Giant hepatic hemangioma in a patient with cirrhosis: challenging to manage.

Giant hepatic hemangiomas are occasional in patients with cirrhosis. It remains a challenge to decide on the need for treatment and choose the most appropriate intervention. A 62-year-old woman was recently diagnosed with cirrhosis and complained of upper abdominal fullness, reduction in oral food intake, and weight loss of 6 kg over the last three years. Upper digestive endoscopy evidenced thin-caliber esophageal varices and significant extrinsic compression of the lesser gastric curvature. Abdominal computed tomography revealed an exophytic tumor in the left hepatic lobe, measuring 11.5 cm, which had progressive centripetal contrast enhancement from the arterial phase, compatible with hepatic hemangioma. Serum tumor markers were negative, and her liver function was unimpaired. The patient underwent surgical resection (non-anatomical hepatectomy of segments II and III) which had no immediate complications, and the histopathological evaluation confirmed cavernous hepatic hemangioma. Two weeks later, she was admitted to the emergency room with jaundice, signs of hepatic encephalopathy, and moderate ascites, and was further diagnosed with secondary bacterial peritonitis. As no perforations, abscesses, or fistulas were observed on subsequent imaging tests, clinical management was successfully carried out. This case highlights that giant hepatic hemangiomas may be symptomatic and warrant treatment. In the setting of cirrhosis and portal hypertension, physicians should be aware of the risk of hepatic decompensation following surgical resection, even in patients with Child-Pugh class A.

Complication Rates of Central Venous Catheters: A Systematic Review and Meta-Analysis.

Importance: Central venous catheters (CVCs) are commonly used but are associated with complications. Quantifying complication rates is essential for guiding CVC utilization decisions. Objective: To summarize current rates of CVC-associated complications. Data Sources: MEDLINE, Embase, CINAHL, and CENTRAL databases were searched for observational studies and randomized clinical trials published between 2015 to 2023. Study Selection: This study included English-language observational studies and randomized clinical trials of adult patients that reported complication rates of short-term centrally inserted CVCs and data for 1 or more outcomes of interest. Studies that evaluated long-term intravascular devices, focused on dialysis catheters not typically used for medication administration, or studied catheters placed by radiologists were excluded. Data Extraction and Synthesis: Two reviewers independently extracted data and assessed risk of bias. Bayesian random-effects meta-analysis was applied to summarize event rates. Rates of placement complications (events/1000 catheters with 95% credible interval [CrI]) and use complications (events/1000 catheter-days with 95% CrI) were estimated. Main Outcomes and Measures: Ten prespecified complications associated with CVC placement (placement failure, arterial puncture, arterial cannulation, pneumothorax, bleeding events requiring action, nerve injury, arteriovenous fistula, cardiac tamponade, arrhythmia, and delay of ≥1 hour in vasopressor administration) and 5 prespecified complications associated with CVC use (malfunction, infection, deep vein thrombosis [DVT], thrombophlebitis, and venous stenosis) were assessed. The composite of 4 serious complications (arterial cannulation, pneumothorax, infection, or DVT) after CVC exposure for 3 days was also assessed. Results: Of 11 722 screened studies, 130 were included in the analyses. Seven of 15 prespecified complications were meta-analyzed. Placement failure occurred at 20.4 (95% CrI, 10.9-34.4) events per 1000 catheters placed. Other rates of CVC placement complications (per 1000 catheters) were arterial canulation (2.8; 95% CrI, 0.1-10), arterial puncture (16.2; 95% CrI, 11.5-22), and pneumothorax (4.4; 95% CrI, 2.7-6.5). Rates of CVC use complications (per 1000 catheter-days) were malfunction (5.5; 95% CrI, 0.6-38), infection (4.8; 95% CrI, 3.4-6.6), and DVT (2.7; 95% CrI, 1.0-6.2). It was estimated that 30.2 (95% CrI, 21.8-43.0) in 1000 patients with a CVC for 3 days would develop 1 or more serious complication (arterial cannulation, pneumothorax, infection, or DVT). Use of ultrasonography was associated with lower rates of arterial puncture (risk ratio [RR], 0.20; 95% CrI, 0.09-0.44; 13.5 events vs 68.8 events/1000 catheters) and pneumothorax (RR, 0.25; 95% CrI, 0.08-0.80; 2.4 events vs 9.9 events/1000 catheters). Conclusions and Relevance: Approximately 3% of CVC placements were associated with major complications. Use of ultrasonography guidance may reduce specific risks including arterial puncture and pneumothorax.

Vision System for a Forestry Navigation Machine.

This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

Which Remote Rehabilitation Interventions Work Best for Chronic Musculoskeletal Pain and Depression? A Bayesian Network Meta-Analysis.

OBJECTIVE: To evaluate the effectiveness of remote rehabilitation interventions for people living with chronic musculoskeletal pain and depression. DESIGN: A systematic review with network meta-analysis (NMA) of randomized control trials. LITERATURE SEARCH: Cochrane Central Register of Controlled Trials, CINAHL, EMBASE, LILACS, MEDLINE, PSYNDEX and PsycINFO until May 2023. STUDY SELECTION CRITERIA: Randomized controlled trials that evaluated the effectiveness of remote rehabilitation interventions in people with chronic musculoskeletal pain and depression. DATA SYNTHESIS: We used Bayesian random-effects models for the NMA. Effect estimates were comparisons between rehabilitation interventions and waitlist. We performed a sensitivity analysis based on bias in the randomisation process, large trials (>100 patient per arm) and musculoskeletal condition. RESULTS: Fifty-eight randomized controlled trials involving 10,278 participants (median sample size: 137 (IQR: 77 to 236) were included. Interactive-voice-response cognitive behavioural therapy (CBT) (SMD -0.66, 95%CrI -1.17 to -0.16), CBT in person (SMD -0.50, 95%CrI -0.97 to -0.04) and mobile-app CBT plus exercise (SMD -0.37, 95% CrI -0.69 to -0.02) were superior to waitlist at 12-weeks follow-up for reducing pain (> 98% probability of superiority). For depression outcomes, Internet-delivered CBT & tele-care was superior to waitlist at 12-weeks follow-up (SMD -0.51, 95% CrI -0.87 to -0.13) (> 99% probability of superiority). For pain outcomes, the certainty of evidence ranged from low to moderate. For depression outcomes, the certainty of evidence ranged from very low to moderate. The proportion of dropouts attributed to adverse events was unclear. No intervention was associated with higher odds of dropout. CONCLUSION: Interactive-voice-response CBT, and mobile-app CBT plus exercise showed similar treatment effects with in-person CBT on pain reduction among people living with chronic musculoskeletal pain and had over 98% probability of superiority than waitlist control at 12-weeks follow-up. Internet-delivered CBT & tele-care had over 99% probability of superiority than waitlist control for improving depression outcomes at 12-weeks follow-up.

Effectiveness of Fludrocortisone Plus Hydrocortisone Versus Hydrocortisone Alone in Septic Shock: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

RATIONALE: The use of hydrocortisone in adult septic shock is controversial, and effectiveness of adding fludrocortisone to hydrocortisone remains uncertain. OBJECTIVE: To assess comparative effectiveness and safety of fludrocortisone plus hydrocortisone, hydrocortisone alone and placebo/usual care in adults with septic shock. METHODS: Systematic review and Bayesian network meta-analysis of peer-reviewed randomized trials. The primary outcome was all-cause mortality at last follow-up. Treatment effects were presented as relative risks (RR) with 95% credible intervals (CrI). Placebo/usual care was the reference treatment. MAIN RESULTS: Out of 7,553 references, we included 17 trials (7,688 patients). All-cause mortality at last follow-up was lowest with fludrocortisone plus hydrocortisone (RR: 0.85, 95% CrI: 0.72-0.99, 98.3% probability of superiority, moderate-certainty evidence), followed by hydrocortisone alone (RR: 0.97, 95% CrI: 0.87-1.07, 73.1% probability of superiority, low-certainty evidence). The comparison of fludrocortisone plus hydrocortisone versus hydrocortisone alone was primarily based on indirect evidence (only two trials with direct evidence). Fludrocortisone plus hydrocortisone was associated with a 12% lower risk of all-cause mortality compared to hydrocortisone alone (RR: 0.88, 95% CrI: 0.74-1.03, 94.2% probability of superiority, moderate-certainty evidence). CONCLUSIONS: In adult septic shock patients, fludrocortisone plus hydrocortisone was associated with lower risk of all-cause mortality at last follow-up than placebo and hydrocortisone alone. The scarcity of head-to-head trials comparing fludrocortisone plus hydrocortisone versus hydrocortisone alone led our network meta-analysis to rely primarily on indirect evidence for this comparison. Though we undertook several sensitivity analyses and assessments, these findings should be considered while also acknowledging the heterogeneity of included trials.

Efficient reactivation of anammox sludge after prolonged storage using a combination of batch and continuous reactors.

Due to the slow growth rate of anammox bacteria, enriched sludge is required for the rapid start-up of anammox-based reactors. However, it is still unclear if long-term stored anammox sludge (SAS) is an effective source of inoculum to accelerate reactor start-up. This study explored the reactivation of long-term SAS and developed an efficient protocol to reduce the start-up period of an anammox reactor. Although stored for 13 months, a low level of the specific anammox activity of 28 mg N/g VSS/d was still detected. Experimental Phase 1 involved the direct application of SAS to an upflow sludge bed reactor (USB) operated for 90 d under varying conditions of hydraulic retention time and nitrogen concentrations. In Phase 2, batch runs were executed prior to the continuous operation of the USB reactor. The biomass reactivation in the continuous flow reactor was unsuccessful. However, the SAS was effectively reactivated through a combination of batch runs and continuous flow feed. Within 75 days, the anammox process achieved a stable rate of nitrogen removal of 1.3 g N/L/day and a high nitrogen removal efficiency of 84.1 ± 0.2%. Anammox bacteria (Ca. Brocadia) abundance was 37.8% after reactivation. These overall results indicate that SAS is a feasible seed sludge for faster start-up of high-rate mainstream anammox reactors.

Sensor Integration in a Forestry Machine.

This paper presents the integration of multimodal sensor systems for an autonomous forestry machine. The utilized technology is housed in a single enclosure which consolidates a set of components responsible for executing machine control actions and comprehending its behavior in various scenarios. This sensor box, named Sentry, will subsequently be connected to a forestry machine from MDB, model LV600 PRO. The article outlines previous work in this field and then details the integration and operation of the equipment, integrated into the forest machine, providing descriptions of the adopted architecture at both the hardware and software levels. The gathered data enables the assessment of the forestry machine's orientation and position based on the information collected by the sensors. Finally, practical experiments are presented to demonstrate the system's behavior and to analyze the methods to be employed for autonomous navigation, thereby assessing the performance of the established architecture. The novel aspects of this work include the physical and digital integration of a multimodal sensor system on a forestry machine, its use in a real case scenario, namely, forest vegetation removal, and the strategies adopted to improve the machine localization and navigation performance on unstructured environments.

Impact of students assistance policies on quality of life and mental health.

Introduction: Student assistance policies in higher education, in their various modalities, seek to reduce the dropout of a new profile of students, non-traditional, with socioeconomic weaknesses, promoting quality of life (QoL) and mental health during the university journey. In this context, the Student Social Support Center (C.A.S.A) promotes assistance to students who need personal and/or economic support. Objectives: To evaluate the QoL and the presence of minor mental disorders (MMD) in students from the 1st to the 4th year of medicine at a public college in Brazil, comparing C.A.S.A beneficiaries and non-beneficiaries. Materials and methods: Cross-sectional study with 283 students. SRQ-20 and WHOQOL-BREF questionnaires were used, in addition to a questionnaire addressing sociodemographic data. Results: The general average of QoL was regular in the four evaluated domains (physical, psychological, social relationships, environment) and 55.5% of the students have evidence of MMD, in which the QoL scores are lower in all domains. The environment domain, which discusses socioeconomic conditions, has the worst score among C.A.S.A beneficiaries and the best among C.A.S.A non-beneficiaries. Discussion: The data corroborate the fragile situation of mental health and QoL of medical students. The student assistance modality of the analyzed program possibly presents vulnerabilities in its performance since the environment domain, the one with the lowest score among the program beneficiaries, precisely encompasses financial resources, access to goods and leisure and the individual's physical environment.

Complete Distal Rupture of the Rectus Femoris in an Elite Football Player: A Non-operative Treatment.

Although muscle injuries represent the most frequent injury in professional football, isolated complete distal ruptures of the rectus femoris (RF) muscle are rare, and there is no consensus on their treatment and return to play (RTP). In this article, we report a clinical case of successful non-surgical management of an RF grade 4c muscle injury in a professional football player, in which the athlete was able to RTP 21 weeks after the injury, had no re-injury >1 year after RTP, and is playing at an elite level in the Portuguese Football First League.

Enhancing reinforcement learning for de novo molecular design applying self-attention mechanisms.

The drug discovery process can be significantly improved by applying deep reinforcement learning (RL) methods that learn to generate compounds with desired pharmacological properties. Nevertheless, RL-based methods typically condense the evaluation of sampled compounds into a single scalar value, making it difficult for the generative agent to learn the optimal policy. This work combines self-attention mechanisms and RL to generate promising molecules. The idea is to evaluate the relative significance of each atom and functional group in their interaction with the target, and to utilize this information for optimizing the Generator. Therefore, the framework for de novo drug design is composed of a Generator that samples new compounds combined with a Transformer-encoder and a biological affinity Predictor that evaluate the generated structures. Moreover, it takes the advantage of the knowledge encapsulated in the Transformer's attention weights to evaluate each token individually. We compared the performance of two output prediction strategies for the Transformer: standard and masked language model (MLM). The results show that the MLM Transformer is more effective in optimizing the Generator compared with the state-of-the-art works. Additionally, the evaluation models identified the most important regions of each molecule for the biological interaction with the target. As a case study, we generated synthesizable hit compounds that can be putative inhibitors of the enzyme ubiquitin-specific protein 7 (USP7).

Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information.

In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding [Formula: see text] values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor's active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation.

Emergence of chaotic cluster synchronization in heterogeneous networks.

Many real-world complex systems rely on cluster synchronization to function properly. A cluster of nodes exhibits synchronous behavior, while others behave erratically. Predicting the emergence of these clusters and understanding the mechanism behind their structure and variation in response to a parameter change is a daunting task in networks that lack symmetry. We unravel the mechanism for the emergence of cluster synchronization in heterogeneous random networks. We develop heterogeneous mean-field approximation together with a self-consistent theory to determine the onset and stability of the cluster. Our analysis shows that cluster synchronization occurs in a wide variety of heterogeneous networks, node dynamics, and coupling functions. The results could lead to a new understanding of the dynamical behavior of networks ranging from neural to social.

Rehabilitation Interventions for Physical Capacity and Quality of Life in Adults With Post-COVID-19 Condition: A Systematic Review and Meta-Analysis.

Importance: Current rehabilitation guidelines for patients with post-COVID-19 condition (PCC) are primarily based on expert opinions and observational data, and there is an urgent need for evidence-based rehabilitation interventions to support patients with PCC. Objective: To synthesize the findings of existing studies that report on physical capacity (including functional exercise capacity, muscle function, dyspnea, and respiratory function) and quality of life outcomes following rehabilitation interventions in patients with PCC. Data Sources: A systematic electronic search was performed from January 2020 until February 2023, in MEDLINE, Scopus, CINAHL, and the Clinical Trials Registry. Key terms that were used to identify potentially relevant studies included long-covid, post-covid, sequelae, exercise therapy, rehabilitation, physical activity, physical therapy, and randomized controlled trial. Study Selection: This study included randomized clinical trials that compared respiratory training and exercise-based rehabilitation interventions with either placebo, usual care, waiting list, or control in patients with PCC. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A pairwise bayesian random-effects meta-analysis was performed using vague prior distributions. Risk of bias was assessed using the Cochrane risk of bias tool version 2, and the certainty of evidence was evaluated using the GRADE system by 2 independent researchers. Main Outcomes and Measures: The primary outcome was functional exercise capacity, measured at the closest postintervention time point by the 6-minute walking test. Secondary outcomes were fatigue, lower limb muscle function, dyspnea, respiratory function, and quality of life. All outcomes were defined a priori. Continuous outcomes were reported as standardized mean differences (SMDs) with 95% credible intervals (CrIs) and binary outcomes were summarized as odds ratios with 95% CrIs. The between-trial heterogeneity was quantified using the between-study variance, τ2, and 95% CrIs. Results: Of 1834 identified records, 1193 were screened, and 14 trials (1244 patients; 45% female participants; median [IQR] age, 50 [47 to 56] years) were included in the analyses. Rehabilitation interventions were associated with improvements in functional exercise capacity (SMD, -0.56; 95% CrI, -0.87 to -0.22) with moderate certainty in 7 trials (389 participants). These improvements had a 99% posterior probability of superiority when compared with current standard care. The value of τ2 (0.04; 95% CrI, 0.00 to 0.60) indicated low statistical heterogeneity. However, there was significant uncertainty and imprecision regarding the probability of experiencing exercise-induced adverse events (odds ratio, 1.68; 95% CrI, 0.32 to 9.94). Conclusions and Relevance: The findings of this systematic review and meta-analysis suggest that rehabilitation interventions are associated with improvements in functional exercise capacity, dyspnea, and quality of life, with a high probability of improvement compared with the current standard care; the certainty of evidence was moderate for functional exercise capacity and quality of life and low for other outcomes. Given the uncertainty surrounding the safety outcomes, additional trials with enhanced monitoring of adverse events are necessary.

Statistical analysis of blast-induced vibration near an open pit mine.

Blast-induced vibration may be harmful to facilities in the vicinity of operating mines, mainly causing structural damage and human discomfort. This study presents an application of multivariate statistics to predict vibration levels regarding their potential to cause structural damage and human discomfort. An extensive seismic monitoring campaign was executed in a large open-pit iron ore mine, near a small village, to gather a dataset for a predictive multivariate analysis. Ten blasting events have produced a dataset of 158 valid measurements. Three classes of vibration peak velocity were adopted from legal standards, which later supported a cluster analysis. Then, it was possible to compare how much these two classification modalities respond to discriminant analysis. The next step was to carry out a principal component analysis (PCA) from the original database, and, comparatively, to plot both the scores concerning the classes derived from the vibration standard and those from the groups obtained from cluster analysis. PCA has considerably explained the data variability, while the three classes from cluster analysis resulted very similar to the corresponding ones from the vibration standards. The results have demonstrated that multivariate statistics may be applied to manage blasting-induced vibration and its deleterious effects with few adjustments and automation.

Trends in trust of professional groups in Portugal during COVID-19.

We repeated a Portuguese study, 5 months after a first evaluation, to observe any change in the average levels of trust of professional groups during the COVID-19 pandemic. From our sample of the Portuguese population, 1455 completed the questionnaire from June through September 2020, and 1156 during 2 months, February and March 2021. We found a trend toward higher trust of medical doctors, nurses, and researchers. Analysis of changes between the two pandemic periods (first and second lockdown) showed a statistically significant increase in trust in nurses (t = - 2.01; p = 0.04) and researchers (t = - 2.40; p = 0.02), and the opposite trend for journalists and politicians. This study shows a trend in increasing trust of the Portuguese people toward science-based activities during the COVID-19 pandemic.

Analytical performance of the FDA-cleared Parsortix® PC1 system.

Introduction: The Parsortix® PC1 system, Food and Drug Administration (FDA) cleared for use in metastatic breast cancer (MBC) patients, is an epitope-independent microfluidic device for the capture and harvest of circulating tumor cells from whole blood based on cell size and deformability. This report details the analytical characterization of linearity, detection limit, precision, and reproducibility for this device. Methods: System performance was determined using K2-EDTA blood samples collected from self-declared healthy female volunteers (HVs) and MBC patients spiked with prelabeled cultured breast cancer cell lines (SKBR3, MCF7, or Hs578T). Samples were processed on Parsortix® PC1 systems and captured cells were harvested and enumerated. Results: The system captured and harvested live SKBR3, MCF7, and Hs578T cells and fixed SKBR3 cells linearly between 2 and ~100 cells, with average harvest rates of 69%, 73%, 79%, and 90%, respectively. To harvest ≥1 cell ≥95% of the time, the system required 3, 5 or 4 live SKBR3, MCF7 or Hs578T cells, respectively. Average harvest rates from precision studies using 5, 10, and ~50 live cells spiked into blood for each cell line ranged from 63.5% to 76.2%, with repeatability and reproducibility percent coefficient of variation (%CV) estimates ranging from 12.3% to 32.4% and 13.3% to 34.1%, respectively. Average harvest rates using ~20 fixed SKBR3 cells spiked into HV and MBC patient blood samples were 75.0% ± 16.1% (%CV = 22.3%) and 68.4% ± 14.3% (%CV = 21.1%), respectively. Conclusions: These evaluations demonstrate the Parsortix® PC1 system linearly and reproducibly harvests tumor cells from blood over a range of 1 to ~100 cells.

FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers.

The design of compounds that target specific biological functions with relevant selectivity is critical in the context of drug discovery, especially due to the polypharmacological nature of most existing drug molecules. In recent years, in silico-based methods combined with deep learning have shown promising results in the de novo drug design challenge, leading to potential leads for biologically interesting targets. However, several of these methods overlook the importance of certain properties, such as validity rate and target selectivity, or simplify the generative process by neglecting the multi-objective nature of the pharmacological space. In this study, we propose a multi-objective Transformer-based architecture to generate drug candidates with desired molecular properties and increased selectivity toward a specific biological target. The framework consists of a Transformer-Decoder Generator that generates novel and valid compounds in the SMILES format notation, a Transformer-Encoder Predictor that estimates the binding affinity toward the biological target, and a feedback loop combined with a multi-objective optimization strategy to rank the generated molecules and condition the generating distribution around the targeted properties. The results demonstrate that the proposed architecture can generate novel and synthesizable small compounds with desired pharmacological properties toward a biologically relevant target. The unbiased Transformer-based Generator achieved superior performance in the novelty rate (97.38%) and comparable performance in terms of internal diversity, uniqueness, and validity against state-of-the-art baselines. The optimization of the unbiased Transformer-based Generator resulted in the generation of molecules exhibiting high binding affinity toward the Adenosine A2A Receptor (AA2AR) and possessing desirable physicochemical properties, where 99.36% of the generated molecules follow Lipinski's rule of five. Furthermore, the implementation of a feedback strategy, in conjunction with a multi-objective algorithm, effectively shifted the distribution of the generated molecules toward optimal values of molecular weight, molecular lipophilicity, topological polar surface area, synthetic accessibility score, and quantitative estimate of drug-likeness, without the necessity of prior training sets comprising molecules endowed with pharmacological properties of interest. Overall, this research study validates the applicability of a Transformer-based architecture in the context of drug design, capable of exploring the vast chemical representation space to generate novel molecules with improved pharmacological properties and target selectivity. The data and source code used in this study are available at: https://github.com/larngroup/FSM-DDTR.

Return to Play After the Diagnosis of Reactive Arthritis in a Professional Football Player.

In professional football, most of the injuries are traumatic; however, these athletes may suffer from rheumatologic diseases, that may present as sports-related injuries. Reactive arthritis (ReA) is classified as a sub-group of the spondyloarthritis family and is relatively rare. In this article, we highlight the successful return to play (RTP) process after the ReA diagnosis in an elite football player in the Portuguese first league. The athlete was able to RTP four months and one week after the diagnosis, had no ReA recurrence nor re-injury >8 months after RTP, and is playing at an elite level.

Exponentially Long Transient Time to Synchronization of Coupled Chaotic Circle Maps in Dense Random Networks.

We study the transition to synchronization in large, dense networks of chaotic circle maps, where an exact solution of the mean-field dynamics in the infinite network and all-to-all coupling limit is known. In dense networks of finite size and link probability of smaller than one, the incoherent state is meta-stable for coupling strengths that are larger than the mean-field critical coupling. We observe chaotic transients with exponentially distributed escape times and study the scaling behavior of the mean time to synchronization.