Heterozygous NRXN1 deletions constitute the most prevalent currently known single-gene mutation associated with schizophrenia, and additionally predispose to multiple other neurodevelopmental disorders. Engineered heterozygous NRXN1 deletions impaired neurotransmitter release in human neurons, suggesting a synaptic pathophysiological mechanism. Utilizing this observation for drug discovery, however, requires confidence in its robustness and validity. Here, we describe a multicenter effort to test the generality of this pivotal observation, using independent analyses at two laboratories of patient-derived and newly engineered human neurons with heterozygous NRXN1 deletions. Using neurons transdifferentiated from induced pluripotent stem cells that were derived from schizophrenia patients carrying heterozygous NRXN1 deletions, we observed the same synaptic impairment as in engineered NRXN1-deficient neurons. This impairment manifested as a large decrease in spontaneous synaptic events, in evoked synaptic responses, and in synaptic paired-pulse depression. Nrxn1-deficient mouse neurons generated from embryonic stem cells by the same method as human neurons did not exhibit impaired neurotransmitter release, suggesting a human-specific phenotype. Human NRXN1 deletions produced a reproducible increase in the levels of CASK, an intracellular NRXN1-binding protein, and were associated with characteristic gene-expression changes. Thus, heterozygous NRXN1 deletions robustly impair synaptic function in human neurons regardless of genetic background, enabling future drug discovery efforts.

A high neutrophil to lymphocyte ratio (NLR) is considered an unfavorable prognostic factor in various diseases, including COVID-19. The prognostic value of NLR in other respiratory viral infections, such as Influenza, has not hitherto been extensively studied. We aimed to compare the prognostic value of NLR in COVID-19, Influenza and Respiratory Syncytial Virus infection (RSV). A retrospective cohort of COVID-19, Influenza and RSV patients admitted to the Tel Aviv Medical Center from January 2010 to October 2020 was analyzed. Laboratory, demographic, and clinical parameters were collected. Two way analyses of variance (ANOVA) was used to compare the association between NLR values and poor outcomes among the three groups. ROC curve analyses for each virus was applied to test the discrimination ability of NLR. 722 COVID-19, 2213 influenza and 482 RSV patients were included. Above the age of 50, NLR at admission was significantly lower among COVID-19 patients (P < 0.001). NLR was associated with poor clinical outcome only in the COVID-19 group. ROC curve analysis was performed; the area under curve of poor outcomes for COVID-19 was 0.68, compared with 0.57 and 0.58 for Influenza and RSV respectively. In the COVID-19 group, multivariate logistic regression identified a high NLR (defined as a value above 6.82) to be a prognostic factor for poor clinical outcome, after adjusting for age, sex and Charlson comorbidity score (odds ratio of 2.9, P < 0.001). NLR at admission is lower and has more prognostic value in COVID-19 patients, when compared to Influenza and RSV.

The population of adults with Alzheimer's disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinicallyhomogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, "Anosognosia dementia" and "Insightful dementia", differentiate between severe participants based on clinical characteristics and biomarkers. The "Uncompensated mild cognitive impairment (MCI)" subtype, demonstrates clinical, demographic and imaging differences from the "Affective MCI" subtype. Differences were also observed between the "Worried Well" and "Healthy" clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous subgroups can potentially form the basis for enhancement of brain medicine research. Alzheimer's disease (AD) is a degenerative brain disease and the most common cause of dementia 1 according to the 2018 Alzheimer's association report 2 an estimated 5.7 million Americans of all ages are living with AD in 2018. The percentage of people with AD increases with age: 3% of people age 65-74, 17% of people age 75-84, and 32% of people age 85 and older have AD 3. Symptoms vary among people with AD, and the differences between typical age-related cognitive changes and early signs of AD can be subtle. The definite diagnosis of AD, requiring histopathological examination, is characterized by the accumulation of β-amyloid (Aβ) plaques and neurofibrillary tangles composed of tau amyloid fibrils associated with brain cell damage and neurodegeneration 4. In clinical practice, the diagnosis of AD is based on clinical criteria, while laboratory and imaging examinations are used to exclude other diagnoses. Sub classification of AD has been previously attempted, mostly based on a small set of parameters or on a single modality 5,6 , and in some studies has relied only on previous knowledge. Current diagnostic subgroupings are informative, however, they are quite crude as they are based on rough criteria 7,8. This may lead astray supervised data mining tools that rely solely on these definitions while trying to predict or associate disease manifestation with clinical and biological markers. Thus, for the search of new insights, it is essential to use unsupervised processes, which do not rely on the current diagnostic subgroupings, Nevertheless, despite numerous attempts to use unsupervised processes as progn...

Purpose: To characterize by evidence grades and examine variation in type of physical therapy intervention delivered in routine clinical care in individuals with cerebral palsy (CP). Methods: Retrospective data collection from the electronic record over 1 year at a tertiary care pediatric outpatient therapy division. Results: Four hundred sixty-five individuals with CP received 28 344 interventions during 4335 treatment visits. Sixty-six percent of interventions were evidence-based interventions (EBIs). Significant variation was demonstrated across Gross Motor Function Classification System levels, with children classified as level V receiving the least and level III the most. The most frequent EBIs delivered were caregiver education, motor control, functional strengthening, ankle-foot orthoses, treadmill training, and fit of adaptive equipment. Conclusions: Further work is needed to determine whether amount of EBI is related to better outcomes. Combining this information with other aspects of dose (intensity, time, and frequency) may elucidate the contribution of each with outcomes.

The purpose of reporting this series of patients is to illustrate the role of ascorbic acid in the treatment of severe acquired methemoglobinemia (metHb), especially when methylene blue is not available. Medical records of affected patients were reviewed to collect history of exposures, food ingestion, physical examination, pulse oximetry, blood gas, and co-oximetry results, and outcomes. Five cases of acquired metHb are presented here, all of whom received treatment with ascorbic acid and fully recovered after 24 hours of treatment. Our series emphasizes that ascorbic acid is an effective alternative in the management of acquired metHb if methylene blue is unavailable and suggests that ascorbic acid infusion may be indicated in patients with glucose-6-phosphatase dehydrogenase deficiency.

No abstract

This paper presents homogeneous clusters of patients, identified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer’s disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.

BackgroundIdentification of biomarkers for the Alzheimer’s disease (AD) is a challenge and a very difficult task both for medical research and data analysis.MethodsWe applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors.ResultsThe main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes.ConclusionsThe results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.