Immune checkpoint reprogramming via sequential nucleic acid delivery strategy optimizes systemic immune responses for gastrointestinal cancer immunotherapy.

Monoclonal antibodies targeting immune checkpoints have been widely applied in gastrointestinal cancer immunotherapy. However, systemic administration of various monoclonal antibodies does not often result in sustained effects in reversing the immunosuppressive tumor microenvironment (TME), which may be due to the spatiotemporal dynamic changes of immune checkpoints. Herein, we reported a novel immune checkpoint reprogramming strategy for gastrointestinal cancer immunotherapy. It was achieved by the sequential delivery of siPD-L1 (siRNA for programmed cell death ligand 1) and pOX40L (plasmid for OX40 ligand), which were complexed with two cationic polymer brush-grafted carbon nanotubes (dense short (DS) and dense long (DL)) designed based on the structural characteristics of nucleic acids and brush architectures. Upon administrating DL/pOX40L for the first three dosages, then followed by DS/siPD-L1 for the next three dosages to the TME, it upregulated the stimulatory checkpoint OX40L on dendritic cells (DCs) and downregulated inhibitory checkpoint PD-L1 on tumor cells and DCs in a sequential reprogramming manner. Compared with other combination treatments, this sequential strategy drastically boosted the DCs maturation, and CD8+ cytotoxic T lymphocytes infiltration in tumor site. Furthermore, it could augment the local antitumor response and improve the T cell infiltration in tumor-draining lymph nodes to reverse the peripheral immunosuppression. Our study demonstrated that sequential nucleic acid delivery strategy via personalized nanoplatforms effectively reversed the immunosuppression status in both tumor microenvironment and peripheral immune landscape, which significantly enhanced the systemic antitumor immune responses and established an optimal immunotherapy strategy against gastrointestinal cancer.

Practical and analytical considerations when performing interim analyses in diagnostic test accuracy studies.

Interim analysis is a common methodology in randomised clinical trials but has received less attention in studies of diagnostic test accuracy. In such studies, early termination for futility may be beneficial if early evidence indicates that a diagnostic test is unlikely to achieve a clinically useful level of diagnostic performance, as measured by the sensitivity and specificity. In this paper, we describe relevant practical and analytical considerations when planning and performing interim analysis in diagnostic accuracy studies, focusing on stopping rules for futility. We present an adaptation of the exact group sequential method for diagnostic testing, with R code provided for implementing this method in practice. The method is illustrated using two simulated data sets and data from a published diagnostic accuracy study for point-of-care testing for SARS-CoV-2. The considerations described in this paper can be used to guide decisions as to when an interim analysis in a diagnostic accuracy study is suitable and highlight areas for further methodological development.

Dendritic cell vaccine for glioblastoma: an updated meta-analysis and trial sequential analysis.

BACKGROUND: Dendritic cell (DC) vaccine is an emerging immunotherapy that could potentially improve glioblastoma survival. The first phase III clinical trial of DC vaccine was recently published. This meta-analysis aims to update and reappraise existing evidence on the efficacy of DC vaccine in patients with glioblastoma. METHODS: We searched PubMed, Embase, and Cochrane Library for clinical trials of DC vaccine for glioblastoma. The quality of the studies was assessed using the RoB 2.0 and ROBINS-I tools. The results of overall survival (OS) and progression-free survival (PFS) were pooled using hazard ratios (HRs) with corresponding 95% confidence intervals (CI). Summary effects were evaluated using random effects models. Trial sequential analysis (TSA) was performed. RESULTS: Seven clinical trials involving 3,619 patients were included. DC vaccine plus standard care was associated with significantly improved OS (HR = 0.71; 95% CI, 0.57 - 0.88) and PFS (HR = 0.65; 95% CI, 0.43 - 0.98). In the subgroup of newly diagnosed glioblastoma, DC vaccine was associated with improved PFS (HR = 0.59; 95% CI, 0.39 - 0.90). TSA of OS showed that the cumulative z-score line for the DC vaccine crossed the benefit boundary and reached the required sample size. TSA of PFS and subgroup analysis of newly diagnosed glioblastoma showed that the required sample size was not reached. CONCLUSIONS: This updated meta-analysis, which included the first phase III trial of a DC vaccine for glioblastoma, demonstrated that the DC vaccine was associated with improved OS. Moreover, TSA showed that the required sample size was reached, indicating a true-positive result. Future studies are required for patient subgroups with newly diagnosed and recurrent glioblastoma.

Synthesis and quality assessment of combined time-series and static medical data using a real-world time-series generative adversarial network.

This study addresses challenges related to privacy issues in utilizing medical data, particularly the protection of personal information. To overcome this obstacle, the research focuses on data synthesis using real-world time-series generative adversarial networks (RTSGAN). A total of 53,005 data were synthesized using the dataset of 15,799 patients with colorectal cancer. The results of the quantitative evaluation of the synthetic data's quality are as follows: the Hellinger distance ranged from 0 to 0.25; the train on synthetic, test on real (TSTR) and train on real, test on synthetic (TRTS) results showed an average area under the curve of 0.99 and 0.98; a propensity mean squared error was 0.223. The synthetic and real data were similar in the qualitative methods including t-SNE and histogram analyses. The application of synthetic data in predicting five-year survival in colorectal cancer patients demonstrates comparable performance to models based on real data. This study employs distance to closest records and membership inference test to assess potential privacy exposure, revealing minimal risk. This study demonstrated that it is feasible to synthesize medical data, including time-series data, using the RTSGAN, and the synthetic data can be evaluated to accurately reflect the characteristics of real data through quantitative and qualitative methods as well as by utilizing real-world artificial intelligence models.

A computationally efficient sequential regression imputation algorithm for multilevel data.

Due to the computational burden, especially in high-dimensional settings, sequential imputation may not be practical. In this paper, we adopt computationally advantageous methods by sampling the missing data from their perspective predictive distributions, which leads to significantly improved computation time in the class of variable-by-variable imputation algorithms. We assess the computational performance in a comprehensive simulation study. We then compare and contrast the performance of our algorithm with commonly used alternatives. The results show that our method has a significant advantage over the commonly used alternatives with respect to computational efficiency and inferential quality. Finally, we demonstrate our methods in a substantive problem aimed at investigating the effects of area-level behavioral, socioeconomic, and demographic characteristics on poor birth outcomes in New York State among singleton births.

Utility of epitope-specific IgE, IgG4, and IgG1 antibodies for the diagnosis of wheat allergy.

BACKGROUND: The bead-based epitope assay (BBEA) has been used to identify epitope-specific (es) antibodies and successfully utilized to diagnose clinical allergy to milk, egg and peanut. OBJECTIVE: This study aimed to identify es-IgE, es-IgG4 and es-IgG1 of wheat proteins and determine the optimal peptides to differentiate wheat-allergic from wheat-tolerant using the BBEA. METHODS: Children and adolescents who underwent an oral food challenge to confirm their wheat allergy status were enrolled. Seventy-nine peptides from alpha/beta-gliadin, gamma-gliadin (γ-gliadin), omega-5-gliadin (ω-5-gliadin), high and low molecular weight glutenin were commercially synthesized and coupled to LumAvidin beads. Machine learning (ML) methods were used to identify diagnostic epitopes and performance was evaluated using DeLong's test. RESULTS: The analysis includes 122 children (83 wheat-allergic and 39 wheat-tolerant, 57.4% male). ML coupled with simulations identified wheat es-IgE, but not es-IgG4 or es-IgG1 to be most informative for diagnosing wheat allergy. Higher es-IgE binding intensity correlated with the severity of allergy phenotypes, with wheat anaphylaxis exhibiting the highest es-IgE binding intensity. In contrast, wheat-dependent exercise-induced anaphylaxis (WDEIA) showed lower es-IgG1 binding than all other groups. A set of 4 informative epitopes from ω-5-gliadin, and γ-gliadin were the best predictors of wheat allergy with an AUC of 0.908 (sensitivity=83.4%, specificity=88.4%), higher than the performance exhibited by wheat-specific IgE (AUC=0.646, p < 0.001). The predictive ability of our model was confirmed in an external cohort of 71 patients (29 allergic, 42 non-allergic), with an AUC of 0.908 (sensitivity=75.9%, specificity=90.5%). CONCLUSION: The wheat BBEA demonstrated greater diagnostic accuracy compared to existing specific IgE tests for wheat allergy.

3D Bioprinted Tissue-Engineered Bone with Enhanced Mechanical Strength and Bioactivities: Accelerating Bone Defect Repair through Sequential Immunomodulatory Properties.

In this study, a new-generation tissue-engineered bone capable of temporally regulating the immune response, balancing proinflammatory and anti-inflammatory activities, and facilitating bone regeneration and repair to address the challenges of delayed healing and nonunion in large-sized bone defects, is innovatively developed. Using the innovative techniques including multiphysics-assisted combined decellularization, side-chain biochemical modification, and sterile freeze-drying, a novel photocurable extracellular matrix hydrogel, methacrylated bone-derived decellularized extracellular matrix (bdECM-MA), is synthesized. After incorporating the bdECM-MA with silicon-substituted calcium phosphate and bone marrow mesenchymal stem cells, the tissue-engineered bone is fabricated through digital light processing 3D bioprinting. This study provides in vitro confirmation that the engineered bone maintains high cellular viability while achieving MPa-level mechanical strength. Moreover, this engineered bone exhibits excellent osteogenesis, angiogenesis, and immunomodulatory functions. One of the molecular mechanisms of the immunomodulatory function involves the inhibition of the p38-MAPK pathway. A pioneering in vivo discovery is that the natural biomaterial-based tissue-engineered bone demonstrates sequential immunomodulatory properties that activate proinflammatory and anti-inflammatory responses in succession, significantly accelerating the repair of bone defects. This study provides a new research basis and an effective method for developing autogenous bone substitute materials and treating large-sized bone defects.

Effector Binding Sequentially Alters KRAS Dimerization on the Membrane: New Insights Into RAS-Mediated RAF Activation.

RAS proteins are peripheral membrane GTPases that activate multiple downstream effectors for cell proliferation and differentiation. The formation of a signaling RAS-RAF complex at the plasma membrane is implicated in a quarter of all human cancers; however, the underlying mechanism remains unclear. In this work, nanodisc platforms and paramagnetic relaxation enhancement (PRE) analyses to determine the structure of a hetero-tetrameric complex comprising KRAS and the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of activated RAF1 are employed. The binding of the RBD or RBD-CRD differentially alters the dimerization modes of KRAS on both anionic and neutral membranes, validated by interface-specific mutagenesis. Notably, the RBD binding allosterically generated two distinct KRAS dimer interfaces in equilibrium, favored by KRAS free and in complex with the RBD-CRD, respectively. Additional interactions of the CRD with both KRAS protomers are mutually cooperative to stabilize a new dimer configuration of KRAS bound to the RBD-CRD. The RAF binding sequentially alters KRAS dimerization, providing new insights into RAF activation, including a configurational transition of the KRAS dimer to provide an interaction site for the CRD and release the autoinhibited RAF complex. These methods are applicable to many other signaling protein complexes on the membrane.

Enhanced Post-Movement Beta Rebound: Unraveling the Impact of Preplanned Sequential Actions.

The Post-Movement Beta Rebound (PMBR) is the increase in beta-band power after voluntary movement ends, but its specific role in cognitive processing is unclear. Current theory links PMBR with updates to internal models, mental frameworks that help anticipate and react to sensory feedback. However, research has not explored how reactivating a preexisting action plan, another source for internal model updates, might affect PMBR intensity. To address this gap, we recruited 20 participants (mean age 18.55 ± 0.51; 12 females) for an experiment involving isolated (single-step) or sequential (two-step) motor tasks based on predetermined cues. We compared PMBR after single-step movements with PMBR after the first movement in two-step tasks to assess the influence of a subsequent action on the PMBR power associated with the first action. The results show a significant increase in PMBR magnitude after the first movement in sequential tasks compared to the second action and the isolated movements. Notably, this increase is more pronounced for right-hand movements, suggesting lateralized brain activity in the left hemisphere. These findings indicate that PMBR is influenced not only by external stimuli but also by internal cognitive processes such as working memory. This insight enhances our understanding of PMBR's role in motor control, emphasizing the integration of both external and internal information.

Relationship between CCL2 gene 2518A/G (rs1024611) polymorphism and age-related macular degeneration susceptibility: meta-analysis and trial sequential analysis.

PURPOSE: This study aimed to investigate the association between the CC-cytokine ligand-2 (CCL2) 2518A/G (rs1024611) single nucleotide polymorphism (SNP) and susceptibility to age-related macular degeneration (AMD). METHODS: PubMed, Embase, Web of Science, and other databases were searched for articles published before August 24, 2023. After searching, data extraction, and quality assessment, meta-analysis and trial sequential analysis were conducted using RevMan 5.4, Stata 17.0, and TSA 0.9.5.10 Beta software. Combined OR, P values, and 95% confidence intervals (CIs) were calculated. Sensitivity analysis, subgroup analysis and publication bias assessment were also performed. RESULTS: Six articles, comprising 1186 cases and 1124 controls, were included. No significant statistical difference was found in six main outcomes. However, due to observed heterogeneity and high sensitivity, subgroup analysis was performed, revealing statistically significant differences across different regions. No significant publication bias was observed. Trial sequential analysis suggested the need for additional follow-up case-control studies to further validate the findings. CONCLUSION: The CCL2 gene 2518A/G (rs1024611) polymorphism is associated with AMD susceptibility. Among Caucasian populations in West Asia and Europe, the G allele is protective against AMD, whereas in East and South Asia, it poses a risk factor.

Sequential ChIP-Seq.

ChIP-Seq has been used extensively to profile genome-wide transcription factor binding and post-translational histone modifications. A sequential ChIP assay determines the in vivo co-localization of two proteins to the same genomic locus. In this chapter, we combine the two protocols in Sequential ChIP-Seq, a method for identifying genome-wide sites of in vivo protein co-occupancy.

Toxicities associated with sequential or combined use of immune checkpoint inhibitors and small targeted therapies in non-small cell lung cancer: A critical review of the literature.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have become standard-of-care at different stage disease in non-small cell lung cancer (NSCLC). Based on the increasing characterization of molecular aberrations and oncogenic drivers in NSCLC, it is expected that more and more patients will benefit from orally small targeted therapies in NSCLC. However, their concomitant or sequential use is associated with an increased risk of a various toxicity pattern. METHODS: Relevant publications were included if they reported data on the question of toxicities associated with sequential or combined use of ICIs and small targeted therapies used in NSCLC treatment. MEDLINE, Google Scholar, and the Cochrane Library were searched for the following request, from database inception until June 2023. RESULTS: This review highlighted a various pattern of toxicities (i.e., interstitial lung disease, hepatitis, dermatoses) in the context of both sequential and concomitant administration of ICIs and small targeted therapies. Such toxicities seem rather a "drug-effect" than a "class-effect" and some of these toxicities are more specific of a small targeted therapy. This review highlights on the impact of treatment sequence administration and emphasis for physicians to be particularly careful whether small targeted therapy is administered within one to three months after last ICIs injection. CONCLUSION: Physicians have to be aware of severe toxicities in case of both concomitant or sequential ICIs/small targeted therapies administration in NSCLC. Further studies are needed to better understand the mechanisms underlying these toxicities in order to prevent them and to refine ICIs and small targeted therapy sequencing strategy.

Enhancing mutual understanding of e-scooter user's perspective in overtaking maneuver through replaying own driving trajectory.

The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers' viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation.

Injectable immunoregulatory hydrogels sequentially drive phenotypic polarization of macrophages for infected wound healing.

Regulating macrophage phenotypes to reconcile the conflict between bacterial suppression and tissue regeneration is ideal for treating infectious skin wounds. Here, an injectable immunoregulatory hydrogel (SrmE20) that sequentially drives macrophage phenotypic polarization (M0 to M1, then to M2) was constructed by integrating anti-inflammatory components and proinflammatory solvents. In vitro experiments demonstrated that the proinflammatory solvent ethanol stabilized the hydrogel structure, maintained the phenolic hydroxyl group activity, and achieved macrophages' proinflammatory transition (M0 to M1) to enhance antibacterial effects. With ethanol depletion, the hydrogel's cations and phenolic hydroxyl groups synergistically regulated macrophages' anti-inflammatory transition (M1 to M2) to initiate regeneration. In the anti-contraction full-thickness wound model with infection, this hydrogel effectively eliminated bacteria and even achieved anti-inflammatory M2 macrophage accumulation at three days post-surgery, accelerated angiogenesis and collagen deposition. By sequentially driving macrophage phenotypic polarization, this injectable immunoregulatory hydrogel will bring new guidance for the care and treatment of infected wounds.

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis.

Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in 3 years SCIT. Methods: The research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM). SLVM is a probabilistic model that captures the dynamics of patient adherence, while LSTM is a type of recurrent neural network designed to handle time-series data by maintaining long-term dependencies. These models were evaluated based on scoring and adherence prediction capabilities. Results: Excluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60% to 72%, and for LSTM models, it is 66%-84%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55. Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.

Adaptive self-supervised learning for sequential recommendation.

Sequential recommendation typically utilizes deep neural networks to mine rich information in interaction sequences. However, existing methods often face the issue of insufficient interaction data. To alleviate the sparsity issue, self-supervised learning is introduced into sequential recommendation. Despite its effectiveness, we argue that current self-supervised learning-based (i.e., SSL-based) sequential recommendation models have the following limitations: (1) using only a single self-supervised learning method, either contrastive self-supervised learning or generative self-supervised learning. (2) employing a simple data augmentation strategy in either the graph structure domain or the node feature domain. We believe that they have not fully utilized the capabilities of both self-supervised methods and have not sufficiently explored the advantages of combining graph augmentation schemes. As a result, they often fail to learn better item representations. In light of this, we propose a novel multi-task sequential recommendation framework named Adaptive Self-supervised Learning for sequential Recommendation (ASLRec). Specifically, our framework combines contrastive and generative self-supervised learning methods adaptively, simultaneously applying different perturbations at both the graph topology and node feature levels. This approach constructs diverse augmented graph views and employs multiple loss functions (including contrastive loss, generative loss, mask loss, and prediction loss) for joint training. By encompassing the capabilities of various methods, our model learns item representations across different augmented graph views to achieve better performance and effectively mitigate interaction noise and sparsity. In addition, we add a small proportion of random uniform noise to item representations, making the item representations more uniform and mitigating the inherent popularity bias in interaction records. We conduct extensive experiments on three publicly available benchmark datasets to evaluate our model. The results demonstrate that our approach achieves state-of-the-art performance compared to 14 other competitive methods: the hit rate (HR) improved by over 14.39%, and the normalized discounted cumulative gain (NDCG) increased by over 18.67%.

A survey exploring ophthalmologists' attitudes and beliefs in performing immediate sequential bilateral cataract surgery in Singapore.

BACKGROUND: To evaluate current practice patterns of Immediate Sequential Bilateral Cataract Surgery (ISBCS) by ophthalmologists in Singapore and assess their attitudes towards performing ISBCS in future cataract care. METHODS: An anonymised electronic survey, modified to local context from a similar study conducted in the United Kingdom, was distributed to members of the College of Ophthalmologists, Academy of Medicine, Singapore, from 20 June to 1 September 2023. An initial screening question on prior experience with ISBCS directed the rest of the survey. Questions explored ophthalmologists' current ISBCS practice patterns and the importance of factors affecting their willingness to perform ISBCS. Results were descriptively analysed. RESULTS: Results collated 2 months upon survey dissemination saw a total of 58 respondents from 235 eligible members (24.7% response rate). Of these, 16 (27.6%) were currently performing ISBCS, 37 (63.8%) had never performed, and 5 (8.6%) had stopped performing. In considering ISBCS, patient convenience (n = 11, 68.8%) and reduced hospital visits (n = 8, 50.0%) were the most important factors nominated. The most important barriers to performing ISBCS were medico-legal issues (n = 31, 83.8%) and risk of endophthalmitis (n = 27, 73.0%), followed by perceived lack of evidence for its effectiveness (n = 19, 51.4%). CONCLUSION: This is one of the first studies evaluating ophthalmologists' sentiments towards performing ISBCS in an Asian country. It highlights some of the most pertinent barriers and concerns that ophthalmologists face in performing and offering ISBCS. This study provides a better understanding of the potential role and prospects of ISBCS in future cataract care in Singapore.

Interlimb transfer of sequential motor learning between upper and lower effectors.

BACKGROUND: Interlimb transfer of sequential motor learning (SML) refers to the positive influence of prior experiences in performing the same sequential movements using different effectors. Despite evidence from intermanual SML, and while most daily living activities involve interlimb cooperation and coordination between the four limbs, nothing is known about bilateral SML transfer between the upper and lower limbs. RESEARCH QUESTION: We examined the transfer of bilateral SML from the upper to the lower limbs and vice versa. METHODS: Twenty-four participants had to learn an initial bilateral SML task using the upper limbs and then performed the same sequence using the lower limbs during a transfer SML task. They performed the reversed situation 1 month apart. The performance was evaluated at the beginning and the end of both initial and transfer SML practice phases. RESULTS: Significant and reciprocal transfer gains in performance were observed regardless of the effectors. Greater transfer gains in performance were observed at the beginning of the transfer SML from the lower to the upper limbs (44 %) but these gains vanished after practice with the transfer effectors (5 %). Although smaller gains were initially achieved in the transfer of SML from the upper to the lower limbs (15 %), these gains persisted and remained significant (9 %) after practice with the transfer effectors. SIGNIFICANCE: Our results provide evidence of a reciprocal and asymmetrical interlimb transfer of bilateral SML between the upper and lower limbs. These findings could be leveraged as a relevant strategy in the context of sports and functional rehabilitation.

Retrospective study on neonatal seizures in a tertiary center of northern Italy after ILAE classification: Incidence, seizure type, EEG and etiology.

OBJECTIVE: We aimed to evaluate epidemiology, seizure type, EEG, and etiology of neonatal seizures (NS) in a tertiary neonatal intensive care unit. METHODS: Data on infants with a neurophysiological confirmation of NS were collected between 2009 and 2022. Seizure types and epileptic syndromes were classified by the ILAE classification and EEG by the Italian Neonatal Seizure Collaborative Network (INNESCO) score. RESULTS: Out of 91,253 neonates, 145 presented with NS; 69.7 % were born at term and 30.3 % were preterm infants. The incidence of NS in neonates born at our center was 1.2 per 1,000 live newborns (96/80697 neonates) while in the entire neonatal population admitted to our center it was 1.6 per 1,000 live births, increasing with lower preterm age. Compared to previous studies, we found a lower proportion of hypoxic-ischemic encephalopathy (HIE) (23.4 %) and a higher rate of genetic contribution (26.2 %). The infection rate was higher in preterm (31.8 %) than in full term (9.9 %) infants. Electrographic seizures were associated with acute provoked seizures (35.9 %), preterm age (52.3 %), and HIE (52.9 %). Vascular etiology was associated with focal clonic seizures (56.8 %). Non-structural neonatal genetic epilepsy was associated with sequential seizures (68.2 %), particularly KCNQ2 and SCN2A epilepsy. Background EEG was abnormal in all HIE, infections (85.7 %) and metabolic NS (83.3 %). In genetic epilepsy, background EEG depended on the epileptic syndrome: normal in 80 % of self-limited neonatal epilepsy and abnormal in 77.8 % of developmental and epileptic encephalopathy. Electroclinical seizures were associated with focal onset, while electrographic seizures correlated with a multifocal onset. CONCLUSIONS: A low incidence of HIE and a high incidence of genetic etiology were observed in our cohort of NS. Seizure type and EEG features are fundamental to address etiology.

A generalized outcome-adaptive sequential multiple assignment randomized trial design.

A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power.