Insights into the anticancer photodynamic activity of Ir(III) and Ru(II) polypyridyl complexes bearing ß-carboline ligands.

Ir(III) and Ru(II) polypyridyl complexes are promising photosensitizers (PSs) for photodynamic therapy (PDT) due to their outstanding photophysical properties. Herein, one series of cyclometallated Ir(III) complexes and two series of Ru(II) polypyridyl derivatives bearing three different thiazolyl-ß-carboline N^N' ligands have been synthesized, aiming to evaluate the impact of the different metal fragments ([Ir(C^N)2]+ or [Ru(N^N)2]2+) and N^N' ligands on the photophysical and biological properties. All the compounds exhibit remarkable photostability under blue-light irradiation and are emissive (605 < λem < 720 nm), with the Ru(II) derivatives displaying higher photoluminescence quantum yields and longer excited state lifetimes. The Ir PSs display pKa values between 5.9 and 7.9, whereas their Ru counterparts are less acidic (pKa > 9.3). The presence of the deprotonated form in the Ir-PSs favours the generation of reactive oxygen species (ROS) since, according to theoretical calculations, it features a low-lying ligand-centered triplet excited state (T1 = 3LC) with a long lifetime. All compounds have demonstrated anticancer activity. Ir(III) complexes 1-3 exhibit the highest cytotoxicity in dark conditions, comparable to cisplatin. Their activity is notably enhanced by blue-light irradiation, resulting in nanomolar IC50 values and phototoxicity indexes (PIs) between 70 and 201 in different cancer cell lines. The Ir(III) PSs are also activated by green (with PI between 16 and 19.2) and red light in the case of complex 3 (PI = 8.5). Their antitumor efficacy is confirmed by clonogenic assays and using spheroid models. The Ir(III) complexes rapidly enter cells, accumulating in mitochondria and lysosomes. Upon photoactivation, they generate ROS, leading to mitochondrial dysfunction and lysosomal damage and ultimately cell apoptosis. Additionally, they inhibit cancer cell migration, a crucial step in metastasis. In contrast, Ru(II) complex 6 exhibits moderate mitochondrial activity. Overall, Ir(III) complexes 1-3 show potential for selective light-controlled cancer treatment, providing an alternative mechanism to chemotherapy and the ability to inhibit lethal cancer cell dissemination.

Alternative and efficient one-pot three-component synthesis of substituted 2-aryl-4-styrylquinazolines/4-styrylquinazolines from synthetically available 1-(2-aminophenyl)-3-arylprop-2-en-1-ones: characterization and evaluation of their antiproliferative activities.

In this study, an alternative and efficient one-pot three-component synthesis approach to develop a new series of (E)-2-aryl-4-styrylquinazolines and (E)-4-styrylquinazolines is described. According to this approach, the target compounds were synthesized straightforward in high yields and in short reaction times from substituted 1-(2-aminophenyl)-3-arylprop-2-en-1-ones via its well-Cu(OAc)2-mediated cyclocondensation reactions with aromatic aldehydes or its well-catalyst-free cyclocondensation reactions with trimethoxy methane (trimethyl orthoformate), and ammonium acetate under aerobic conditions. This is an operationally simple, valuable, and direct method to synthesize 2-aryl- and non-C2-substituted quinazolines containing a styryl framework at C4 position from cheap and synthetically available starting materials. All the synthesized compounds were submitted to the US National Cancer Institute for in vitro screening. The bromo- and chloro-substituted quinazolines 5c and 5d displayed a potent antitumor activity against all the tested subpanel tumor cell lines with IC50 (MG-MID) values of 5.25 and 5.50 µM, and a low cytotoxic effect with LC50 (MG-MID) values of 91.20 and 84.67 µM, respectively, indicating a low toxicity of these compounds to normal human cell lines, as required for potential antitumor agents.

Pain Management Treatments and Opioid Use Disorder Risk in Medicaid Patients.

INTRODUCTION: People with chronic pain are at increased risk of opioid misuse. Less is known about the unique risk conferred by each pain management treatment, as treatments are typically implemented together, confounding their independent effects. This study estimated the extent to which pain management treatments were associated with risk of opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain management treatments at their observed levels. METHODS: Data were analyzed in 2024 from 2 chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: those with (1) chronic pain and physical disability (CPPD) (N=6,133) or (2) chronic pain without disability (CP) (N=67,438). Nine pain management treatments were considered: prescription opioid (1) dose and (2) duration; (3) number of opioid prescribers; opioid co-prescription with (4) benzo- diazepines, (5) muscle relaxants, and (6) gabapentinoids; (7) nonopioid pain prescription, (8) physical therapy, and (9) other pain treatment modality. The outcome was OUD risk. RESULTS: Having opioids co-prescribed with gabapentin or benzodiazepine was statistically significantly associated with a 37-45% increased OUD risk for the CP subgroup. Opioid dose and duration also were significantly associated with increased OUD risk in this subgroup. Physical therapy was significantly associated with an 18% decreased risk of OUD in the CP subgroup. DISCUSSION: Coprescription of opioids with either gabapentin or benzodiazepines may substantially increase OUD risk. More positively, physical therapy may be a relatively accessible and safe pain management strategy.

Asymmetric multi-task learning for interpretable gaze-driven grasping action forecasting.

This work tackles the problem of automatically predicting the grasping intention of humans observing their environment, with eye-tracker glasses and video cameras recording the scene view. Our target application is the assistance to people with motor disabilities and potential cognitive impairments, using assistive robotics. Our proposal leverages the analysis of human attention captured in the form of gaze fixations recorded by an eye-tracker on the first person video, as the anticipation of prehension actions is a well studied and well known phenomenon. We propose a multi-task system that simultaneously addresses the prediction of human attention in the near future, and the anticipation of grasping actions. In our model, visual attention is modeled as a competitive process between a discrete set of states, each one associated to a well-known gaze movement pattern from visual psychology. We additionally consider an asymmetric multitask problem, where attention modeling is an auxiliary task that helps to regularize the learning process of the main action prediction task, and propose a constrained multi-task loss that naturally deals with this asymmetry. Our model shows superior performance than other losses for dynamic multi-task learning, current dominant deep architectures for general action forecasting and particularly-tailored models for predicting grasping intention. In particular, it provides state-of-the-art performance in three datasets for egocentric action anticipation, with an average precision of 0.569 and 0.524 in GITW and Sharon datasets, respectively, and an accuracy of 89.2% and a success rate of 51.7% in Invisible dataset.

A self-supervised embedding of cell migration features for behavior discovery over cell populations.

BACKGROUND AND OBJECTIVE: Recent studies point out that the dynamics and interaction of cell populations within their environment are related to several biological processes in immunology. Hence, single-cell analysis in immunology now relies on spatial omics. Moreover, recent literature suggests that immunology scenarios are hierarchically organized, including unknown cell behaviors appearing in different proportions across some observable control and therapy groups. These dynamic behaviors play a crucial role in identifying the causes of processes such as inflammation, aging, and fighting off pathogens or cancerous cells. In this work, we use a self-supervised learning approach to discover these behaviors associated with cell dynamics in an immunology scenario. MATERIALS AND METHODS: Specifically, we study the different responses of control group and therapy groups in a scenario involving inflammation due to infarct, with a focus on neutrophil migration within blood vessels. Starting from a set of hand-crafted spatio-temporal features, we use a recurrent neural network to generate embeddings that properly describe the dynamics of the migration processes. The network is trained using a novel multi-task contrastive loss that, on the one hand, models the hierarchical structure of our scenario (groups-behaviors-samples) and, on the other, ensures temporal consistency within the embedding, enforcing that subsequent temporal samples obtained from a given cell stay close in the latent space. RESULTS: Our experimental results demonstrate that the resulting embeddings improve the separability of cell behaviors and log-likelihood of the therapies, when compared to the hand-crafted feature extraction and recent methods from the state of the art, even with dimensionality reduction (16 vs. 21 hand-crafted features). CONCLUSIONS: Our approach enables single-cell analyses at a population level, being able to automatically discover shared behaviors among different groups. This, in turn, enables the prediction of the therapy effectiveness based on their proportions within a study group.

Learning Optimal Dynamic Treatment Regimes from Longitudinal Data.

Studies often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone (BUP-NX) dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy.

Chemical Composition, Antioxidant Activities, Antidepressant Effect, and Lipid Peroxidation of Peruvian Blueberry: Molecular Docking Studies on Targets Involved in Oxidative Stress and Depression.

Blueberries (Vaccinium corymbosum L.) are cultivated worldwide and are among the best dietary sources of bioactive compounds with beneficial health effects. This study aimed to investigate the components of Peruvian blueberry using high-performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (HPLC-ESI-QTOF-MS/MS), identifying 11 compounds. Furthermore, we assessed in vitro the antioxidant activity and in vivo the antidepressant effect using a rat model and protective effect on lipid peroxidation (in the serum, brain, liver, and stomach). We also conducted molecular docking simulations with proteins involved in oxidative stress and depression for the identified compounds. Antioxidant activity was assessed by measuring total phenolic and flavonoid contents, as well as using 1,1-diphenyl-2-picrylhydrazin (DPPH), 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic) acid (ABTS•+), and ferric-reducing antioxidant power (FRAP) assays. Peruvian blueberries demonstrated higher antioxidant activity than Vaccinium corymbosum fruits from Chile, Brazil, the United States, Turkey, Portugal, and China. The results showed that oral administration of Peruvian blueberries (10 and 20 mg/kg) for 28 days significantly (p < 0.001) increased swimming and reduced immobility in the forced swimming test (FST). Additionally, at doses of 40 and 80 mg/kg, oxidative stress was reduced in vivo (p < 0.001) by decreasing lipid peroxidation in brain, liver, stomach, and serum. Molecular docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions were performed. In the molecular docking studies, quercitrin and 3,5-di-O-caffeoylquinic acid showed the best docking scores for nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, superoxide dismutase, and xanthine oxidase; while 3,5-dicaffeoylquinic acid methyl ester and caffeoyl coumaroylquinic acid had the best docking scores for monoamine oxidase and serotonin receptor 5-HT2. In summary, our results suggest that the antidepressant and protective effects against lipid peroxidation might be related to the antioxidant activity of Peruvian Vaccinium corymbosum L.

Causal Inference for Social Network Data.

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects.

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders.

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.

Using instrumental variables to address unmeasured confounding in causal mediation analysis.

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding. However, in contrast to the rich literature on the use of IV methods to identify and estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy to identify mediational indirect effects. In response, we define and nonparametrically identify novel estimands-double complier interventional direct and indirect effects-when 2, possibly related, IVs are available, one for the exposure and another for the mediator. We propose nonparametric, robust, efficient estimators for these effects and apply them to a housing voucher experiment.

URI-CADS: A Fully Automated Computer-Aided Diagnosis System for Ultrasound Renal Imaging.

Ultrasound is a widespread imaging modality, with special application in medical fields such as nephrology. However, automated approaches for ultrasound renal interpretation still pose some challenges: (1) the need for manual supervision by experts at various stages of the system, which prevents its adoption in primary healthcare, and (2) their limited considered taxonomy (e.g., reduced number of pathologies), which makes them unsuitable for training practitioners and providing support to experts. This paper proposes a fully automated computer-aided diagnosis system for ultrasound renal imaging addressing both of these challenges. Our system is based in a multi-task architecture, which is implemented by a three-branched convolutional neural network and is capable of segmenting the kidney and detecting global and local pathologies with no need of human interaction during diagnosis. The integration of different image perspectives at distinct granularities enhanced the proposed diagnosis. We employ a large (1985 images) and demanding ultrasound renal imaging database, publicly released with the system and annotated on the basis of an exhaustive taxonomy of two global and nine local pathologies (including cysts, lithiasis, hydronephrosis, angiomyolipoma), establishing a benchmark for ultrasound renal interpretation. Experiments show that our proposed method outperforms several state-of-the-art methods in both segmentation and diagnosis tasks and leverages the combination of global and local image information to improve the diagnosis. Our results, with a 87.41% of AUC in healthy-pathological diagnosis and 81.90% in multi-pathological diagnosis, support the use of our system as a helpful tool in the healthcare system.

EFFECT OF PAXLOVID TREATMENT DURING ACUTE COVID-19 ON LONG COVID ONSET: AN EHR-BASED TARGET TRIAL EMULATION FROM THE N3C AND RECOVER CONSORTIA.

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.

A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining.

Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

Second generation of pyrimidin-quinolone hybrids obtained from virtual screening acting as sphingosine kinase 1 inhibitors and potential anticancer agents.

We report here the virtual screening design, synthesis and activity of eight new inhibitors of SphK1. For this study we used a pre-trained Graph Convolutional Network (GCN) combined with docking calculations. This exploratory analysis proposed nine compounds from which eight displayed significant inhibitory effect against sphingosine kinase 1 (SphK1) demonstrating a high level of efficacy for this approach. Four of these compounds also displayed anticancer activity against different tumor cell lines, and three of them (5), (6) and (7) have shown a wide inhibitory action against many of the cancer cell line tested, with GI50 below 5 µM, being (5) the most promising with TGI below 10 µM for the half of cell lines. Our results suggest that the three most promising compounds reported here are the pyrimidine-quinolone hybrids (1) and (6) linked by p-aminophenylsulfanyl and o-aminophenol fragments respectively, and (8) without such aryl linker. We also performed an exhaustive study about the molecular interactions that stabilize the different ligands at the binding site of SphK1. This molecular modeling analysis was carried out by using combined techniques: docking calculations, MD simulations and QTAIM analysis. In this study we also included PF543, as reference compound, in order to better understand the molecular behavior of these ligands at the binding site of SphK1.These results provide useful information for the design of new inhibitors of SphK1 possessing these structural scaffolds.

Independent and joint contributions of physical disability and chronic pain to incident opioid use disorder and opioid overdose among Medicaid patients.

BACKGROUND: Chronic pain has been extensively explored as a risk factor for opioid misuse, resulting in increased focus on opioid prescribing practices for individuals with such conditions. Physical disability sometimes co-occurs with chronic pain but may also represent an independent risk factor for opioid misuse. However, previous research has not disentangled whether disability contributes to risk independent of chronic pain. METHODS: Here, we estimate the independent and joint adjusted associations between having a physical disability and co-occurring chronic pain condition at time of Medicaid enrollment on subsequent 18-month risk of incident opioid use disorder (OUD) and non-fatal, unintentional opioid overdose among non-elderly, adult Medicaid beneficiaries (2016-2019). RESULTS: We find robust evidence that having a physical disability approximately doubles the risk of incident OUD or opioid overdose, and physical disability co-occurring with chronic pain increases the risks approximately sixfold as compared to having neither chronic pain nor disability. In absolute numbers, those with neither a physical disability nor chronic pain condition have a 1.8% adjusted risk of incident OUD over 18 months of follow-up, those with physical disability alone have an 2.9% incident risk, those with chronic pain alone have a 3.6% incident risk, and those with co-occurring physical disability and chronic pain have a 11.1% incident risk. CONCLUSIONS: These findings suggest that those with a physical disability should receive increased attention from the medical and healthcare communities to reduce their risk of opioid misuse and attendant negative outcomes.

Causal survival analysis under competing risks using longitudinal modified treatment policies.

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as [Formula: see text]-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

The application of target trials with longitudinal targeted maximum likelihood estimation to assess the effect of alcohol consumption in adolescence on depressive symptoms in adulthood.

Time-varying confounding is a common challenge for causal inference in observational studies with time-varying treatments, long follow-up periods, and participant dropout. Confounder adjustment using traditional approaches can be limited by data sparsity, weight instability and computational issues. The Nicotine Dependence in Teens (NDIT) study is a prospective cohort study involving 24 data collection cycles from 1999 to date, among 1,294 students recruited from 10 high schools in Montreal, Canada, including follow-up into adulthood. Our aim is to estimate associations between the timing of alcohol initiation and the cumulative duration of alcohol use on depression symptoms in adulthood. Based on the target trials framework, we define intention-to-treat and as-treated parameters in a marginal structural model with sex as a potential effect-modifier. We then use the observational data to emulate the trials. For estimation, we use pooled longitudinal target maximum likelihood estimation (LTMLE), a plug-in estimator with double robust and local efficiency properties. We describe strategies for dealing with high-dimensional potential drinking patterns and practical positivity violations due to a long follow-up time, including modifying the effect of interest by removing sparsely observed drinking patterns from the loss function and applying longitudinal modified treatment policies to represent the effect of discouraging drinking.

Governing the emissive properties of 4-aminobiphenyl-2-pyrimidine push-pull systems via the restricted torsion of N,N-disubstituted amino groups.

Donor-acceptor-substituted biphenyl derivatives are particularly interesting model compounds, which exhibit intramolecular charge transfer because of the extent of charge transfer between both substituents. The connection of a 4-[1,1'-biphenyl]-4-yl-2-pyrimidinyl) moiety to differently disubstituted amino groups at the biphenyl terminal can offer push-pull compounds with distinctive photophysical properties. Herein, we report a comprehensive study of the influence of the torsion angle of the disubstituted amino group on the emissive properties of two pull-push systems: 4-[4-(4-N,N-dimethylaminophenyl)phenyl]-2,6-diphenylpyrimidine (D1) and 4-[4-(4-N,N-diphenylaminophenyl)phenyl]-2,6-diphenylpyrimidine (D2). The torsion angle of the disubstituted amino group, either N,N-dimethyl-amine or N,N-diphenyl-amine, at the biphenyl end governs their emissive properties. A drastic fluorescence quenching occurs in D1 as the solvent polarity increases, whereas D2 maintains its emission independently of the solvent polarity. Theoretical calculations on D1 support the presence of a twisted geometry for the lowest energy, charge-transfer excited state (S1,90), which corresponds to the minimum energy structure in polar solvents and presents a small energy barrier to move from the excited to the ground state, thereby favoring the non-radiative pathway and reducing the fluorescence efficiency. In contrast, this twisted structure is absent in D2 due to the steric hindrance of the phenyl groups attached to the amine group, making the non-radiative decay less favorable. Our findings provide insights into the crucial role of the substituent in the donor moiety of donor-acceptor systems on both the singlet excited state and the intramolecular charge-transfer process.

A causal roadmap for generating high-quality real-world evidence.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Caracterización de las cirugías laparoscópicas en el Servicio de Ginecología del Hospital Materno-Infantil Ángel Arturo Aballí / Characterization of Laparoscopic Surgeries in the Gynecology Service at Hospital Materno-Infantil Ángel Arturo Aballí

Introducción: La cirugía laparoscópica ginecológica permite intervenir a la paciente sin tener que abrir el abdomen, pues consiste en realizar pequeñas incisiones. Actualmente se emplea en enfermedades como el cáncer o en la extracción de quistes y miomas, por lo que se puede aplicar en casi cualquier intervención de cirugía ginecológica. Objetivo: Caracterizar los procedimientos laparoscópicos del Servicio de Ginecología en el Hospital Materno-Infantil Ángel Arturo Aballí en el período comprendido entre enero del 2018 y diciembre del 2021. Métodos: Se realizó un estudio observacional, descriptivo, retrospectivo de corte longitudinal con el fin de describir la actividad asistencial laparoscópica. La muestra fue de 180 pacientes, según el criterio de selección no probabilístico de los investigadores. Resultados: La edad promedio fue 41,6 años. Según los antecedentes, predominó el tamaño del útero con 91,1 por ciento (n = 164). Dentro de las indicaciones quirúrgicas, se encontró la paridad satisfecha en un 34,4 por ciento, seguido del tumor de ovario (18,9 por ciento), las neoplasias intraepiteliales cervicales (13,4 por ciento) y el fibroma uterino sintomático. En cuanto a la cirugía mayor, se encontró la histerectomía abdominal laparoscópica en un 49,0 por ciento (n = 78), seguido de la salpingectomía con un 38,9 por ciento, resultados con significación desde el punto de vista estadístico (p = 0,004). En cuanto a la cirugía menor, el 11,6 por ciento de la muestra recibió la ablación de los focos endometriales. Las complicaciones fueron escasas. Conclusiones: Los procederes laparoscópicos del Servicio de Ginecología del Hospital Materno-Infantil Ángel Arturo Aballí se consideraron adecuados de acuerdo a los indicadores quirúrgicos(AU)

Introduction: Laparoscopic gynecological surgery allows for the patient to undergo surgery without having to open their abdomen, since it consists in making small incisions. It is currently used in diseases such as cancer or in the removal of cysts and myomas; therefore, it can be applied in almost any gynecological surgery. Objective: To characterize the laparoscopic procedures of the gynecology service at Hospital Materno-Infantil Ángel Arturo Aballí, in the period from January 2018 to December 2021. Methods: An observational, descriptive, retrospective and longitudinal study was carried out in order to describe the laparoscopic assistance activity. The sample consisted of 180 patients, according to the researchers' nonprobabilistic selection criteria. Results: The mean age was 41.6 years. Concerning antecedents, uterine size predominated, with 91.1 percent (n = 164). Among surgical indications, satisfied parity was found in 34.4 percent , followed by ovarian tumor (18.9 percent ), cervical intraepithelial neoplasms (13.4 percent ) and symptomatic uterine fibroid. With respect to major surgery, laparoscopic abdominal hysterectomy was found in 49.0 percent (n = 78), followed by salpingectomy, with 38.9 percent ; such results are statistically significant (p = 0.004). Regarding minor surgery, 11.6 percent of the sample received ablation of endometrial focuses. Complications were rare. Conclusions: The laparoscopic procedures of the gynecology service at Hospital Materno-Infantil Ángel Arturo Aballí are considered adequate according to the surgical indicators(AU)