Dendrimer nanotherapy targeting of glial dysfunction improves inflammation and neurobehavioral phenotype in adult female Mecp2-heterozygous mouse model of Rett syndrome.

Rett syndrome is an X-linked neurodevelopmental disorder caused by mutation of Mecp2 gene and primarily affects females. Glial cell dysfunction has been implicated in in Rett syndrome (RTT) both in patients and in mouse models of this disorder and can affect synaptogenesis, glial metabolism and inflammation. Here we assessed whether treatment of adult (5-6 months old) symptomatic Mecp2-heterozygous female mice with N-acetyl cysteine conjugated to dendrimer (D-NAC), which is known to target glia and modulate inflammation and oxidative injury, results in improved behavioral phenotype, sleep and glial inflammatory profile. We show that unbiased global metabolomic analysis of the hippocampus and striatum in adult Mecp2-heterozygous mice demonstrates significant differences in lipid metabolism associated with neuroinflammation, providing the rationale for targeting glial inflammation in this model. Our results demonstrate that treatment with D-NAC (10 mg/kg NAC) once weekly is more efficacious than equivalently dosed free NAC in improving the gross neurobehavioral phenotype in symptomatic Mecp2-heterozygous female mice. We also show that D-NAC therapy is significantly better than saline in ameliorating several aspects of the abnormal phenotype including paw clench, mobility, fear memory, REM sleep and epileptiform activity burden. Systemic D-NAC significantly improves microglial proinflammatory cytokine production and is associated with improvements in several aspects of the phenotype including paw clench, mobility, fear memory, and REM sleep, and epileptiform activity burden in comparison to saline-treated Mecp2-hetereozygous mice. Systemic glial-targeted delivery of D-NAC after symptom onset in an older clinically relevant Rett syndrome model shows promise in improving neurobehavioral impairments along with sleep pattern and epileptiform activity burden. These findings argue for the translational value of this approach for treatment of patients with Rett Syndrome.

Advances in laser-assisted conversion of polymeric and graphitic carbon into nanodiamond films.

Nanodiamond (ND) synthesis by nanosecond laser irradiation has sparked tremendous scientific and technological interest. This review describes efforts to obtain cost-effective ND synthesis from polymers and carbon nanotubes (CNT) by the melting route. For polymers, ultraviolet (UV) irradiation triggers intricate photothermal and photochemical processes that result in photochemical degradation, subsequently generating an amorphous carbon film; this process is followed by melting and undercooling of the carbon film at rates exceeding 109K s-1. Multiple laser shots increase the absorption coefficient of PTFE, resulting in the growth of 〈110〉 oriented ND film. Multiple laser shots on CNTs result in pseudo topotactic diamond growth to form a diamond fiber. This technique is useful for fabricating 4-50 nm sized NDs. These NDs can further be employed as seed materials that are used in bulk epitaxial growth of microdiamonds using chemical vapor deposition, particularly for use with non-lattice matched substrates that formerly did not form continuous and adherent films. We also provide insights into biocompatible precursors for ND synthesis such as polybenzimidazole fiber. ND fabrication by UV irradiation of graphitic and polymeric carbon opens up a pathway for preparing selective coatings of polymer-diamond composites, doped nanodiamonds, and graphene composites for quantum computing and biomedical applications.

The TimeGeo modeling framework for urban motility without travel surveys.

Well-established fine-scale urban mobility models today depend on detailed but cumbersome and expensive travel surveys for their calibration. Not much is known, however, about the set of mechanisms needed to generate complete mobility profiles if only using passive datasets with mostly sparse traces of individuals. In this study, we present a mechanistic modeling framework (TimeGeo) that effectively generates urban mobility patterns with resolution of 10 min and hundreds of meters. It ties together the inference of home and work activity locations from data, with the modeling of flexible activities (e.g., other) in space and time. The temporal choices are captured by only three features: the weekly home-based tour number, the dwell rate, and the burst rate. These combined generate for each individual: (i) stay duration of activities, (ii) number of visited locations per day, and (iii) daily mobility networks. These parameters capture how an individual deviates from the circadian rhythm of the population, and generate the wide spectrum of empirically observed mobility behaviors. The spatial choices of visited locations are modeled by a rank-based exploration and preferential return (r-EPR) mechanism that incorporates space in the EPR model. Finally, we show that a hierarchical multiplicative cascade method can measure the interaction between land use and generation of trips. In this way, urban structure is directly related to the observed distance of travels. This framework allows us to fully embrace the massive amount of individual data generated by information and communication technologies (ICTs) worldwide to comprehensively model urban mobility without travel surveys.

Enhanced mechanical properties of Q-carbon nanocomposites by nanosecond pulsed laser annealing.

Q-carbon is a metastable phase of carbon formed by melting and subsequently quenching amorphous carbon films by a nanosecond laser in a super undercooled state. As Q-carbon is a material harder than diamond, it makes an excellent reinforcing component inside the softer matrix of a composite coating. In this report, we present a single-step strategy to fabricate adherent coatings of hard and lubricating Q-carbon nanocomposites. These nanocomposites consist of densely-packed sp 3-rich Q-carbon (82% sp 3), and sp 2-rich α-carbon (40% sp 3) amorphous phases. The nanoindentation tests show that the Q-carbon nanocomposites exhibit a hardness of 67 GPa (Young's modulus âˆ¼ 840 GPa) in contrast to the soft α-carbon (hardness âˆ¼ 18 GPa). The high hardness of Q-carbon nanocomposites results in 0.16 energy dispersion coefficient, in comparison with 0.74 for α-carbon. The soft α-carbon phase provides lubrication, resulting in low friction and wear coefficients of 0.09 and 1 × 10-6, respectively, against the diamond tip. The nanoscale dispersion of hard Q-carbon and soft α-carbon phases in the Q-carbon nanocomposites enhances the toughness of the coatings. We present detailed structure-property correlations to understand enhancement in the mechanical properties of Q-carbon nanocomposites. This work provides insights into the characteristics of Q-carbon nanocomposites and advances carbon-based superhard materials for longer lasting protective coatings and related applications.

Evaluation of QTc in Rett syndrome: Correlation with age, severity, and genotype.

Rett syndrome (RTT) is caused by MECP2 mutations, resulting in various neurological symptoms. Prolonged corrected QT interval (QTc) is also reported and is a speculated cause of sudden death in RTT. The purpose of this study was to correlate QTc in RTT patients with age, clinical severity, and genotype. 100 RTT patients (98 females, 2 males) with MECP2 mutations underwent baseline neurological evaluation (KKI-RTT Severity Scale) and QTc measurement (standard 12 lead electrocardiogram) as part of our prospective natural history study. Mean QTc of the cohort was 422.6 msec, which did not exceed the normal values for age. 7/100 patients (7%) had QTc prolongation (>450 msec). There was a trend for increasing QTc with age and clinical severity (P = 0.09). No patients with R106C, R106W, R133C, R168*, R270*, R294*, R306C, R306H, and R306P mutations demonstrated QTc prolongation. There was a relatively high proportion of QTc prolongation in patients with R255* mutations (2/8, 25%) and large deletions (1/4, 25%). The overall presence of QTc prolongation did not correlate with mutation category (P = 0.52). Our findings demonstrate that in RTT, the prevalence of QTc prolongation is lower than previously reported. Hence, all RTT patients warrant baseline ECG; if QTc is prolonged, then cardiac followup is warranted. If initial QTc is normal, then annual ECGs, particularly in younger patients, may not be necessary. However, larger sample sizes are needed to solidify the association between QTc and age and clinical severity. The biological and clinical significance of mild QTc prolongation above the normative data remains undetermined.

BRAT1 mutations present with a spectrum of clinical severity.

Mutations in BRAT1, encoding BRCA1-associated ATM activator 1, are associated with a severe phenotype known as rigidity and multifocal seizure syndrome, lethal neonatal (RMFSL; OMIM # 614498), characterized by intractable seizures, hypertonia, autonomic instability, and early death. We expand the phenotypic spectrum of BRAT1 related disorders by reporting on four individuals with various BRAT1 mutations resulting in clinical severity that is either mild or moderate compared to the severe phenotype seen in RMFSL. Representing mild severity are three individuals (Patients 1-3), who are girls (including two sisters, Patients 1-2) between 4 and 10 years old, with subtle dysmorphisms, intellectual disability, ataxia or dyspraxia, and cerebellar atrophy on brain MRI; additionally, Patient 3 has well-controlled epilepsy and microcephaly. Representing moderate severity is a 15-month-old boy (Patient 4) with severe global developmental delay, refractory epilepsy, microcephaly, spasticity, hyperkinetic movements, dysautonomia, and chronic lung disease. In contrast to RMFSL, his seizure onset occurred later at 4 months of age, and he is still alive. All four of the individuals have compound heterozygous BRAT1 mutations discovered via whole exome sequencing: c.638dupA (p.Val214Glyfs*189); c.803+1G>C (splice site mutation) in Patients 1-2; c.638dupA (p.Val214Glyfs*189); c.419T>C (p.Leu140Pro) in Patient 3; and c.171delG (p.Glu57Aspfs*7); c.419T>C (p.Leu140Pro) in Patient 4. Only the c.638dupA (p.Val214Glyfs*189) mutation has been previously reported in association with RMFSL. These patients illustrate that, compared with RMFSL, BRAT1 mutations can result in both moderately severe presentations evident by later-onset epilepsy and survival past infancy, as well as milder presentations that include intellectual disability, ataxia/dyspraxia, and cerebellar atrophy. © 2016 Wiley Periodicals, Inc.

De novo KCNB1 mutations in epileptic encephalopathy.

OBJECTIVE: Numerous studies have demonstrated increased load of de novo copy number variants or single nucleotide variants in individuals with neurodevelopmental disorders, including epileptic encephalopathies, intellectual disability, and autism. METHODS: We searched for de novo mutations in a family quartet with a sporadic case of epileptic encephalopathy with no known etiology to determine the underlying cause using high-coverage whole exome sequencing (WES) and lower-coverage whole genome sequencing. Mutations in additional patients were identified by WES. The effect of mutations on protein function was assessed in a heterologous expression system. RESULTS: We identified a de novo missense mutation in KCNB1 that encodes the KV 2.1 voltage-gated potassium channel. Functional studies demonstrated a deleterious effect of the mutation on KV 2.1 function leading to a loss of ion selectivity and gain of a depolarizing inward cation conductance. Subsequently, we identified 2 additional patients with epileptic encephalopathy and de novo KCNB1 missense mutations that cause a similar pattern of KV 2.1 dysfunction. INTERPRETATION: Our genetic and functional evidence demonstrate that KCNB1 mutation can result in early onset epileptic encephalopathy. This expands the locus heterogeneity associated with epileptic encephalopathies and suggests that clinical WES may be useful for diagnosis of epileptic encephalopathies of unknown etiology.

Building bridges across electronic health record systems through inferred phenotypic topics.

OBJECTIVE: Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems. METHODS: We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC). RESULTS: The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems. CONCLUSIONS: Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.

Tumour morphology after neoadjuvant chemotherapy as a predictor of survival in serous ovarian cancer: an experience from a tertiary care centre in India.

Serous ovarian cancer is the most common malignant ovarian tumour. Traditional management consists of surgical resection with postoperative chemotherapy. Currently neoadjuvant chemotherapy is offered to patients with advanced stage disease. The present study aims to analyse the histomorphological alterations in serous ovarian cancer following neoadjuvant chemotherapy. Correlation of these morphological alterations with survival is also presented here. Serous ovarian cancers from 100 advanced stage cases were included; 50 were treated with pre-surgery chemotherapy. Semi-quantitative scoring was used to grade the alterations in tumour morphology. Survival data was correlated with the final morphological score. Tumour morphology was significantly different in cases treated with neoadjuvant chemotherapy (CT group) as compared to cases with upfront surgery. The CT group cases showed more fibrosis, calcification, and infiltration by lymphocytes, plasma cells, foamy and hemosiderin-laden macrophages. The residual tumour cells had degenerative cytoplasmic changes with nuclear atypia. Patients with significant morphological response had a longer median survival, although it did not attain statistical significance in the current study. With the increasing use of neoadjuvant chemotherapy in management, the pathologist needs to be aware of the altered morphological appearance of tumour. Further studies are required to establish a grading system to assess the tissue response which can be helpful in predicting the overall therapeutic outcome and the prognosis of patients.

Effects of Surface Abrasion on the Flexural Strength of Glazed and Re-glazed Metal Ceramics: An In Vitro Study.

The functional surfaces of the porcelain fused to metal fixed partial dentures are often abraded to adjust occlusion, such restorations are often found to fail in service. This study was therefore conducted to study the effect of surface abrasion on flexural strength of glazed porcelain fused to metal samples. It was also the aim of this study to find the effect of re-glazing on flexural strength of abraded samples. A total of ninety glazed porcelain fused to metal bar samples of the dimension 15 mm × 2 mm × 1.5 mm were fabricated. These samples were then divided into three groups (30 samples each) according to the surface treatments: group A-glazed (control); group B-abraded and group C-abraded and then re-glazed (self-glazed). Flexural strength was measured by using three point bend test on universal testing machine (texture analyser) with a cross-head speed of 0.6 mm/min. Peak force at the time of failure for all the samples was recorded. Statistical analysis found that mean flexural strength was highest for group A-80.65 ± 12.81 MPa; as compared to group B-74.18 ± 10.74 MPa and group C-77.85 ± 9.39 MPa. Student's t test indicated that the difference in the flexural strength between groups A and B was significant while it was non-significant between groups B and C and also between groups A and C. The 'f' test indicated that the difference between the groups was non-significant. This study therefore showed that there is a marked decrease in the flexural strength of the porcelain fused to metal restorations after occlusal abrasion. The study also found that reglazing of these restorations may not restore their flexural strength significantly.

Lateralized Rhythmic Delta Activity and Lateralized Periodic Discharges in Critically Ill Pediatric Patients.

PURPOSE: To evaluate the clinical and electrographic characteristics of critically ill pediatric patients with lateralized rhythmic delta activity (LRDA) and compare them with patients with lateralized periodic discharges (LPDs). METHODS: This was a retrospective study examining consecutive critically ill pediatric patients (1 month-18 years) with LRDA or LPDs monitored on continuous electroencephalography. Clinical, radiologic, and electrographic characteristics; disease severity; and acute sequelae were compared between the two groups. RESULTS: Of 668 pediatric patients monitored on continuous electroencephalography during the study period, 12 (1.79%) patients had LRDA and 15 (2.24%) had LPDs. The underlying etiologies were heterogeneous with no difference in the acuity of brain MRI changes between both groups. Lateralized rhythmic delta activity and LPDs were concordant with the side of MRI abnormality in most patients [85.7% (LRDA) and 83.3% (LPD)]. There was no difference in the measures of disease severity between both groups. Seizures were frequent in both groups (42% in the LRDA group and 73% in the LPD group). Patients in the LPD group had a trend toward requiring a greater number of antiseizure medications for seizure control (median of 4 vs. 2 in the LRDA group, p = 0.09), particularly those patients with LPDs qualifying as ictal-interictal continuum compared with those without ictal-interictal continuum (p = 0.02). CONCLUSIONS: Lateralized rhythmic delta activity and LPDs are uncommon EEG findings in the pediatric population. Seizures occur commonly in patients with these patterns. Seizures in patients with LPDs, especially those qualifying as ictal-interictal continuum, showed a trend toward being more refractory. Larger studies are needed in the future to further evaluate these findings.

Electrolyte selection toward efficient photoelectrochemical glycerol oxidation on BiVO4.

Glycerol, a primary by-product of biodiesel production, can be oxidized into various value-added chemicals, significantly enhancing the techno-economic value of photoelectrochemical (PEC) cells. Several studies have explored various photoelectrode materials and co-catalysts, but the influence of electrolytes on PEC glycerol oxidation has remained relatively unexplored despite its significance. Here, we explore the impact of various acidic (pH = 2) electrolytes, namely NaNO3, NaClO4, Na2SO4, K2SO4, and KPi, on PEC glycerol oxidation using nanoporous thin film BiVO4 as a model photoanode. Our experimental findings reveal that the choice of electrolyte anion and cation significantly affects the PEC performance (i.e., photocurrent, onset potential, stability, and selectivity towards value-added products) of BiVO4 for glycerol oxidation. To explain this interesting phenomenon, we correlate the observed performance trend with the ion specificity in the Hofmeister series as well as the buffering capacity of the electrolytes. Notably, NaNO3 is identified as the optimal electrolyte for PEC glycerol oxidation with BiVO4 when considering various factors such as stability and production rates for glycerol oxidation reaction (GOR) products, surpassing the previously favored Na2SO4. Glycolaldehyde emerges as the most dominant product with ∼50% selectivity in NaNO3. The general applicability of our findings is confirmed by similar observation in electrochemical (EC) GOR with a polycrystalline platinum anode. Overall, these results emphasize the critical role of electrolyte selection in enhancing the efficiency of EC/PEC glycerol oxidation.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

BACKGROUND: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. METHODOLOGY: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. RESULTS: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. CONCLUSIONS: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Proinflammatory phenotype of iPS cell-derived JAK2 V617F megakaryocytes induces fibrosis in 3D in vitro bone marrow niche.

The myeloproliferative disease polycythemia vera (PV) driven by the JAK2 V617F mutation can transform into myelofibrosis (post-PV-MF). It remains an open question how JAK2 V617F in hematopoietic stem cells induces MF. Megakaryocytes are major players in murine PV models but are difficult to study in the human setting. We generated induced pluripotent stem cells (iPSCs) from JAK2 V617F PV patients and differentiated them into megakaryocytes. In differentiation assays, JAK2 V617F iPSCs recapitulated the pathognomonic skewed megakaryocytic and erythroid differentiation. JAK2 V617F iPSCs had a TPO-independent and increased propensity to differentiate into megakaryocytes. RNA sequencing of JAK2 V617F iPSC-derived megakaryocytes reflected a proinflammatory, profibrotic phenotype and decreased ribosome biogenesis. In three-dimensional (3D) coculture, JAK2 V617F megakaryocytes induced a profibrotic phenotype through direct cell contact, which was reversed by the JAK2 inhibitor ruxolitinib. The 3D coculture system opens the perspective for further disease modeling and drug discovery.

Reimagining Carbon Nanomaterial Analysis: Empowering Transfer Learning and Machine Vision in Scanning Electron Microscopy for High-Fidelity Identification.

In this report, we propose a novel technique for identifying and analyzing diverse nanoscale carbon allotropes using scanning electron micrographs. By precisely controlling the quenching rates of undercooled molten carbon through laser irradiation, we achieved the formation of microdiamonds, nanodiamonds, and Q-carbon films. However, standard laser irradiation without proper undercooling control leads to the formation of sparsely located diverse carbon polymorphs, hindering their discovery and classification through manual analyses. To address this challenge, we applied transfer-learning approaches using convolutional neural networks and computer vision techniques to achieve allotrope discovery even with sparse spatial presence. Our method achieved high accuracy rates of 92% for Q-carbon identification and 94% for distinguishing it from nanodiamonds. By leveraging scanning electron micrographs and precise undercooling control, our technique enables the efficient identification and characterization of nanoscale carbon structures. This research significantly contributes to the advancement of the field, providing automated tools for Q-materials and carbon polymorph identification. It opens up new opportunities for the further exploration of these materials in various applications.

Case report: Off-label use of low-dose perampanel in a 25-month-old girl with a pathogenic SYNGAP1 variant.

Introduction: Preclinical studies in a mouse model have shown that SYNGAP1 haploinsufficiency results in an epilepsy phenotype with excessive GluA2-AMPA insertion specifically on the soma of fast-spiking parvalbumin-positive interneurons associated with significant dysfunction of cortical gamma homeostasis that was rescued by perampanel (PER), an AMPA receptor blocker. In this single case, we aimed to investigate the presence of dysregulated cortical gamma in a toddler with a pathogenic SYNGAP1 variant and report on the effect of low-dose PER on electroencephalogram (EEG) and clinical profile. Methods: Clinical data from physician's clinic notes; genetic testing reports; developmental scores from occupational therapy, physical therapy, speech and language therapy evaluations; and applied behavioral analysis reports were reviewed. Developmental assessments and EEG analysis were done pre- and post-PER. Results: Clinically, the patient showed improvements in the developmental profile and sleep quality post-PER. EEG spectral power analysis in our patient revealed a loss of gamma power modulation with behavioral-state transitions similar to what was observed in Syngap1+/- mice. Furthermore, the administration of low-dose PER rescued the dysfunctional cortical gamma homeostasis, similar to the preclinical study. However, as in the epileptic mice, PER did not curb epileptiform discharges or clinical seizures. Conclusion: Similar to the Syngap1+/- mice, cortical gamma homeostasis was dysregulated in the patient. This dysfunction was rescued by PER. These encouraging results necessitate further validation of gamma dysregulation as a potential translational EEG biomarker in SYNAP1-DEE. Low-dose PER can be explored as a therapeutic option through clinical trials.

Central Granular Cell Odontogenic Tumor of the Mandible: An Uncommon Presentation.

Central granular cell odontogenic tumor (CGCOT) is a rare, benign odontogenic tumor resulting from the jaw bone, especially the mandible or maxilla. It affects women of middle age and usually occurs as a painless swelling of the mandibular premolar-molar area. CGCOT is characterized by the presence of granular cells, which are large, eosinophilic, granular-looking cells found in the tumor tissue. We report an unusual CGCOT in a 38-year-old male patient's mandibular region. We also describe the clinical, radiological, and pathological characteristics along with the immunohistochemical investigation of the tumor.

Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers.

Membrane electrode assemblies enable CO2 electrolysis at industrially relevant rates, yet their operational stability is often limited by formation of solid precipitates in the cathode pores, triggered by cation crossover from the anolyte due to imperfect ion exclusion by anion exchange membranes. Here we show that anolyte concentration affects the degree of cation movement through the membranes, and this substantially influences the behaviors of copper catalysts in catholyte-free CO2 electrolysers. Systematic variation of the anolyte (KOH or KHCO3) ionic strength produced a distinct switch in selectivity between either predominantly CO or C2+ products (mainly C2H4) which closely correlated with the quantity of alkali metal cation (K+) crossover, suggesting cations play a key role in C-C coupling reaction pathways even in cells without discrete liquid catholytes. Operando X-ray absorption and quasi in situ X-ray photoelectron spectroscopy revealed that the Cu surface speciation showed a strong dependence on the anolyte concentration, wherein dilute anolytes resulted in a mixture of Cu+ and Cu0 surface species, while concentrated anolytes led to exclusively Cu0 under similar testing conditions. These results show that even in catholyte-free cells, cation effects (including unintentional ones) significantly influence reaction pathways, important to consider in future development of catalysts and devices.

Poly(ionic liquid) nanovesicles via polymerization induced self-assembly and their stabilization of Cu nanoparticles for tailored CO2 electroreduction.

Herein, we report a straightforward, scalable synthetic route towards poly(ionic liquid) (PIL) homopolymer nanovesicles (NVs) with a tunable particle size of 50 to 120 nm and a shell thickness of 15 to 60 nm via one-step free radical polymerization induced self-assembly. By increasing monomer concentration for polymerization, their nanoscopic morphology can evolve from hollow NVs to dense spheres, and finally to directional worms, in which a multilamellar packing of PIL chains occurred in all samples. The transformation mechanism of NVs' internal morphology is studied in detail by coarse-grained simulations, revealing a correlation between the PIL chain length and the shell thickness of NVs. To explore their potential applications, PIL NVs with varied shell thickness are in situ functionalized with ultra-small (1 âˆ¼ 3 nm in size) copper nanoparticles (CuNPs) and employed as electrocatalysts for CO2 electroreduction. The composite electrocatalysts exhibit a 2.5-fold enhancement in selectivity towards C1 products (e.g., CH4), compared to the pristine CuNPs. This enhancement is attributed to the strong electronic interactions between the CuNPs and the surface functionalities of PIL NVs. This study casts new aspects on using nanostructured PILs as new electrocatalyst supports in CO2 conversion to C1 products.