Current approaches to the prevention, detection, treatment, and monitoring of postmenopausal osteoporosis are discussed. In the United States, 44 million men and women ages 50 years or older have low bone mass or osteoporosis. The most devastating consequence of this disease is fractures. The assessment of osteoporosis risk includes determining risk factors, conducting laboratory and physical examinations, and measuring bone density and bone-turnover markers. Once risk has been established, nonpharmacologic strategies, such as exercise, appropriate dietary habits, and discontinuing tobacco and alcohol use, are helpful. Fall prevention and adequate intake of calcium and vitamin D are critical. When pharmacologic therapy is warranted, bisphosphonates have shown the greatest benefit in preventing bone loss and lowering fracture rates. Selective estrogen-receptor modulators and calcitonin are also options for prevention or treatment of osteoporosis. Estrogen should not be used for the sole purpose of osteoporosis prevention; however, short-term use is acceptable for women with vasomotor symptoms or in whom the benefits outweigh the risks. Parathyroid hormone may offer another treatment alternative. A variety of pharmacologic options are available for patients with osteoporosis in whom lifestyle modifications have proven insufficient. Bisphosphonates are the mainstay of drug therapy.
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Eur Arch Otorhinolaryngol
January 2025
ENT institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 FenYang Road, Shanghai, 200031, China.
Background: Vocal fold leukoplakia (VFL), a precancerous lesion of the larynx, is characterized by white plaques on the vocal fold mucous membrane. Currently, there are no reliable biomarkers to predict the recurrence and malignant transformation of VFL. Considering chondroitin sulfate proteoglycan 4 (CSPG4) as a biomarker for malignant tumors such as laryngeal squamous cell carcinoma (LSCC), we conducted this cohort study to evaluate the prognostic influence of CSPG4 expression on VFL patients.
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January 2025
Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, KSA, Saudi Arabia.
In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators.
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January 2025
Department of Endocrinology and Metabolism, Faculty of Medicine, Yeditepe University, Istanbul, Türkiye.
Sheehan syndrome (SS) is postpartum pituitary necrosis leading to severe hypopituitarism. Severe bleeding during delivery and postpartum period results in ischemic necrosis of the enlarged pituitary gland during pregnancy. The improved obstetrical care decreased the incidence of SS significantly, however SS should always be kept in mind in the etiologies of hypopitutarism in women which can be easily recognized by medical history of the patient.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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