Unmet need for family planning and barriers to contraceptive use among women living with HIV in Cross River State, Nigeria This project aims to determine the prevalence and knowledge of contraceptive methods among women (n=936) aged 18-49 years living with HIV in Cross River, Nigeria via tablet-based survey. The use of modern family planning (FP) methods (e.g., long-acting implant) to prevent unintended pregnancies in women living with HIV (WLH) is not only a highly cost-effective approach to preventing vertical transmission of HIV, but also has the potential to reduce maternal deaths, of which Nigeria has the highest number in the world. While Nigeria ranks among the lowest in contraceptive use globally, Cross River State, in South-South Nigeria, has the highest unmet need for family planning (35%) among women of reproductive age in the country. Also, Cross River State’s HIV prevalence of 1.8% ranks higher than the national average of 1.4%. Prevention of unintended pregnancies among women of reproductive age living with HIV is an important element of the global HIV prevention strategy. Although several studies have documented the unmet need for family planning among women of reproductive age in Nigeria, there is less research on unmet need for modern contraceptive methods among WLH and its implication for HIV prevention in Nigeria. It is against this background that this study sought to determine the prevalence of unmet need for FP and identify the barriers hindering effective uptake of modern contraceptive methods among WLH in Cross River State via survey among 936 WLH. This project offers interesting data of multiple factors shaping women’s health, maternal and child health, HIV and family planning.
Anterior Cruciate Ligament Re-Tear The anterior cruciate ligament (ACL) is a key intra-articular structure of the knee joint, oriented obliquely to connect the femur to the tibia. Its primary biomechanical function is to resist anterior translation of the tibia relative to the femur and to contribute significantly to rotational stability of the knee. ACL injuries are prevalent among athletes participating in cutting and pivoting sports such as basketball, football, soccer, and lacrosse. The incidence of ACL injuries among adolescent athletes is steadily increasing, approaching nearly 1 per 10,000 AEs for female athletes, who were almost 1.5 times as likely as male athletes to suffer an ACL injury across all adolescent sports. Clinical management of ACL rupture frequently involves surgical reconstruction, most commonly utilizing autografts such as bone–patellar tendon–bone, quadriceps tendon, or hamstring tendon. The procedure aims to restore knee stability and function, with graft selection influenced by patient-specific factors, activity level, and surgeon preference. Even though 83% of patients return to their pre-injury level of activity, re-tear rates have been documented to be as high as 30%. Contributing factors, such as graft type, surgical technique, time between surgery and release to RTS, and age have been gaining more attention in the orthopedic surgery literature. Functional testing, level of preinjury activity, quadriceps and hamstring strength, and psychological testing have been shown to aid in determination for RTS. Researchers at the University of Iowa Department of Rehabilitation Therapies and Sports Medicine are interested in the mechanism by which graft type, quad strength, rate of force development, time to recovery, and other risk factors.
Deep Learning Approach for Automated Sleep Apnea and Apnea Severity Detection Sleep apnea is a common sleep disorder characterized by breathing difficulties during sleep and presents a major public health concern due to its high prevalence and its health impact. It has been estimated that approximately 15% of men and 5% of women in the adult population, and 1%–5% of infants and children harbor the condition. Disruption of normal sleep architecture is associated with daytime sleepiness, fatigue, impaired attention, and serious health conditions such as irregular heart rate, ischemic heart disease, cardiovascular dysfunction, and stroke. The current clinical gold standard for the diagnosis of sleep apnea is overnight polysomnography (PSG), which records multiple physiological signals such as airflow, respiratory effort, oxygen saturation, electrocardiogram, and electroencephalogram—used as informative indicators. Based on PSG, physicians can manually identify the occurrence of apnea and evaluate its severity. The process, however, is expensive, time-consuming, and uncomfortable for patients, leaving many individuals undiagnosed and untreated. Accurate and affordable diagnostic approaches would be critical for improving sleep care and population health. Recently, machine learning techniques have gained increasing attention in automated apnea detection. Prior studies suggest that physiological patterns and apnea characteristics vary across different demographics and population groups. This project aims to explore deep learning approaches for automated sleep apnea detection using multi-channel PSG signals to analyze model performance and physiological variability across diverse population groups.