Design of quetiapine fumarate loaded polyethylene glycol decorated graphene oxide nanosheets: Invitro-exvivo characterization.

Quetiapine Fumarate (QF) is an atypical antipsychotic with poor oral bioavailability (9%) due to its low permeability and pH-dependent solubility. Therefore, this study aims to design QF-loaded polyethylene glycol (PEG) functionalized graphene oxide nanosheets (GON) for nasal delivery of QF. In brief, GO was synthesized using a modified Hummers process, followed by ultra-sonication to produce GON. Subsequently, PEG-functionalized GON was prepared using carbodiimide chemistry (PEG-GON). QF was then decorated onto the cage of PEG-GON using the π-π stacking phenomenon (QF@PEG-GON). The QF@PEG-GON nanocomposite underwent several spectral characterizations, in vitro drug release, mucoadhesion study, ex vivo diffusion study, etc. The surface morphology of QF@PEG-GON nanocomposite validates the cracked nature of the nanocomposite, whereas the diffractograms and thermogram of nanocomposite confirm the conversion of QF into an amorphous form with uniform distribution in PEG-GON. Moreover, an ex vivo study of PEG-GON demonstrates superior mucoadhesion capacity due to its surface functional groups and hydrophilicity. The percent drug loading content and percent entrapment efficiency of the nanocomposite were found to be 9.2±0.62% and 92.3±1.02%, respectively. The developed nanocomposite exhibited 43.82±1.65% drug release within 24h, with the Korsemeyer-Peppas model providing the best-fit release kinetics (R2: 0.8614). Here, the interlayer spacing of PEG-GON prevented prompt diffusion of the buffer, leading to a delayed release pattern. In conclusion, the anticipated QF@PEG-GON nanocomposite shows promise as a nanocarrier platform for nasal delivery of QF.

Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition.

BACKGROUND: Mild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aß) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships. METHOD: We integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients. RESULTS: Deep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter. CONCLUSION: Deep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.

Neutrophilic Panniculitides.

Neutrophilic panniculitides are a heterogeneous group of inflammatory disorders encompassing many different entities. This review article focuses on the epidemiology, pathogenesis, clinicopathological features, diagnosis, and treatment of selected diseases. Patients often seek care due to systemic involvement, but the variable presentation of panniculitides can present a diagnostic challenge. Most therapeutic modalities for neutrophilic disorders are anecdotal at best with a notable lack of standardization of the responses to medications. There is an urgent need for a larger multi-institutional collaboration to address the unmet needs of these challenging, yet rare conditions.

Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling.

Introduction: Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. Methods: In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (n = 67) and healthy controls (n = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. Results: In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney U test; p < 10-16). Conclusions: These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning.

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aß) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aß biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aß biomarkers and identify the Aß-related dominant brain regions involved with cognitive impairment. We employed Aß biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aß biomarkers on the test set. To identify Aß-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aß in MCI compared to controls and a stronger correlation between Aß and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aß biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aß biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aß biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability.

Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Two Neuroanatomical Signatures in Schizophrenia: Expression Strengths Over the First 2 Years of Treatment and Their Relationships to Neurodevelopmental Compromise and Antipsychotic Treatment.

BACKGROUND AND HYPOTHESIS: Two machine learning derived neuroanatomical signatures were recently described. Signature 1 is associated with widespread grey matter volume reductions and signature 2 with larger basal ganglia and internal capsule volumes. We hypothesized that they represent the neurodevelopmental and treatment-responsive components of schizophrenia respectively. STUDY DESIGN: We assessed the expression strength trajectories of these signatures and evaluated their relationships with indicators of neurodevelopmental compromise and with antipsychotic treatment effects in 83 previously minimally treated individuals with a first episode of a schizophrenia spectrum disorder who received standardized treatment and underwent comprehensive clinical, cognitive and neuroimaging assessments over 24 months. Ninety-six matched healthy case-controls were included. STUDY RESULTS: Linear mixed effect repeated measures models indicated that the patients had stronger expression of signature 1 than controls that remained stable over time and was not related to treatment. Stronger signature 1 expression showed trend associations with lower educational attainment, poorer sensory integration, and worse cognitive performance for working memory, verbal learning and reasoning and problem solving. The most striking finding was that signature 2 expression was similar for patients and controls at baseline but increased significantly with treatment in the patients. Greater increase in signature 2 expression was associated with larger reductions in PANSS total score and increases in BMI and not associated with neurodevelopmental indices. CONCLUSIONS: These findings provide supporting evidence for two distinct neuroanatomical signatures representing the neurodevelopmental and treatment-responsive components of schizophrenia.

Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population.

Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] ß, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

Numerical Methodology to Evaluate Trackability and Pushability of PTCA Balloon Catheter.

PURPOSE: During percutaneous coronary intervention (PCI), the ability to navigate a catheter without causing injury to the vessel and damage to the device is crucial outcome of the procedure. This study aimed to develop a numerical model to analyse the percutaneous transluminal coronary angioplasty (PTCA) catheter navigation in coronary vessels. METHODS: Trackability and pushability are two major parameters used to characterize the navigation of PTCA balloon catheters, and they are influenced by vessel tortuosity, contact interactions and catheter design. In the current study, finite element analysis model is presented to evaluate trackability and pushability considering two different vessel geometries. Impact of contact interactions among catheter, guidewire, and vessel were studied to validate the numerical model with in vitro test data. Further, a parametric study was conducted to understand the influence of distal shaft, and proximal shaft outer diameter. RESULTS: Obtained results suggest that contact interaction and co-efficient of friction between guidewire and catheter are critical parameters to obtain numerical results comparable to experimental data. Results from the parametric study predicted strong positive correlation of distal shaft diameter on pushability, and weak correlation on trackability force. Furthermore, parametric variation in proximal shaft diameter has strong positive correlation on trackability force and strong negative correlation on pushability. CONCLUSION: Numerical methodology presented in this study is a preliminary attempt to simulate the behavior of PTCA balloon catheter navigation. This methodology will be helpful in the design and optimization of PTCA balloon catheter and similar devices with improved deliverability.

Design and Development of Gastro-retentive Drug Delivery System for Trazodone Hydrochloride: a Promising Alternative to Innovator's Controlled-Release Tablet.

Trazodone hydrochloride (TZN) is a serotonin reuptake inhibitor that treats a major depressive disorder. It exhibits a short plasma half-life of 4.1 h and shows pH-dependent solubility. Above its pKa (6.74), solubility of TZN is very low, affecting its dissolution in the lower part of GIT. Hence, the present work aimed to develop gastro-retentive floating tablet of TZN. Central composite design was employed to optimize the formulation. Formulation variables like the concentration of HPMC-K100M, Polyox WSR 303 Leo, and sodium bicarbonate were evaluated for the responses like floating lag time and drug release. X-ray imaging study was performed on rabbits to determine the in vivo gastric retention of the optimized formulation. The accelerated stability study was conducted on optimized tablets as per ICH guidelines. Floating lag time and f2 value of the optimized formulation were found to be 2.51±0.02 min and 62.79, respectively. X-ray imaging studies in rabbits determined the in vivo gastro retention time. After 12 h of administration, tablet remained in the gastric region, indicating better retentive power. Accelerated stability studies showed sufficient formulation stability even after 3 months of storage. All these studies depict that the floating gastro-retentive system could be used as an alternative to the innovator formulation.

Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population.

OBJECTIVE: The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS: This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS: Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS: The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Differential Sphingosine-1-Phosphate Receptor-1 Protein Expression in the Dorsolateral Prefrontal Cortex Between Schizophrenia Type 1 and Type 2.

Understanding the etiology and treatment approaches in schizophrenia is challenged in part by the heterogeneity of this disorder. One encouraging progress is the growing evidence that there are subtypes of schizophrenia. Recent in vitro findings of messenger ribonucleic acid (mRNA) gene expression on postmortem dorsolateral prefrontal cortex (DLPFC) showed that schizophrenia has two subtypes, those with a relatively normal DLPFC transcriptome (Type 1) and those with differentially expressed genes (Type 2). Sphingosine-1-phosphate receptor-1 (S1PR1) is one of the genes that was highly upregulated in Type 2 compared to Type 1 and controls. The impact of that finding is limited because it only can be confirmed through analysis of autopsy tissue, and the clinical characteristics such as symptoms severity or illness duration except for cause of death was not available from that Medical Examiner based autopsy study. However, S1PR1 has great potential because it is a target gene that can be accessed via positron emission tomography (PET) in vivo using specific radioligands (starting with [11C]CS1P1) successfully developed at our center in human brain imaging. As a preliminary study to validate this PET target in schizophrenia, S1PR1 protein expression was assessed by receptor autoradiography (ARG) using [3H]CS1P1 and immunohistochemistry (IHC) in the DLPFC from patients with schizophrenia classified as Type 1 or Type 2 based on their DLPFC transcriptomes and from controls. Our analyses demonstrate that ARG S1PR1 protein expression is significantly higher in Type 2 compared to Type 1 (p < 0.05) and controls (p < 0.05), which was consistent with previous mRNA S1PR1. These findings support the possibility that PET S1PR1 can be used as a future imaging biomarker to distinguish these subgroups of schizophrenic patients during life with obvious implications for both patient management and the design of clinical trials to validate novel pharmacologic therapies.

Salience network anatomical and molecular markers are linked with cognitive dysfunction in mild cognitive impairment.

BACKGROUND AND PURPOSE: Recent studies indicate disrupted functional mechanisms of salience network (SN) regions-right anterior insula, left anterior insula, and anterior cingulate cortex-in mild cognitive impairment (MCI). However, the underlying anatomical and molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multimodal-anatomical and molecular-markers could predict cognitive impairment better in MCI. METHODS: Herein we quantified anatomical volumetric markers via structural MRI and molecular amyloid markers via PET with Pittsburgh compound B in SN regions of MCI (n = 33) and healthy controls (n = 27). From these markers, we built support vector machine learning models aiming to estimate cognitive dysfunction in MCI. RESULTS: We found that anatomical markers are significantly reduced and molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < .05). These altered markers in MCI patients were associated with their worse cognitive performance (p < .05). Our machine learning-based modeling further suggested that the integration of multimodal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers. CONCLUSIONS: These findings shed light on the underlying anatomical volumetric and molecular amyloid alterations in SN regions and show the significance of multimodal markers integration approach in better predicting cognitive impairment in MCI.

Numerical Analysis for Non-Uniformity of Balloon-Expandable Stent Deployment Driven by Dogboning and Foreshortening.

PURPOSE: Stenting is the most common intervention for arteriosclerosis treatment; however, the success of the treatment depends on the incidence of in-stent restenosis (ISR). Stent deployment characteristics are major influencers of ISR and can be measured in terms of dogboning, asymmetry, and foreshortening. This study aimed to analyse the implications of balloon and stent-catheter assembly parameters on the stent deployment characteristics. METHODS: Experimental approach to analyse the impact of the balloon and stent-catheter assembly parameters on stent deployment characteristics is a time-consuming and complex task, whereas numerical methods prove to be quick, efficient, and reliable. In this study, eleven finite element models were employed to analyse non-uniform balloon stent expansion pattern, comprised of variation in, stent axial position on balloon, balloon length, balloon folding pattern, and balloon wall thickness. RESULTS: Obtained results suggest that the axially noncentral position of the stent on balloon and variable balloon thickness lead to non-uniform stent deployment pattern. Also, it was proved that variation in balloon length and balloon folding pattern influence deployment process. CONCLUSION: Improved positional accuracies, uniform balloon wall thickness, and selection of the appropriate length of a balloon for selected stent configuration will help to minimize dogboning, asymmetry, and foreshortening during non-uniform stent expansion, thereby reducing the risk of restenosis. The stated numerical approach will be helpful to optimize stent catheter assembly parameters thus minimizing in-vitro tests and product development time.

Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes.

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, "Multi-scAle heteroGeneity analysIs and Clustering" (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N = 4403). We then applied MAGIC to imaging data from Alzheimer's disease (ADNI, N = 1728) and schizophrenia (PHENOM, N = 1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye.

Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.

Identification of a Novel Series of Potent Organosilicon Mosquito Repellents.

Mosquito control by personal protection is one of the most efficient ways of curtailing deadly diseases such as malaria and dengue with the potential to save millions of lives per year. DEET (N,N-diethyl-3-methyl benzamide) is currently considered as the gold standard for mosquito repellents, being used for the past several decades. Control by DEET, however, is being threatened by emerging resistance among mosquitoes. To address this concern and also to improve protection times, we synthesized a novel series of 25 silicon-containing acyl piperidines using acid-amine coupling protocol and tested their activity against Aedes aegypti in mosquito-repellent assays. Several compounds from this series appear to possess good mosquito-repellent properties. Most notably, at 0.5 mg/cm2 concentrations, the mean protection time for NDS100100 was 756 min, which was higher than that of DEET (616 min). The details of design, synthesis, and biological evaluation are discussed herein.

Neurocognitive and functional heterogeneity in depressed youth.

Depression is a common psychiatric illness that often begins in youth, and is sometimes associated with cognitive deficits. However, there is significant variability in cognitive dysfunction, likely reflecting biological heterogeneity. We sought to identify neurocognitive subtypes and their neurofunctional signatures in a large cross-sectional sample of depressed youth. Participants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (MDD n = 368, TD n = 200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A recently developed semi-supervised machine learning algorithm was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task. We identified three neurocognitive subtypes in the depressed group. Subtype 1 was high-performing (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), and Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition. Taken together, these data suggest disparate mechanisms of cognitive vulnerability and resilience in depressed youth, which may inform the identification of biomarkers for prognosis and treatment response.

Newer variants of retroaortic left renal vein.

The left renal vein (LRV) passing behind the abdominal aorta is termed as a retroaortic LRV (RLRV) and it is a relatively uncommon condition. Since the left kidney is preferred in the setting of live donor kidney transplantation, urologists must be familiar with the anomalies of the LRV. There are four variants of RLRV mentioned in the literature. However, we came across two newer variants of RLRV in two donors for renal transplantation. Both donors underwent successful left donor nephrectomy.