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Spectrum of Dermatoses Affecting the Lower Leg and Foot in an Outpatient Clinic at a Tertiary Care Hospital.

Adv Skin Wound Care

January 2025

At ESIC Medical College & Hospital, Faridabad, Haryana, India, Shanta Passi, MD, is Associate Professor, Department of Dermatology, Venereology, & Leprology; Deepika Uikey, MD, is Assistant Professor, Department of Dermatology, Venereology, & Leprology; and Manoj Kumar, MD, is Assistant Professor, Department of Psychiatry.

Background: Infective and noninfective dermatoses of the lower leg and foot can be attributed to factors such as infections, blood stagnation, chemical contact, and abnormal mechanics. These factors make the lower leg and foot more susceptible to microbial infections, contact dermatitis, stasis eczema, ulcers, corns, and calluses.

Objective: To identify the patterns of infective and noninfective dermatoses on the lower leg and foot.

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Objectives: To update the 2017 European Alliance of Associations for Rheumatology (EULAR) recommendations for treatment of systemic sclerosis (SSc), incorporating new evidence and therapies.

Methods: An international task force was convened in line with EULAR standard operating procedures. A nominal group technique exercise was performed in two rounds to define questions underpinning a subsequent systematic literature review.

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Background: Inflammatory bowel disease (IBD) is a chronic disease that includes Crohn's disease and ulcerative colitis. Studies found that 40-60% of women diagnosed with IBD have sexual dysfunction (SD).

Aims: To determine SD and associated factors in women with IBD.

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Aims: To describe the nutritional status of people with diabetes-related foot complications and explore the association between nutrition and ulceration healing.

Methods: This retrospective cohort study included attendees of a diabetes foot service who completed a dietary questionnaire. Diet was compared to guideline recommendations and biochemical measures were recorded.

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Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.

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