbow injury were increased: pitch velocity (all pitch types), utilization of sliders (SLs), fastball (FB) spin rate, FB horizontal movement, and player age. The strongest game theory interaction effects were that higher FB velocity did not alter a younger pitcher's predicted risk of shoulder/elbow injury versus older pitchers, risk of shoulder/elbow injury increased with the number of high-velocity pitches thrown (regardless of pitch type and in an additive fashion), and FB velocity <95 mph (<152.9 kph) demonstrated strong negative interaction effects with higher SL percentage, suggesting that the overall predicted risk of injury for pitchers throwing a high number of SLs could be attenuated by lower FB velocity. Conclusion: Pitch-tracking metrics were substantially more predictive of future injury than player demographics and workload metrics. There were many significant game theory interdependencies of injury risk. Notably, the increased risk of injury that was conferred by throwing with a high velocity was even further magnified if the pitchers were also older, threw a high percentage of SLs, and/or threw a greater number of pitches.

Exploring the impact of curcumin on osteoarthritis symptomatology: correlations and insights from a Bulgarian cohort.

INTRODUCTION: Osteoarthritis is a prevalent degenerative joint disorder associated with pain and functional impairment. Curcumin, a natural anti-inflammatory compound, has garnered attention for its potential therapeutic benefits in osteoarthritis management.

A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury.

Objective: To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) in patients with traumatic brain injury (TBI). Materials and Methods: We employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify critical features related to pneumonia. Five ML models-Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes Classifier (NB), and Support Vector Machine (SVC)-were developed and assessed using the training and validation datasets. The optimal model was selected based on its performance metrics and used to create a dynamic web-based nomogram. Results: In a cohort of 858 TBI patients, the HAP incidence was 41.02%. LR was determined to be the optimal model with superior performance metrics including AUC, accuracy, and F1-score. Key predictive factors included Age, Glasgow Coma Score, Rotterdam Score, D-dimer, and the Systemic Immune Response to Inflammation Index (SIRI). The nomogram developed based on these predictors demonstrated high predictive accuracy, with AUCs of 0.818 and 0.819 for the training and validation datasets, respectively. Decision curve analysis (DCA) and calibration curves validated the model's clinical utility and accuracy. Conclusion: We successfully developed and validated a high-performance ML algorithm to assess the risk of HAP in TBI patients. The dynamic nomogram provides a practical tool for real-time risk assessment, potentially improving clinical outcomes by aiding in early intervention and personalized patient management.

Exploring the dynamics of Women's field hockey performance: A comparative analysis of the impact of opponent quality between Olympic Games and Chinese National Games.

This is an observational retrospective study analyzed the performance of the Chinese women's national field hockey team during the 2020 Tokyo Olympics and 2021 National Games to assess the impact of opposition quality on performance. Game statistics were collected using notational analysis software for 76 Olympic and 40 National Games matches. Non-parametric Mann-Whitney U tests were used to compare tournaments. No significant differences existed for 35 out of 38 metrics, except Offense in 25-Yd Area (P = 0.013), Handball Style (P = 0.000) and Entry into Arc - Right Lane (P = 0.017). When exclusively considering Chinese national team's observations, superior National Games performance did emerge for Shot (P = 0.046), Goal from Short Corner (P = 0.044), Into the Arc (P = 0.046), Entry into Arc - Q3 (P = 0.009), Dribble into the Arc (P = 0.046), Handball Style into Arc (P = 0.041), Forehand Shot (P = 0.033), and Small Skill Shot (P = 0.014). The study underscores the influence of opposition quality on team performance, with a need for targeted improvements in arc penetration efficacy, conversion rates of shots to goals, and adaptation of tactical approaches against stronger defenses. The research points towards the need for strategic high-performance programs, improved domestic league quality, and a structured youth development system to elevate the overall standard of Chinese field hockey to achieve global competitiveness.

Maximal Analgesic Effect Attained by the Use of Objective Neurophysiological Measurements With Closed-Loop Spinal Cord Stimulation.

OBJECTIVES: Spinal cord stimulation (SCS) has been challenged by the lack of neurophysiologic data to guide therapy optimization. Current SCS programming by trial-and-error results in suboptimal and variable therapeutic effects. A novel system with a physiologic closed-loop feedback mechanism using evoked-compound action potentials enables the optimization of physiologic neural dose by consistently and accurately activating spinal cord fibers. We aimed to identify neurophysiologic dose metrics and their ranges that resulted in clinically meaningful treatment responses. MATERIALS AND METHODS: Subjects from 3 clinical studies (n = 180) with baseline back and leg pain ≥60 mm visual analog scale and physical function in the severe to crippled category were included. Maximal analgesic effect (MAE) was operationally defined as the greatest percent reduction in pain intensity or as the greatest cumulative responder score (minimal clinically important differences [MCIDs]) obtained within the first 3 months of SCS implant. The physiologic metrics that produced the MAE were analyzed. RESULTS: We showed that a neural dose regimen with a high neural dose accuracy of 2.8µV and dose ratio of 1.4 resulted in a profound clinical benefit to chronic pain patients (MAE of 79 ± 1% for pain reduction and 12.5 ± 0.4 MCIDs). No differences were observed for MAE or neurophysiological dose metrics between the trial phase and post-implant MAE visit. CONCLUSION: For the first time, an evidence-based neural dose regimen is available for a neurostimulation intervention as a starting point to enable optimization of clinical benefit, monitoring of adherence, and management of the therapy.