Tuberculosis verrucosa cutis in a patient with pulmonary tuberculosis: A case report

Cutaneous tuberculosis is a skin disorder caused by Mycobacterium tuberculosis, commonly classified according to its spreading route into endogenous, exogenous, and hematogenous, with distinct skin morphology and histopathological findings. Tuberculosis verrucosa cutis is a classification form of cutaneous tuberculosis, a paucibacillary disease caused by exogenous reinfection (inoculation) after sensitization on an immunocompromised individual. Management of cutaneous TB is generally similar to the management of pulmonary TB. In this paper, we presented a case of 62 years old male with a purplish rough patch on the right dorsum of the foot that began 1,5 years. There is a history of trauma and pulmonary TB. On the right dorsum of the foot, we identified a verrucous lesion with a livid base, irregular with the size of 6x3 cm. The diagnosis is established based on clinical features and histopathology evaluation. The patient received treatment with antituberculosis medication, topical mupirocin ointment 2%, and vaseline petroleum jelly. Improvement of the lesions was observed after the patient completed the intensive phase in two months of treatment with antituberculosis drugs

Quercetin: the bioactive compound from Allium cepa L. as anti-inflammation based on in silico screening

Inflammation is a tissue injury that occurs due to physical trauma or microbiological substances that involve the activities of many cell types. Inflammation can be prevented using the natural medicines from Allium cepa L. Quercetin is one of the bioactive compounds found in Allium cepa L and has been reported to have anti-inflammatory activity. The natural medicines have been used to minimize non-steroidal anti-inflammatory drugs. This study aims to investigated the modeling structures and the protein receptor from quecertin in inflammation mechanism and their optimization of the effectiveness in the human body. The bioinformatics tools used in this study are the database of quercetin compounds, Pubchem and Swis Target Prediction protein prediction databases, PyRx 0.8 molecular docking software, ligand docking, and binding site analysis with PyMOL and LigPlus software. The results from in silico show that quercetin compounds can interact with Muscleblind-like protein 1 target protein with a Binding Affinity minus value which is not much different from the dexamethasone compound. Dexamethason is a standart because it is a corticosteroid drug that can be used as an anti-inflammatory to reduce inflammation, allergic reactions, arthritis and other inflammatory diseases

The potential effect of fatty acids from Pliek U on epidermal fatty acid binding protein: chromatography and bioinformatic studies

The fatty acid analysis in Pliek U and its bioinformatic studies had been carried out and reported. Herein, fatty acids were analyzed by gas chromatography-mass spectrometry (GC-MS), meanwhile their potential effect, based on its interaction on epidermal fatty acid binding protein (E-FABP), was studied by bioinformatics approach with reverse docking technique using palmitic acid as a control compound. For the stated purpose, two Pliek U extracts were prepared, namely ethanolic Pliek U extract (EPUE), and ethanolic of residue hexane of Pliek U extract (ERHPUE). The GC-MS results showed that lauric, myristic, palmitic, and oleic acids were predominant, followed by stearic, capric, linoleic, and caprylic acids. Reverse docking results showed that linoleic acid had the lowest binding affinity (-5.9 kcal/mol) and was strongly bound to E-FABP on the same side of amino acid GLN A98, ARG A81, TYR A22, and LYS A61. These findings indicated that linoleic acid has a potential utility as a drug candidate for atopic dermatitis treatment

The Profile of Leprosy Patients in Aceh: Retrospective Study

Backgrounds: Morbus Hansen or leprosy is a chronic granulomatous infectious disease caused by the obligate intracellular Mycobacterium leprae bacillus. Leprosy can cause loss of sensation in the skin with or without lesions and body dysfunction during the course of the disease. In Aceh, there were 337 new cases of leprosy in 2019 and this was the highest case in Sumatra. Purpose: To analyze the profile of leprosy patients in the Department of Dermatology and Venereology of RSUDZA Banda Aceh. Methods: Observational descriptive study with retrospective design, using medical record of leprosy patients who visited Dermatology and Venereology Outpatient Clinic RSUDZA Banda Aceh for the period of January 2017 – December 2021. Result: A total of 183 leprosy patients were obtained from the study. Most cases occured in male patients (68.3%), the majority was in the 18-40 year age group (60.1%), most patients live outside of Banda Aceh (71%). The most frequently reported type of leprosy was the multibacillary type (71.6%). Leprosy patients without disability were 35.5%, the majority did not experience a leprosy reaction (71%), the most common morphology of the lesion was erythematous (29.5%), and the highest proportion of the bacteriological and morphological index was negative (46.4% and 50.8% respectively). Conclusion: The diagnosis of leprosy was based on clinical, bacteriological, and histopathological features. Early diagnosis, patient access to treatment, early initiation of treatment and adherence to therapy would reduce the disease transmission thus would decrease the prevalence of leprosy in Aceh

Combination of PCA with LDA and SVM classifiers: A model for determining the geographical origin of coconut in the coastal plantation, Aceh Province, Indonesia

Since Indonesia is the world's largest producer of coconuts, it is necessary to classify them according to their chemical profiles. The examination of coconut endosperm from 13 districts in Aceh's coastal plantations was done using the NIRS. Chemometric analysis of the NIRS spectra revealed that smoothing, the 1st derivative, and SNV preprocessing of the PCA produced improved visualization outcomes. The discrimination study revealed that the PCA-LDA and PCA-SVM produced accurate results that were satisfactory, with 100% accuracy for each. The effectiveness of combinations of PCA-LDA and PCA-SVM was examined in this article as being helpful for enhancing classification accuracy

Synthesis of Chitosan-Silver Nanoparticle Composite Spheres and Their Antimicrobial Activities

Synthesis of silver nanoparticles–chitosan composite particles sphere (AgNPs-chi-spheres) has been completed and its characterization was fulfilled by UV–vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and zetasizer nano. UV–vis spectroscopy characterization showed that AgNPs-chi-spheres gave optimum absorption at a wavelength of 410 nm. The XRD spectra showed that the structure of AgNPs-chi-spheres were crystalline and spherical. Characterization by SEM showed that AgNPs-chi-spheres, with the addition of 20% of NaOH, resulted in the lowest average particle sizes of 46.91 nm. EDX analysis also showed that AgNPs-chi-spheres, with the addition of a 20% NaOH concentration, produced particles with regular spheres, a smooth and relatively nonporous structure. The analysis using zetasizer nano showed that the zeta potential value and the polydispersity index value of the AgNPs-chi-sphere tended to increase with an increased NaOH concentration. The results of the microbial activity screening showed that the AgNP-chi-Spheres with highest concentration of NaOH, produced the highest inhibition zone diameters against S. aureus, E. coli, and C. albicans, with inhibition zone diameters of 19.5, 18.56, and 12.25 nm, respectively

Evaluation of Gradient Boosted Classifier in Atopic Dermatitis Severity Score Classification

This study investigates the application of the Gradient Boosting machine learning technique to enhance the classification of Atopic Dermatitis (AD) skin disease images, reducing the potential for manual classification errors. AD, also known as eczema, is a common and chronic inflammatory skin condition characterized by pruritus (itching), erythema (redness), and often lichenification (thickening of the skin). AD affects individuals of all ages and significantly impacts their quality of life. Accurate and efficient diagnostic tools are crucial for the timely management of AD. To address this need, our research encompasses a multi-step approach involving data preprocessing, feature extraction using various color spaces and evaluating classification outcomes through Gradient Boosting. The results demonstrate an accuracy of 93.14%. This study contributes to the field of dermatology by providing a robust and reliable tool to support dermatologists in identifying AD skin disease, facilitating timely intervention and improved patient care

Evaluation of atopic dermatitis severity using artificial intelligence

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes

A fatal case of Harlequin ichthyosis: Experience from low-resource setting

Harlequin ichthyosis is a severe and fatal presentation of ichthyosis with an autosomal recessive inheritance. Infants with Harlequin ichthyosis have a high mortality rate, and a dismal prognosis; therefore the majority of neonates die shortly after birth from infection, heat loss, dehydration, electrolytic imbalances, or respiratory distress. The aim of this case report was to present a fatal case of Harlequin ichthyosis with no family history of any inherited skin disorder. A 3-day-old baby was presented to the emergency room with congenital abnormalities at birth, fissured hyperkeratotic skin, and thick yellow plates of scales. The parents had no history of consanguineous marriage, no relevant past medical history, and no family history of the same condition. The patient was unwell, pulse 162 times/minute, respiratory rate 48 times/minute, and axillary temperature 36.9oC. APGAR score was 8 in the 1st minute and 9 in the 5th minute. Based on the typical clinical appearance, the patient was diagnosed with Harlequin ichthyosis. Due to a lack of facility, a mutation analysis was not carried out. The patient was then transferred to the neonatal intensive care unit (NICU) and treated in a humidified incubator and medicated with intravenous antibiotics (ampicillin sulbactam 125 mg/12 hour and gentamicin 13 mg/24 hour), topically fusidic acid and mild emollients. A central venous catheter was used for intravenous access. The poor prognosis resulted in the patient dying at the age of 5-day-old. This case highlights that prenatal diagnosis is critical for early detection and disease prevention. Mutation screening for the ABCA12 gene is suggested for consanguinity marriages and with a history of ichthyosis