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The sympathetic nervous system (SNS) is a major regulatory mediator connecting the brain and the immune system that influences accordingly inflammatory processes within the entire body. In the periphery, the SNS exerts its effects mainly via its neurotransmitters norepinephrine (NE) and epinephrine (E), which are released by peripheral nerve endings in lymphatic organs and other tissues. Depending on their concentration, NE and E bind to specific α- and β-adrenergic receptor subtypes and can cause both pro- and anti-inflammatory cellular responses. The co-transmitter neuropeptide Y, adenosine triphosphate, or its metabolite adenosine are also mediators of the SNS. Local pro-inflammatory processes due to injury or pathogens lead to an activation of the SNS, which in turn induces several immunoregulatory mechanisms with either pro- or anti-inflammatory effects depending on neurotransmitter concentration or pathological context. In chronic inflammatory diseases, the activity of the SNS is persistently elevated and can trigger detrimental pathological processes. Recently, the sympathetic contribution to mild chronic inflammatory diseases like osteoarthritis (OA) has attracted growing interest. OA is a whole-joint disease and is characterized by mild chronic inflammation in the joint. In this narrative article, we summarize the underlying mechanisms behind the sympathetic influence on inflammation during OA pathogenesis. In addition, OA comorbidities also accompanied by mild chronic inflammation, such as hypertension, obesity, diabetes, and depression, will be reviewed. Finally, the potential of SNS-based therapeutic options for the treatment of OA will be discussed.
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition with an onset in early development. ASD has varying degrees of severity and thus affects people differently throughout their lives. Early diagnosis of ASD is essential to provide children with individually-tailored support.8 Eye-tracking may contribute to an earlier diagnosis: Several studies showed differences in eye movements between people with autism spectrum disorder (ASD) and typically developing controls (TD). Different eye movements may contribute to different visual perception that perpetuates to problems in attention, communication and social interaction.
Eye movements are divided into: (1) Fixations (2) Saccades (fast and short eye movements) and (3) Smooth Pursuit Eye Movements (SPEM). SPEM follow the target in a continuous manner. The latter are the subject of the present thesis. SPEM consist of two phases: the open loop phase (= phase of initiation, first 50- 100ms) and the closed loop phase (= phase of maintenance, after about 100ms). SPEM are usually measured by a gain index. It is defined as the ratio of smooth pursuit velocity and visual target velocity and ideally equals to 1.2
In young children, corneal-reflection (CR) eye-tracking is usually applied to quantify eye movement. It allows precise measurements without the use of potentially intrusive devices.
Studies in ASD reported deficits in open loop and closed loop pursuit in children and adults with a mean age of 19.32 (TD) and 20.04 (ASD) years. However, SPEM in preschoolers with ASD remain understudied, although this developmental phase is crucial to the development of non-social and social attentional abilities.
In the present study 66 toddlers and preschoolers (18 to 72 months; ASD: n = 33, TD: n = 33) with matched cognitive abilities and sex were assessed. The main objective was to compare the gain index (Smooth Pursuit Gain = SPG). SPEM were compared between groups with gain index as a dependent measure. We hypothesized that participants with ASD show lower average gain compared to the control group.
We could show a significant group influence on the gain when considering interactions between target velocity and group (p = 0.041). The TD group showed a greater dependence on the increasing object speed than the ASD group with a trend of -0.30 ± 0.11 in the TD group and a trend of -0.13 ± 0.12 in the ASD group. Across groups, the gain decreased with increasing target velocity and dropped faster in vertical than in horizontal trials. Additionally, participants showed a lower SPG in vertical sequences than in horizontal sequences. This supports the general validity of the measure.
Toddlers and preschoolers represent a group that has been subject of little research to date. In addition, there has been only a limited number of studies analyzing SPEM in ASD. To check for a possible group difference without interactions a study with a larger sample size at fixed target velocity and target direction should follow.
Highlights
• Reduced evoked theta activity in the deaf.
• Reduced theta-gamma and alpha-gamma cross-frequency couplings in the deaf.
• Stronger delta-alpha coupling in the deaf.
Abstract
Neurons within a neuronal network can be grouped by bottom-up and top-down influences using synchrony in neuronal oscillations. This creates the representation of perceptual objects from sensory features. Oscillatory activity can be differentiated into stimulus-phase-locked (evoked) and non-phase-locked (induced). The former is mainly determined by sensory input, the latter by higher-level (cortical) processing. Effects of auditory deprivation on cortical oscillations have been studied in congenitally deaf cats (CDCs) using cochlear implant (CI) stimulation. CI-induced alpha, beta, and gamma activity were compromised in the auditory cortex of CDCs. Furthermore, top-down information flow between secondary and primary auditory areas in hearing cats, conveyed by induced alpha oscillations, was lost in CDCs. Here we used the matching pursuit algorithm to assess components of such oscillatory activity in local field potentials recorded in primary field A1. Additionally to the loss of induced alpha oscillations, we also found a loss of evoked theta activity in CDCs. The loss of theta and alpha activity in CDCs can be directly related to reduced high-frequency (gamma-band) activity due to cross-frequency coupling. Here we quantified such cross-frequency coupling in adult 1) hearing-experienced, acoustically stimulated cats (aHCs), 2) hearing-experienced cats following acute pharmacological deafening and subsequent CIs, thus in electrically stimulated cats (eHCs), and 3) electrically stimulated CDCs. We found significant cross-frequency coupling in all animal groups in > 70% of auditory-responsive sites. The predominant coupling in aHCs and eHCs was between theta/alpha phase and gamma power. In CDCs such coupling was lost and replaced by alpha oscillations coupling to delta/theta phase. Thus, alpha/theta oscillations synchronize high-frequency gamma activity only in hearing-experienced cats. The absence of induced alpha and theta oscillations contributes to the loss of induced gamma power in CDCs, thereby signifying impaired local network activity.
Graph4Med: a web application and a graph database for visualizing and analyzing medical databases
(2022)
Background: Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit.
Description: We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients’ health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients’ cohort analysis. This way our tool (1) quickly displays the overview of patients’ cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients.
Conclusion: We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research.
A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer’s disease) and 14 studies encompassing ∼35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen’s d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 x 10−3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.
Purpose: To evaluate intermediate and long-term visual outcomes and safety of a phakic intraocular posterior chamber lens with a central hole (ICL V4c) for myopic eyes.
Methods: Retrospective, consecutive case study of patients that uneventfully received a ICL V4c for myopia correction, with a 5-year postoperative follow-up. Department of Ophthalmology, Goethe University Frankfurt, Germany.
Results: From 241 eyes that underwent ICL implantation, we included 45 eyes with a mean age at surgery of 33 years ± 6 (18–48 years), with a 5 years follow-up. CDVA improved from 0.05logMAR ± 0.15 CDVA preoperatively to − 0.00 ± 0,07 at 5 years and did not change significantly from 3 to 5 years’ time (p = 0.266). The mean spherical equivalent (SE) improved from -10.13D ± 3.39 to − 0.45D ± 0.69. The change in endothelial cell count showed a mean decrease of 1.9% per year throughout the follow-up. Safety and efficacy index were 1.16 and 0.78, respectively. Cataract formation was seen in 2 of 241 eyes (0.8%), but in none of the 45 eyes that finished the 5-year follow-up.
Conclusions: Our data show a good intermediate and long-term stability, efficiency, and safety of ICL V4c phakic lenses in myopic eyes comparable to other known literature.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.