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Introduction: Lipodystrophy (LD) syndromes are rare heterogeneous disorders characterized by reduction or absence of subcutaneous fat, low or non-detectable leptin concentrations in blood and impaired hunger/satiety regulation. Metreleptin treatment reverses metabolic complications and improves eating behavior in LD. Because depression in anorexia nervosa (AN), which is also characterized by hypoleptinemia, improves substantially upon treatment with metreleptin, we hypothesized, that metreleptin substitution may be associated with an antidepressant effect in patients with LD, too. Methods: In this ancillary study ten adult patients with LD were treated with metreleptin. To assess depressive symptoms, the self-rating questionnaire Beck’s Depression Inventory (BDI) was filled in at pre-established time points prior (T1) and after initiation of metreleptin (T2: 1 week; T3: 4 weeks; T4: 12 weeks) dosing. The differences between time points were tested with nonparametric Friedman's analysis of variance. Sensitivity analyses were performed upon exclusion of the BDI items addressing appetite and weight changes. Results: According to their BDI scores, four patients had mild depression and two had moderate depression at baseline. Friedman’s test revealed significant differences in BDI scores between the four time points. Post-hoc analyses revealed that the difference between T1 and T3 was significant upon Bonferroni correction (p=0.034, effect size r=0.88). The sensitivity analyses upon exclusion of the appetite and weight change items again revealed a significant Friedman’s test and significant Bonferroni corrected differences in the revised BDI scores between T1 vs. T2 (p=0.002, r=0.99) and T1 vs. T3 (p=0.007, r=0.79). Discussion/Conclusion: Our study for the first time revealed suggestive evidence for an antidepressant effect of metreleptin in patients with LD. Metreleptin caused a rapid drop in depression scores within one week of treatment. A reduction of the depression score was also observed in two of the three LD patients whose BDI scores were in the normal range before start of the treatment. The reduction in total scores of BDI were still apparent after three months (T4) of dosing. This observation matches findings obtained in clinical case studies of AN patients, in whom depression scores also dropped during the first week of metreleptin treatment.

Vulvovaginitis is one of the most troublesome and common gynecological problems affecting a woman's health. Vulvovaginitis can be defined as inflammations that affect the vaginal walls causing local pH changes, pruritus and sometimes secretions. They are caused by bacteria (bacterial vaginosis), fungi (fungal vulvovaginitis), protozoa (trichomoniasis) and also by associated microorganisms called mixed vulvovaginitis.

Background For a decade, Latin American Telemedicine Infarct Network (LATIN) Telemedicine has transformed AMI management in Brazil, Colombia, Mexico, Chile, and Argentina. With a hub and spoke strategy, AMI coverage was expanded to 100 million population and 877,177 telemedicine encounters were performed. Cost savings from avoiding unnecessary transfer of patients was $291 million. We are now rapidly escalating on a path to making the telemedicine process “physician-free” by utilizing Artificial Intelligence (AI) protocols. Purpose To demonstrate that AI can replace a cardiologist for remote AMI telemedicine guidance. Methods The process of total AI guidance focused on both aspects of our telemedicine strategy – accurate AMI diagnosis and tele-guidance of the entire STEMI process. We developed our innovative approach by initially creating AI algorithms for computer-aided diagnosis. Next, we incorporated logistic variables (duration of chest pain, transfer times to LATIN hub, etc) to the algorithm for physician-free triage into thrombolysis, primary PCI and pharmaco-invasive management. The intent of creating AI algorithms was early STEMI detection and triage. After the patient was efficiently transferred to the hub, a final treatment decision was made by the hub cardiologists. Results Three crucial areas of telemedicine efficiency are being monitored – Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT). All are showing improvements. Detailed results will be available at the time of presentation. Conclusions We are encouraged with the possibility of making the entire telemedicine guidance of AMI management “physician-free”. Next-Gen improvements are being contemplated by including a Single Lead EKG for AMI detection that will impact symptom-to-balloon times. Funding Acknowledgement Type of funding source: None

Background With the introduction of electronic medical records and other digital platforms, the classification and coding of different medical entities have become a complex, cumbersome task that is prone to diagnostic inconsistencies and errors. By incorporating Artificial Intelligence (AI) to a massive database of EKG records, we have developed an innovative methodology to accurately discriminate an EKG as “normal” or “abnormal”. We firmly believe that this algorithm sets up medicine on a path of complete computer-aided EKG interpretation. Purpose To present a viable AI-guided filter that can accurately discriminate between normal and abnormal EKG within a cardiologist-annotated EKG database. Methods An observational, retrospective, case-control study. Samples: A total of 140,000 randomly sampled 12-lead ECG of 10-seconds length with a sampling frequency of 500 [Hz] from Brazil (BR) and Colombia (CO) (divided as 70,000 normal and 70,000 abnormal EKG records per country dataset) were derived from the private International Telemedical System (ITMS) database from September 2018 to July 2019. Only de-identified records were used, records with artifacts were excluded. Preprocessing: Only the first 2s of each short lead and 9s of the long lead were considered. This data includes mobile (MOB) and transtelephonic (TTP) EKGs (50/50 ratio). Limb leads I, II and III and precordial leads V1, V2, V3 and V5 were used. The mean was removed from each lead. Training Sets: Four models were trained as depicted in the figure below. Each training dataset has 25,000 Normal and 25,000 Abnormal records, where 10% of the total records were used as a validation set. The test sets included 10,000 normal, and 10,000 abnormal records each. Testing and Class Assigning: An inception convolutional neural network was implemented; Each model was tested with 5,000 normal and 5,000 abnormal records of the corresponding country and transmission type with which they were trained. “Normal” or “Abnormal” labels were assigned to each EKG record and were compared to the cardiologists' reports; performance indicators (accuracy, sensitivity, and specificity) were calculated for each model. Results An overall accuracy of 82.4%; sensitivity of 88.7%; and specificity of 76.2% was achieved amongst the 4 testing models (Separate results of each training set are shown below). Conclusion(s) AI enables the interpretation of digital EKG records to be exercised in an organized, accurate, and straightforward manner, taking into consideration the multiple potential entities that can be diagnosed through this historical triage tool. By quickly identifying the normal records, the cardiologist is able to invest efforts in treating patients in a timely manner. Funding Acknowledgement Type of funding source: None

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BD highly impacts patients, families, health services and society.• First presentation of the disorder occurs in the pediatric age range for more than half of the adult patients.

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