H2O2 Photosynthesis from H2O and O2 under Weak Light by Carbon Nitrides with the Piezoelectric Effect.

Driven by the essential need of a green, safe, and low-cost approach to producing H2O2, a highly valuable multifunctional chemical, artificial photosynthesis emerges as a promising avenue. However, current catalyst systems remain challenging, due to the need of high-density sunlight, poor selectivity and activity, or/and unfavorable thermodynamics. Here, we reported that an indirect 2e- water oxidation reaction (WOR) in photocatalytic H2O2 production was unusually activated by C5N2 with piezoelectric effects. Interestingly, under ultrasonication, C5N2 exhibited an overall H2O2 photosynthesis rate of 918.4 µM/h and an exceptionally high solar-to-chemical conversion efficiency of 2.6% after calibration under weak light (0.1 sun). Mechanism studies showed that the piezoelectric effect of carbon nitride overcame the high uphill thermodynamics of *OH intermediate generation, which enabled a new pathway for 2e- WOR, the kinetic limiting step in the overall H2O2 production from H2O and O2. Benefiting from the outstanding sonication-assisted photocatalytic H2O2 generation under weak light, the concept was further successfully adapted to biomedical applications in efficient sono-photochemodynamic therapy for cancer treatment and water purification.

Analysis of the characteristics, efficiency, and influencing factors of third-party mediation mechanisms for resolving medical disputes in public hospitals in China.

BACKGROUND: Medical disputes, which are prevalent in China, are a growing global public health problem. The Chinese government has proposed third-party mediation (TPM) to resolve this issue. However, the characteristics, efficiency, and influencing factors of TPM in resolving medical disputes in public hospitals in China have yet to be determined. METHODS: We conducted a systematic study using TPM records from medical disputes in Gansu Province in China from 2014 to 2019. A χ2 test was used to compare differences between groups, and binary logistic analysis was performed to determine the factors influencing the choice of TPM for resolving medical disputes. RESULTS: We analyzed 5,948 TPM records of medical disputes in Gansu Province in China. The number of medical disputes and the amount of compensation awarded in public hospitals in the Gansu Province increased annually from 2014 to 2019, with most of the disputes occurring in secondary and tertiary hospitals. Approximately 89.01% of the medical disputes were handled by TPM; the average compensation amount with TPM was Chinese Yuan (CNY) 48,688.73, significantly less than that awarded via court judgment and judicial mediation. TPM was more likely to succeed in settling medical disputes in the < CNY10,000 compensation group than in the no-compensation group (odds ratio [OR] = 3.14, 95% confidence interval [CI] 1.53-6.45). However, as the compensation amount increased, the likelihood of choosing TPM decreased significantly. Moreover, TPM was less likely to be chosen when medical disputes did not involve death (OR = 0.49, 95% CI 0.36-0.45) or when no-fault liability was determined (vs. medical accidents; OR = 0.37, 95% CI 0.20-0.67). CONCLUSION: Our findings demonstrate that TPM mechanisms play a positive role in efficiently reducing compensation amounts and increasing medical dispute resolution rates which was the main settlement method in resolving medical disputes in public hospitals of Gansu Province in China. TPM could help greatly reduce conflicts between doctors and patients, avoid litigation, and save time and costs for both parties. Moreover, compensation amounts, non-fatal outcomes, and no-fault liability determinations influence the choice of TPM for settling medical disputes.

Construction of a Risk Prediction Model for the Occurrence of Acute Skin Failure in Critically Ill Patients: A Prospective Study.

BACKGROUND: The risk factors for acute skin failure (ASF), a serious complication of the skin, are not fully understood. PURPOSE: This study was designed to explore the risk factors for ASF in critically ill patients and construct a clinical prediction model. METHODS: Intensive care unit patients were prospectively observed and assigned into two groups: with and without ASF. A logistic regression model was constructed, and its predictive power and clinical utility were evaluated. RESULTS: Of the 204 eligible patients enrolled as participants, 58 (28.43%) developed ASF. Sepsis, vasoactive drugs, and age were shown to be risk factors for ASF, whereas peripheral perfusion index ratio and albumin level were shown to be protective factors. The area under the receiver operating characteristic curve was 0.83. The maximum Youden index of the model was 0.39 (specificity: 0.87, sensitivity: 0.77). The Hosmer-Lemeshow test (p = .20) and calibration curve showed good fitness and predictive utility with respect to the model. CONCLUSIONS: The developed model effectively predicts ASF risk, allowing for the early identification of high-risk patients. Identifying risk factors such as sepsis, vasoactive drugs, and age and considering protective factors such as peripheral perfusion index and albumin levels may help optimize care plans. Clinical staff should pay special attention to these factors and their impact on skin health in critically ill patients.

Metal-ligand dual-site single-atom nanozyme mimicking urate oxidase with high substrates specificity.

In nature, coenzyme-independent oxidases have evolved in selective catalysis using isolated substrate-binding pockets. Single-atom nanozymes (SAzymes), an emerging type of non-protein artificial enzymes, are promising to simulate enzyme active centers, but owing to the lack of recognition sites, realizing substrate specificity is a formidable task. Here we report a metal-ligand dual-site SAzyme (Ni-DAB) that exhibited selectivity in uric acid (UA) oxidation. Ni-DAB mimics the dual-site catalytic mechanism of urate oxidase, in which the Ni metal center and the C atom in the ligand serve as the specific UA and O2 binding sites, respectively, characterized by synchrotron soft X-ray absorption spectroscopy, in situ near ambient pressure X-ray photoelectron spectroscopy, and isotope labeling. The theoretical calculations reveal the high catalytic specificity is derived from not only the delicate interaction between UA and the Ni center but also the complementary oxygen reduction at the beta C site in the ligand. As a potential application, a Ni-DAB-based biofuel cell using human urine is constructed. This work unlocks an approach of enzyme-like isolated dual sites in boosting the selectivity of non-protein artificial enzymes.

Regulating Reactive Oxygen Intermediates of Fe-N-C SAzyme via Second-Shell Coordination for Selective Aerobic Oxidation Reactions.

Reactive oxygen species (ROS) regulation for single-atom nanozymes (SAzymes), e.g., Fe-N-C, is a key scientific issue that determines the activity, selectivity, and stability of aerobic reaction. However, the poor understanding of ROS formation mechanism on SAzymes greatly hampers their wider deployment. Herein, inspired by cytochromes P450 affording bound ROS intermediates in O2 activation, we report Fe-N-C containing the same FeN4 but with tunable second-shell coordination can effectively regulate ROS production pathways. Remarkably, compared to the control Fe-N-C sample, the second-shell sulfur functionalized Fe-N-C delivered a 2.4-fold increase of oxidase-like activity via the bound Fe=O intermediate. Conversely, free ROS (⋅O2 -) release was significantly reduced after functionalization, down to only 17 % of that observed for Fe-N-C. The detailed characterizations and theoretical calculations revealed that the second-shell sulfur functionalization significantly altered the electronic structure of FeN4 sites, leading to an increase of electron density at Fermi level. It enhanced the electron transfer from active sites to the key intermediate *OOH, thereby ultimately determining the type of ROS in aerobic oxidation process. The proposed Fe-N-Cs with different second-shell anion were further applied to three aerobic oxidation reactions with enhanced activity, selectivity, and stability.

[Construction and external validation of a risk prediction model for unplanned interruption during continuous renal replacement therapy].

OBJECTIVE: To identify the independent factors of unplanned interruption during continuous renal replacement therapy (CRRT) and construct a risk prediction model, and to verify the clinical application effectiveness of the model. METHODS: A retrospective study was conducted on critically ill adult patients who received CRRT treatment in the intensive care unit (ICU) of Zhejiang Hospital from January 2021 to August 2022 for model construction. According to whether unplanned weaning occurred, the patients were divided into two groups. The potential influencing factors of unplanned CRRT weaning in the two groups were compared. The independent influencing factors of unplanned CRRT weaning were screened by binary Logistic regression and a risk prediction model was constructed. The goodness of fit of the model was verified by a Hosmer-Lemeshow test and its predictive validity was evaluated by receiver operator characteristic curve (ROC curve). Then embed the risk prediction model into the hospital's ICU multifunctional electronic medical record system for severe illness, critically ill patients with CRRT admitted to the ICU of Zhejiang Hospital from November 2022 to October 2023 were prospectively analyzed to verify the model's clinical application effect. RESULTS: (1) Model construction and internal validation: a total of 331 critically ill patients with CRRT were included to be retrospectively analyzed. Among them, there were 238 patients in planned interruption group and 93 patients in unplanned interruption group. Compared with the planned interruption group, the unplanned interruption group was shown as a lower proportion of males (80.6% vs. 91.6%) and a higher proportion of chronic diseases (60.2% vs. 41.6%), poor blood purification catheter function (31.2% vs. 6.3%), as a higher platelet count (PLT) before CRRT initiation [×109/L: 137 (101, 187) vs. 109 (74, 160)], lower level of blood flow rate [mL/min: 120 (120, 150) vs. 150 (140, 180)], higher proportion of using pre-dilution (37.6% vs. 23.5%), higher filtration fraction [23.0% (17.5%, 32.9%) vs. 19.1% (15.7%, 22.6%)], and frequency of blood pump stops [times: 19 (14, 21) vs. 9 (6, 13)], the differences of the above 8 factors between the two groups were statistically significant (all P < 0.05). Binary Logistic regression analysis showed that chronic diseases [odds ratio (OR) = 3.063, 95% confidence interval (95%CI) was 1.200-7.819], blood purification catheter function (OR = 4.429, 95%CI was 1.270-15.451), blood flow rate (OR = 0.928, 95%CI was 0.900-0.957), and frequency of blood pump stops (OR = 1.339, 95%CI was 1.231-1.457) were the independent factors for the unplanned interruption of CRRT (all P < 0.05). These 4 factors were used to construct a risk prediction model, and ROC curve analysis showed that the area under the curve (AUC) predicted by the model was 0.952 (95%CI was 0.930-0.973, P = 0.003 0), with a sensitivity of 88.2%, a specificity of 89.9%, and a maximum value of 1.781 for the Youden index. (2) External validation: prospective inclusion of 110 patients, including 63 planned interruption group and 47 unplanned interruption group. ROC curve analysis showed that the AUC of the risk prediction model was 0.919 (95%CI was 0.870-0.969, P = 0.004 3), with a sensitivity of 91.5%, a specificity of 79.4%, and a maximum value of the Youden index of 1.709. CONCLUSIONS: The risk prediction model for unplanned interruption during CRRT has a high predictive efficiency, allowing for rapid and real-time identification of the high risk patients, thus providing references for preventative nursing.

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems.

Timescale correlation of shallow trap states increases electrochemiluminescence efficiency in carbon nitrides.

Highly efficient interconversion of different types of energy plays a crucial role in both science and technology. Among them, electrochemiluminescence, an emission of light excited by electrochemical reactions, has drawn attention as a powerful tool for bioassays. Nonetheless, the large differences in timescale among diverse charge-transfer pathways from picoseconds to seconds significantly limit the electrochemiluminescence efficiency and hamper their broad applications. Here, we report a timescale coordination strategy to improve the electrochemiluminescence efficiency of carbon nitrides by engineering shallow electron trap states via Au-N bond functionalization. Quantitative electrochemiluminescence kinetics measurements and theoretic calculations jointly disclose that Au-N bonds endow shallow electron trap states, which coordinate the timescale of the fast electron transfer in the bulk emitter and the slow redox reaction of co-reagent at diffusion layers. The shallow electron trap states ultimately accelerate the rate and kinetics of emissive electron-hole recombination, setting a new cathodic electrochemiluminescence efficiency record of carbon nitrides, and empowering a visual electrochemiluminescence sensor for nitrite ion, a typical environmental contaminant, with superior detection range and limit.

Recent progress on charge transfer engineering in reticular framework for efficient electrochemiluminescence.

Electrochemiluminescence (ECL) is a luminescence production technique triggered by electrochemistry, which has emerged as a powerful analytical technique in bioanalysis and clinical diagnosis. During ECL, charge transfer (CT) is an important process between electrochemical excitation and luminescent emission, and dramatically affects the efficiency of exciton generation, playing a pivotal role in the light-emitting properties of nanomaterials. Reticular framework materials with intramolecular/intermolecular interactions offer a promising platform for regulating CT pathways and enhancing luminescence efficiency. Deciphering the role of intramolecular/intermolecular CT processes in reticular framework materials allows for the targeted design and synthesis of emitters with precisely controlled CT properties. This sheds light on the microscopic mechanisms of electro-optical conversion in ECL, propelling advancements in their efficiency and breakthrough applications. This mini-review focuses on recent advancements in engineering CT within reticular frameworks to boost ECL efficiency. We summarized strategies including intra-reticular charge transfer, CT between the metal and ligands, and CT between guest molecules and frameworks within reticular frameworks, which holds promise for developing next-generation ECL devices with enhanced sensitivity and light emission.

The effect of different resistance and assistance loads on 30-m sprint kinematics.

Resisted sprint and assisted sprint are the two main types of training methods used by athletes in sprint training, so optimizing resisted sprint training and assisted sprint training process is beneficial for improving athletes' sprint performance. Kinematics is the most intuitive parameter that reflects the quality of training during running process, and it is particularly important to analyze the gait of athletes during resisted and assisted sprint process. Therefore, this paper investigates the effects of resisted and assisted sprint on the sprint kinematics of sprinters in the first 30 meters to demonstrate the targeted effects of resisted and assisted sprint training. The experimental results show that compared to the unloaded running, male collegiate sprinters increase their total step count, decrease their step length, increase their step time, increase their contact time, whereas have almost no change in the flight time when performing the 30-m resisted sprint. Male collegiate sprinters decrease their total step count, increase their step length, increase their step time, decrease their contact time and increase their flight time, when performing the 30-m assisted sprint. In addition, it is found that resisted sprint training is beneficial for improving the athletes' power and explosiveness during the acceleration phase, thereby improving acceleration ability. However, prolonged and frequent resisted sprint training may reduce the step length and step frequency of athletes. Assisted sprint training is beneficial for shortening the contact time of athletes, improving their step length and flight time, and enabling them to overspeed, thereby increasing their maximum speed ability.

Football referee gesture recognition algorithm based on YOLOv8s.

Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees' gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model's attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.

Quantifying the effects of ice hockey upper body pads on mobility and comfort.

Ice hockey is a high-intensity sport in which pads such as shoulder and elbow pads (S/EPs) are necessary to help players avoid injury. However, they can also affect mobility and comfort, thereby affecting players' on-ice performance. We aimed to quantify the effects of S/EPs on mobility and comfort by comparing the range of motion (ROM) of nine elite college-level ice hockey players performing static (nine single-DOF upper-body movements) and dynamic (wrist and slap shots) tasks under six pad conditions (no S/EPs and five types of S/EPs). We also analyzed the relationship between ROM and subjective comfort to provide an objective comfort evaluation of hockey pads. Five types of S/EPs restrict ROM at different levels, imposing additional mobility restrictions. We found significant differences among the five types and a high correlation between comfort and ROM. We conducted a comprehensive evaluation of the impact of ice hockey pads on mobility and comfort.

Validity and test-retest reliability of a resistance training device for Smith machine back squat exercise.

This study evaluated the validity and test-retest reliability of a resistance training device Jueying (Beijing, China) for Smith machine back squat exercise. Twelve male participants completed two test sessions with an interval of one week. In each test session, participants completed 30%, 45%, 60%, and 75% of 1RM back squats on a Smith machine equipped with Jueying and a linear position transducer GymAware (Canberra, Australia), which measured the velocity and power during the movement simultaneously. Results showed that Jueying was both valid (Pearson correlation coefficient [r] = 0.896-0.999, effect size [ES] = 0.004-0.192) when compared with GymAware and consistent between two tests in terms of reliability (intraclass correlation coefficient [ICC] = 0.79-0.95) to assess speed and power within all exercises. The device could be applied to provide athletes and coaches with effective and reliable data in actual application.

Markerless vision-based functional movement screening movements evaluation with deep neural networks.

The functional movement screen (FMS) test is a seven-test battery used to assess fundamental movement abilities of individuals. It is commonly used to predict sports injuries but relies on clinical expertise and is not suitable for self-examination. This study presents an automatic FMS movement assessment framework using a multi-view deep neural network called MVDNN. The framework combines automatic skeleton extraction with manual feature selection to extract 3D trajectory features of human skeleton joints from two different directions. Three mainstream methods of time-series modeling are then used to learn high-level feature representation from skeleton sequences, and motion features from two views are fused to provide complementary information. Results of public FMS movements dataset demonstrate that our MVDNN outperforms current state-of-the-art methods with an average miF1 score of 0.857, maF1 score of 0.768, and Kappa score of 0.640 over ten runs.

Supramolecular assembly-induced electrochemiluminescence enhancement of gold nanoclusters for hemoglobin detection.

Gold nanoclusters (Au NCs) with excellent optical properties and biocompatibility have become one of the most promising electrochemiluminescence (ECL) emitters. However, the low efficiency and poor stability of Au NCs restrict their applications in ECL. Herein, by supramolecular assembly of L-arginine (Arg) and 4-hydroxy-2-mercapto-6-methylpyrimidine (MTU) on the surface of Au NCs, Arg/MTU-Au NCs with enhanced ECL efficiency and stability were prepared. Compared with the MTU-stabilized Au NCs (MTU-Au NCs), the ECL efficiency of Arg/MTU-Au NCs increased by 24.8 times. As a proof-of-concept, a sensitive biosensing platform was constructed for sensitive detection of hemoglobin (Hb) in urine using Arg/MTU-Au NCs as ECL emitters. The proposed ECL detection platform provides a feasible strategy for the detection of biomarkers in urine and has broad application prospects in disease screening and clinical marker detection.

Multi-organ spatiotemporal information aware model for sepsis mortality prediction.

BACKGROUND: Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems. These constraints hinder a full application of such models, particularly when dealing with limited clinical data. To surmount these challenges, a comprehensive model, denoted as recurrent Graph Attention Network-multi Gated Recurrent Unit (rGAT-mGRU), was proposed. Taking into account the intricate spatiotemporal interactions among multiple organ systems, the model predicted in-hospital mortality of sepsis using data collected within the 48-hour period post-diagnosis. MATERIAL AND METHODS: Multiple parallel GRU sub-models were formulated to investigate the temporal physiological variations of single organ systems. Meanwhile, a GAT structure featuring a memory unit was constructed to capture spatiotemporal connections among multi-organ systems. Additionally, an attention-injection mechanism was employed to govern the data flowing within the network pertaining to multi-organ systems. The proposed model underwent training and testing using a dataset of 10,181 sepsis cases extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. To evaluate the model's superiority, it was compared with the existing common baseline models. Furthermore, ablation experiments were designed to elucidate the rationale and robustness of the proposed model. RESULTS: Compared with the baseline models for predicting mortality of sepsis, the rGAT-mGRU model demonstrated the largest area under the receiver operating characteristic curve (AUROC) of 0.8777 ± 0.0039 and the maximum area under the precision-recall curve (AUPRC) of 0.5818 ± 0.0071, with sensitivity of 0.8358 ± 0.0302 and specificity of 0.7727 ± 0.0229, respectively. The proposed model was capable of delineating the varying contribution of the involved organ systems at distinct moments, as specifically illustrated by the attention weights. Furthermore, it exhibited consistent performance even in the face of limited clinical data. CONCLUSION: The rGAT-mGRU model has the potential to indicate sepsis prognosis by extracting the dynamic spatiotemporal interplay information inherent in multi-organ systems during critical diseases, thereby providing clinicians with auxiliary decision-making support.

The Impact of high-performance work system perceived by medical staff on job satisfaction: the mediating role of self-efficacy.

As the mainstay of healthcare, the job satisfaction of medical staff deserves attention. This study aimed to explore the correlation between the perception of the high-performance work system (P-HPWS) and job satisfaction of medical staff in public hospitals and to further investigate the mediating effect of self-efficacy. From November 2019 to January 2020, a cross-sectional survey on working doctors and nurses was conducted in five tertiary public hospitals in China. A total of 520 participants were surveyed. The P-HPWS, job satisfaction, and self-efficacy were assessed using the 25-item self-administered scale, six-item job satisfaction questionnaire, and the General Self-Efficacy Scale, respectively. Linear regression and mediation effects models were used to identify the associations between primary variables. The results showed a significant positive correlation between P-HPWS and job satisfaction (P < 0.01), while self-efficacy played a mediating role between P-HPWS and job satisfaction. This finding reveals the benefits of improving employees' P-HPWS and self-efficacy on their job satisfaction, and that hospitals can improve their management systems by implementing and refining HPWS.

Effect of performance determinants on nhl final goal difference / Impacto de los determinantes de desempeño en la diferencia final de goles en la nhl / Efeito dos determinantes de desempenho na diferença final de gols da nhl

ABSTRACT Introduction: In ice hockey games, the team's performance is influenced by many contextual factors, and understanding playing styles allows to reveal how key performance indicators vary under different situations. Objective: This research aims to explore the playing styles of elite ice-hockey teams and to identify key performance aspects under different final goal difference situations. Methods: This article analyzed compared the match performance of 31 National Hockey League teams during 1271 matches considering their playing styles and final goal difference. Results: The principal component analysis obtained 8 performance components describing the technical-tactical styles of the teams. The subsequent analysis found that there was significant difference between three match outcomes in unfavorable state, major penalties, puck possession maintaining ability, shot defending ability, aggressive performance (p<0.001; = 0.007-0.273). Conclusions: Higher-ranked teams winning the unbalanced games showed better performance in shot defending ability and aggressive performance. Lower-ranked teams losing in unbalanced games kept less possession of the puck and were more likely to be shorthanded (p<0.05, ES=0.131-1.410). The study demonstrates how playing styles can be used to contextualize key determinants from ice hockey games. Level of evidence I; Therapeutic Studies Investigating the Results of Treatment.

RESUMEN Introducción: En los juegos de hockey sobre hielo, el rendimiento del equipo está influenciado por varios factores contextuales, y comprender los estilos de juego permite revelar cómo varían los indicadores clave de rendimiento en diferentes situaciones. Objetivo: Esta investigación tiene como objetivo explorar los estilos de juego de los equipos de hockey sobre hielo de élite e identificar aspectos clave del rendimiento en diferentes situaciones de diferencia de gol final. Métodos: El rendimiento del partido de 31 equipos de la Liga Nacional de Hockey durante 1271 partidos fue analizado y comparado, considerando el estilo de juego y la diferencia de gol final. Resultados: El análisis de componentes principales obtuvo 8 componentes de rendimiento que describen los estilos técnico-tácticos de los equipos. El análisis posterior encontró que hubo una diferencia significativa entre tres resultados de partido en estado desfavorable, penalizaciones principales, habilidad para mantener la posesión del disco, habilidad para defender el tiro, desempeño agresivo (p<0,001; = 0,007-0,273). Conclusión: Los equipos de clasificación más alta que ganaron los juegos desequilibrados mostraron un mejor rendimiento en la capacidad de defensa de disparos y en el rendimiento agresivo. Los equipos de clasificación más baja que perdieron en juegos desequilibrados mantuvieron menos posesión del disco y tenían más probabilidades de estar en desventaja numérica (p<0,05, ES=0,131-1,410). El estudio demuestra cómo los estilos de juego pueden utilizarse para contextualizar los determinantes clave de los juegos de hockey sobre hielo. Nivel de Evidencia I; Estudios Terapéuticos que Investigan los Resultados del Tratamiento.

RESUMO Introdução: Nos jogos de hóquei no gelo, o desempenho da equipe é influenciado por vários fatores contextuais, e entender os estilos de jogo permite revelar como os principais indicadores de desempenho variam em diferentes situações. Objetivo: Esta pesquisa tem como objetivo explorar os estilos de jogo das equipes de hóquei no gelo de elite e identificar aspectos-chave de desempenho em diferentes estilos de jogo e a diferença do resultado final. Métodos: O desempenho de partida de 31 equipes da National Hockey League durante 1271 partidas foi analisado e comparado, considerando o estilo de jogo e a diferença de gol final. Resultados: A análise de componentes principais retornou 8 componentes de desempenho, descrevendo os estilos técnico-táticos das equipes. A análise subsequente revelou que houve diferença significativa entre três resultados de jogo em estado desfavorável, penalidades principais, habilidade de manter a posse do disco, habilidade de defender o lance e desempenho agressivo (p<0,001; = 0,007-0,273). Conclusão: As equipes de classificação mais alta que venceram os jogos em desequilíbrio numérico de jogadores apresentaram melhor desempenho na habilidade de defender o lance e no desempenho agressivo. As equipes de classificação mais baixa, que perderam em jogos desequilibrados, mantiveram menos posse do disco e tiveram maior probabilidade de ficar com um jogador a menos (p <0,05, ES = 0,131-1,410). O estudo demonstra como os estilos de jogo podem ser usados para contextualizar os principais determinantes dos jogos de hóquei no gelo. Nível de Evidência I; Estudos Terapêuticos Investigação dos Resultados do Tratamento.

Identifying tumor cell-released extracellular vesicles as biomarkers for breast cancer diagnosis by a three-dimensional hydrogel-based electrochemical immunosensor.

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.