JAK3 as an emerging target for topical treatment of inflammatory skin diseases

The recent interest and elucidation of the JAK/STAT signaling pathway created new targets for the treatment of inflammatory skin diseases (ISDs). JAK inhibitors in oral and topical formulations have shown beneficial results in psoriasis and alopecia areata. Patients suffering from other ISDs might also benefit from JAK inhibition. Given the development of specific JAK inhibitors, the expression patterns of JAKs in different ISDs needs to be clarified. We aimed to analyze the expression of JAK/STAT family members in a set of prevalent ISDs: psoriasis, lichen planus (LP), cutaneous lupus erythematosus (CLE), atopic dermatitis (AD), pyoderma gangrenosum (PG) and alopecia areata (AA) versus healthy controls for (p) JAK1, (p) JAK2, (p) JAK3, (p) TYK2, pSTAT1, pSTAT2 and pSTAT3. The epidermis carried in all ISDs, except for CLE, a strong JAK3 signature. The dermal infiltrate showed a more diverse expression pattern. JAK1, JAK2 and JAK3 were significantly overexpressed in PG and AD suggesting the need for pan-JAK inhibitors. In contrast, psoriasis and LP showed only JAK1 and JAK3 upregulation, while AA and CLE were characterized by a single dermal JAK signal (pJAK3 and pJAK1, respectively). This indicates that the latter diseases may benefit from more targeted JAK inhibitors. Our in vitro keratinocyte psoriasis model displayed reversal of the psoriatic JAK profile following tofacitinib treatment. This direct interaction with keratinocytes may decrease the need for deep skin penetration of topical JAK inhibitors in order to exert its effects on dermal immune cells. In conclusion, these results point to the important contribution of the JAK/STAT pathway in several ISDs. Considering the epidermal JAK3 expression levels, great interest should go to the investigation of topical JAK3 inhibitors as therapeutic option of ISDs

Novel genetic variants associated with lumbar disc degeneration in northern Europeans: A meta-analysis of 4600 subjects

Objective: Lumbar disc degeneration (LDD) is an important cause of low back pain, which is a common and costly problem. LDD is characterised by disc space narrowing and osteophyte growth at the circumference of the disc. To date, the agnostic search of the genome by genome-wide association (GWA) to identify common variants associated with LDD has not been fruitful. This study is the first GWA meta-analysis of LDD. Methods: We have developed a continuous trait based on disc space narrowing and osteophytes growth which is measurable on all forms of imaging (plain radiograph, CT scan and MRI) and performed a meta-analysis of five cohorts of Northern European extraction each having GWA data imputed to HapMap V.2. Results: This study of 4600 individuals identified four single nucleotide polymorphisms with p<5×10-8, the threshold set for genome-wide significance. We identified a variant in the PARK2 gene (p=2.8×10-8) associated with LDD. Differential methylation at one CpG island of the PARK2 promoter was observed in a small subset of subjects (β=8.74×10-4, p=0.006). Conclusions: LDD accounts for a considerable proportion of low back pain and the pathogenesis of LDD is poorly understood. This work provides evidence of association of the PARK2 gene and suggests that methylation of the PARK2 promoter may influence degeneration of the intervertebral disc. This gene has not previously been considered a candidate in LDD and further functional work is needed on this hitherto unsuspected pathway. Copyright Article author (or their employer) 2012

Immunohistochemical staining of normal keratinocytes (KCs) and psoriasis induced keratinocytes (Pso KCs) with or without tofacitinib treatment.

<p>The pJAKs expression in Pso KCs was similar and summarizes the one observed in the epidermis of the psoriasis skin biopsies. Strong pJAK3 expression and weak positive pJAK1 expression was induced by psoriasis stimulation and inhibited after treatment with tofacitinib. Phospho-JAK2 and pTYK2 expression did not change neither with psoriasis stimulation nor with the treatment. Note the cytoplasm localization of pJAK1 and pJAK3 and the nuclear localization of pTYK2 in the keratinocytes. As pJAK2 was negative in all conditions, the localization of the staining could not be analysed. Original magnification x200.</p

Percentage of JAK/STAT family positivity (in grey)/negativity in dermal inflammatory cells of ISDs comparing to controls.

<p>Percentage of JAK/STAT family positivity (in grey)/negativity in dermal inflammatory cells of ISDs comparing to controls.</p

PhosphoJAK1, pJAK2, pJAK3 and pTYK2 immunohistochemical localization in the epidermis.

<p>Cytoplasmic expression of pJAK1, pJAK3 and pTYK2. Intranuclear expression of pJAK2 and pTYK2 (arrows). Similar expression was seen in all studied diseases. Original x 200. Pso = psoriasis, LP = lichen planus, CLE = cutaneous lupus erythemathosus, AD = atopic dermatitis, AA = alopecia areata, PG = pyoderma gangrenosum.</p

Overview of JAK/STAT protein (by immunohistochemistry) expression in the studied inflammatory skin diseases as compared to healthy skin.

<p>Pso = psoriasis, LP = lichen planus, CLE = cutaneous lupus erythemathosus, AD = atopic dermatitis, AA = alopecia areata, PG = pyoderma gangrenosum, epid ext = epidermal extent. NS = not statistically significant.</p

JAK/STAT mRNA levels in human skin biopsies from psoriasis compared to healthy controls illustrates <i>JAK3</i>, <i>STAT1</i> and <i>STAT3</i> overexpression.

<p>* p≤0.05.</p

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers:Development and Validation Study

BACKGROUND: Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general practitioners (GPs) serve as the first point of contact for health care, EHRs generated by these primary care providers contain a wealth of potentially valuable information. Nonetheless, the unstructured nature of free-text entries in EHRs poses challenges in identifying trends, detecting disease outbreaks, or accurately pinpointing COVID-19 cases. OBJECTIVE: This study aims to develop and validate a BERT model for detecting COVID-19 consultations in general practice EHRs in the Netherlands. METHODS: The BERT model was initially pretrained on Dutch language data and fine-tuned using a comprehensive EHR data set comprising confirmed COVID-19 GP consultations and non-COVID-19-related consultations. The data set was partitioned into a training and development set, and the model's performance was evaluated on an independent test set that served as the primary measure of its effectiveness in COVID-19 detection. To validate the final model, its performance was assessed through 3 approaches. First, external validation was applied on an EHR data set from a different geographic region in the Netherlands. Second, validation was conducted using results of polymerase chain reaction (PCR) test data obtained from municipal health services. Lastly, correlation between predicted outcomes and COVID-19-related hospitalizations in the Netherlands was assessed, encompassing the period around the outbreak of the pandemic in the Netherlands, that is, the period before widespread testing. RESULTS: The model development used 300,359 GP consultations. We developed a highly accurate model for COVID-19 consultations (accuracy 0.97, F -score 0.90, precision 0.85, recall 0.85, specificity 0.99). External validations showed comparable high performance. Validation on PCR test data showed high recall but low precision and specificity. Validation using hospital data showed significant correlation between COVID-19 predictions of the model and COVID-19-related hospitalizations (F -score 96.8; P&lt;.001; R =0.69). Most importantly, the model was able to predict COVID-19 cases weeks before the first confirmed case in the Netherlands. CONCLUSIONS: The developed BERT model was able to accurately identify COVID-19 cases among GP consultations even preceding confirmed cases. The validated efficacy of our BERT model highlights the potential of NLP models to identify disease outbreaks early, exemplifying the power of multidisciplinary efforts in harnessing technology for disease identification. Moreover, the implications of this study extend beyond COVID-19 and offer a blueprint for the early recognition of various illnesses, revealing that such models could revolutionize disease surveillance