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J Diabetes Investig ; 2024 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-38725153


AIMS/INTRODUCTION: Deficiency of neurotropic factors is implicated in diabetic neuropathy (DN). Netrin-1 is a neurotropic factor, but its association with DN has not been explored. We have assessed the association between serum netrin-1 levels and early diabetic neuropathy assessed by quantifying corneal nerve fiber loss using corneal confocal microscopy. MATERIALS AND METHODS: A total of 72 participants with type 2 diabetes, without and with corneal nerve fiber loss (DN- n = 42, DN+ n = 30), and 45 healthy controls were studied. Serum netrin-1 levels were measured by enzyme-linked immunosorbent assay, and corneal nerve morphology was assessed using corneal confocal microscopy. RESULTS: Corneal nerve fiber density, branch density, fiber length and serum netrin-1 levels were significantly lower in the DN- and DN+ groups compared with controls (P < 0.001). Netrin-1 levels correlated with corneal nerve fiber length in the DN+ group (r = 0.51; P < 0.01). A receiver operating characteristic curve analysis showed that a netrin-1 cut-off value of 599.6 (pg/mL) had an area under the curve of 0.85, with a sensitivity of 76% and specificity of 74% (P < 0.001; 95% confidence interval 0.76-0.94) for differentiating patients with and without corneal nerve loss. CONCLUSIONS: Serum netrin-1 levels show a progressive decline with increasing severity of small nerve fiber damage in patients with diabetes. Netrin-1 could act as a biomarker for small nerve fiber damage in DN.

Cureus ; 15(8): e43602, 2023 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-37719526


Factitious disorders represent deliberately fabricated dissimulation of physical and psychological signs and symptoms seeking medical attention by the patient. Usually, they are ignorant of conventional treatment and consistently change their version of signs and symptoms. Due to various changes in the version, they do not respond to the treatment. They describe their signs and symptoms as dissimulated, imaginative, and exasperated, involving any part of the body. Gingivitis artefacta is an unusual and dramatic presentation with self-inflicted physical injury to the gingival tissues. We present an extremely rare case of frontal lobe glioma causing abnormal psychology of factitious disorder resulting in self-inflected injury to gingiva in an adult male. This case also highlights the management of the dental condition of multiple recessions with coronally advanced flaps with orthodontic buttons.

Bone Rep ; 17: 101642, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36504506


Aromatase (CYP19A1) is the only enzyme known to catalyse the conversion of androgen to estrogen. Aromatase deficiency occurs due to mutation in CYP19A1gene which has an autosomal recessive inheritance pattern. It leads to decrease in estrogen synthesis and delayed epiphyseal closure, eunuchoid habitus and osteopenia. We are presenting here, a 24 years old male, with history of progressive increase in height and knock knees. X-ray showed open wrist and knee epiphysis. The serum testosterone level was normal and serum estradiol level was undetectable. Semen analysis showed azoospermia. Clinical exome sequencing gave two novel mutations in CYP19A1. The first variant was a novel single nucleotide deletion of thiamine at 570th base of the cDNA (c.570delT) of CYP19A1 gene. The second variant detected was again a novel one in the same gene in Exon 5 corresponding 344th base of the cDNA (c344G>A) resulting in a missense mutation of 115th arginine to glutamine in the protein. Sanger sequencing showed that the later mutation was inherited from the father. The patient was started on oral estradiol valerate for epiphyseal closure to prevent further increase in height. Only 15 mutations have been reported in the aromatase gene in males till date, our report of these novel mutations will be an add-on to the literature.

PLoS One ; 17(3): e0264785, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35298502


The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

COVID-19/mortalidade , Hospitalização/estatística & dados numéricos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Teorema de Bayes , COVID-19/epidemiologia , COVID-19/etiologia , Criança , China/epidemiologia , Feminino , Humanos , Índia/epidemiologia , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Medição de Risco/métodos , Fatores de Risco , Adulto Jovem