Skin and subcutaneous conditions affect an estimated 1.9 billion people at any given time and remain the fourth leading cause of non-fatal disease burden worldwide.Access to dermatology care is limited due to a shortage of dermatologists, causing long wait times and leading patients to seek dermatologic care from general practitioners.However, the diagnostic accuracy of general practitioners has been reported to be only 0. 24-0. 70 (compared to 0. 77-0. 96 for dermatologists), resulting in over-and under-referrals, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories). The DLS distinguishes between 26 of the most common skin conditions, representing roughly 80% of the volume of skin conditions seen in a primary care setting. The DLS was developed and validated using de-identified cases from a teledermatology practice serving 17 clinical sites via a temporal split: the first 14,021 cases for development and the last 3,756 cases for validation. On the validation set, where a panel of three board-certified dermatologists defined the reference standard for every case, the DLS achieved 0.71 and 0.93 top-1 and top-3 accuracies respectively, indicating the fraction of cases where the DLS's top diagnosis and top 3 diagnoses contains the correct diagnosis. For a stratified random subset of the validation set (n=963 cases), 18 clinicians (of three different training levels) reviewed the cases for comparison. On this subset, the DLS achieved a 0.67 top-1 accuracy, non-inferior to board-certified dermatologists (0.63, p<0.001), and higher than primary care physicians (PCPs, 0.45) and nurse practitioners (NPs, 0.41). The top-3 accuracy showed a similar trend: 0.90 DLS, 0.75 dermatologists, 0.60 PCPs, and 0.55 NPs . These results highlight the potential of the DLS to augment the ability of general practitioners who did not have additional specialty training to accurately diagnose skin conditions by suggesting differential diagnoses that may not have been considered. Future work will be needed to prospectively assess the clinical impact of using this tool in actual clinical workflows.

Our findings indicate that the amount of shrinkage is driven by variation in leaf area, leaf thickness, evergreenness, and woodiness and can be reversed by rehydration. The amount of shrinkage may also be a useful trait related to ecologically and physiological differences in drought tolerance and plant life history.

Access to transportation and lack of a regular primary care provider or a medical home are associated with late-stage of CxCa at diagnosis. Many medically underserved women continue to use the ER as their primary source of health care, and as a result their CxCa is diagnosed in advanced stages, with higher medical costs and lower chances of cure. The lack of Medicaid expansion in Texas may result in a worsening of this situation.

Background Sexual dysfunction is a common long-term side effect of treatments for gynecologic cancer. Studies of sexual problems in gynecologic cancer survivors overrepresent White non-Hispanic, highly educated, and married women. Less is known about the sexual health needs of women in medically underserved populations. We therefore conducted a study to characterize sexual activity and sexual function in this population. Methods We recruited patients attending two gynecologic oncology clinics in a large public healthcare system that primarily serves uninsured and low-income patients. Participants were invited to complete a one-time survey to assess sexual function, sexual communication, sexual distress, relationship adjustment, depression, anxiety, prior help-seeking and help-seeking preferences, and reasons for sexual inactivity. Data were analyzed using descriptive statistics and multivariate models to predict sexual activity status and sexual dysfunction. Results Among 243 participants, the majority (n=160, 65.8%) were not sexually active in the past 4 weeks, most often due to lack of a partner or lack of desire for sex. Just over one-fourth of sexually active participants were identified as likely cases of sexual dysfunction. Greater endorsement of depressive symptoms predicted both sexual inactivity and sexual dysfunction in multivariate analyses. Prior help-seeking for sexual problems was uncommon; however, a significant minority of participants expressed interest in receiving care for sexual problems. Conclusions Gynecologic cancer survivors in our medically underserved population have high rates of sexual inactivity and sexual dysfunction. Future research should identify feasible strategies to address barriers to sexual health care in low-resource settings.