Debnath, N. | Search bioRxiv

bioRxiv2024

Interplay of Light, Melatonin, and Circadian Genes in Skin Pigmentation Regulation

HighlightsO_LICircadian pigmentation of tadpoles in vivo is mainly driven by melatonin C_LIO_LILight and melatonin differentially regulate proliferation C_LIO_LIMelatonin mimics the expression of circadian core genes in the dark phase C_LIO_LIDeregulation of the circadian rhythm inhibits melanin synthesis C_LI Circadian regulation of skin pigmentation is essential for thermoregulation, UV protection, and synchronization of skin cell renewal. This regulation involves both cell-autonomous photic responses and non-cell-autonomous hormonal control, particularly through melatonin produced in a light-sensitive manner. Photosensitive opsins, cryptochromes, and melatonin regulate circadian rhythms in skin pigment cells. We studied light/dark cycles and melatonin coordination in melanin synthesis and cell proliferation of Xenopus laevis melanophores. In vivo, tadpole pigmentation shows robust circadian regulation mainly hormone-driven, in that isolated melanophores respond strongly to melatonin but only slightly to light. Melanophore proliferation is faster in the dark and slower with melatonin compared to a 12/12 light/dark cycle. Expression of circadian core genes (clock, bmal1, per1, per2, per3, cry1, cry2, and cry4) in melatonin-treated cells during the light phase mimics dark phase expression. Individual Cry overexpression did not affect melanisation or cell proliferation, likely due to functional redundancy. Melanin synthesis was inhibited by circadian cycle deregulation through: a) pharmacological inhibition of Cry1 and Cry2 degradation with KL001, b) continuous light or dark conditions, and c) melatonin treatment. Our findings suggest that circadian cycle regulation, rather than proliferative capacity, alters melanisation of melanophores. SignificanceCircadian rhythms are a highly conserved phenomenon in nature. In vertebrates, the modification of skin pigmentation and epidermal cell renewal in response to the environmental light-dark cycle are crucial physiological adaptations that serve various purposes, including thermoregulation, reducing ultraviolet damage, and regulating skin stem cell proliferation. Our observations indicate that, in vivo, the circadian regulation of skin pigmentation is more influenced by cycling-melatonin levels than light/dark. The deregulation of the circadian cell cycle through various mechanisms all inhibited melanisation while cell proliferation was increased or reduced, suggesting that proliferation and melanisation are mechanistically dissociated responses. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/604624v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@9787deorg.highwire.dtl.DTLVardef@59022corg.highwire.dtl.DTLVardef@19dd46eorg.highwire.dtl.DTLVardef@e58fec_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

physiology↗

bioRxiv2018

Comparison of Approaches to the identification of Symptom Burden in Hemodialysis Patients Utilizing Electronic Health Records

BackgroundIdentification of symptoms is challenging with surveys, which are time-intensive and low-throughput. Natural language processing (NLP) could be utilized to identify symptoms from narrative documentation in the electronic health record (EHR).\n\nMethodsWe utilized NLP to parse notes for maintenance hemodialysis (HD) patients from two EHR databases (BioMe and MIMIC-III) to identify fatigue, nausea/vomiting, anxiety, depression, cramping, itching, and pain. We compared NLP performance with International Classification of Diseases (ICD) codes and validated the performance of both NLP and codes against manual chart review in a representative subset.\n\nResultsWe identified 1034 and 929 HD patients from BioMe and MIMIC-III respectively. The most frequently identified symptoms by NLP from both cohorts were fatigue, pain, and nausea and/or vomiting. NLP was significantly more sensitive than ICD codes for nearly all symptoms. In the BioMe dataset, sensitivity for NLP ranged from 0.85-0.99 vs. 0.09-0.59 for ICD codes. In the MIMIC-III dataset, NLP sensitivity was 0.8-0.98 vs. 0.02-0.53 for ICD. ICD codes were significantly more specific for nausea and/or vomiting (NLP 0.57 vs. ICD 0.97, P=0.03) in BioMe and for depression (NLP 0.67 vs. ICD 0.99, P=0.002) in MIMIC-III. A majority of patients in both cohorts had ?4 symptoms. The more encounters available for a patient the more likely NLP was to identify a symptom.\n\nConclusionsNLP out performed ICD codes for identification of symptoms on several tests parameters including sensitivity for a majority of symptoms. NLP may be useful for the high-throughput identification of patient centered outcomes from EHR.\n\nSignificance StatementPatients on maintenance hemodialysis experience a high frequency of symptoms. However, symptoms have been measured utilizing time-intensive surveys. This paper compares natural language processing (NLP) to administrative codes for the identification of seven key symptoms from two cohorts with electronic health records and validation through manual chart review. NLP identified high rates of symptoms; the most common were fatigue, pain, and nausea and/or vomiting. A majority of patients had [≥]4 symptoms. NLP was significantly more sensitive at identifying symptoms compared to administrative codes for nearly all symptoms but specificity was not significantly different compared to codes. This paper demonstrates utility of a high throughput method of identifying symptoms from EHR which may advance the field of patient centered research in nephrology.

epidemiology↗