An automated controlled-release device for intravaginal hormone delivery

Toward an optimal contraception dosing strategy

Anovulation refers to a menstrual cycle characterized by the absence of ovulation. Exogenous hormones such as synthetic progesterone and estrogen have been used to attain this state to achieve contraception. However, large doses are associated with adverse effects such as increased risk for thrombosis and myocardial infarction. This study utilizes optimal control theory on a modified menstrual cycle model to determine the minimum total exogenous estrogen/progesterone dose, and timing of administration to induce anovulation. The mathematical model correctly predicts the mean daily levels of pituitary hormones LH and FSH, and ovarian hormones E2, P4, and Inh throughout a normal menstrual cycle and reflects the reduction in these hormone levels caused by exogenous estrogen and/or progesterone. Results show that it is possible to reduce the total dose by 92% in estrogen monotherapy, 43% in progesterone monotherapy, and that it is most effective to deliver the estrogen contraceptive in the mid follicular phase. Finally, we show that by combining estrogen and progesterone the dose can be lowered even more. These results may give clinicians insights into optimal formulations and schedule of therapy that can suppress ovulation.

An automated system for cattle reproductive management under the IoT framework. Part II: Induction of luteinizing hormone release after gonadotropin releasing hormone analogue delivery with e-Synch

Technologies for automating animal management and monitoring tasks can improve efficiency and productivity of livestock production. We developed the e-Synch system for automated control and monitoring the estrous cycle of cattle through intravaginal hormone delivery and sensing. Thus, our objective was to evaluate luteinizing hormone (LH) concentrations after intravaginal instillation of the Gonadotropin-releasing hormone (GnRH) analogue Gonadorelin with the e-Synch system. This system consists of an intravaginal electronically controlled automated hormone delivery and sensing device integrated with an IoT platform. Lactating Holstein cows with their estrous cycle synchronized were used in two experiments (Exp). In Exp 1, at 48 h after induction of luteolysis, cows (n=5-6 per group) were randomized to receive 100 µg of Gonadorelin through intramuscular (i.m.) injection, 100 µg of Gonadorelin in a 2 mL solution delivered with e-Synch, and an empty e-Synch device. In Exp 2, at 48 h after induction of luteolysis cows (n=6-7 per group) were randomized to receive 100 µg of Gonadorelin i.m., or an intravaginal treatment with e-Synch consisting of 100 or 1,000 µg of Gonadorelin in 2 or 10 mL of solution containing 10% citric acid as absorption enhancer. Circulating concentrations of LH were analyzed with linear mixed models with or without repeated measurements. In Exp 1, cows in the i.m. Gonadorelin treatment had a surge of LH whereas cows in the other two treatments did not have a surge of LH for up to 8 h after treatment. In Exp 2, the 1,000 µg dose of Gonadorelin elicited more LH release than the 100 µg dose, regardless of solution quantity. The overall LH response as determined by area under the curve, mean, and maximum LH concentrations was similar between cows receiving 1,000 µg of Gonadorelin delivered with e-Synch and 100 μg of Gonadorelin i.m. Increasing volume of solution for delivering the same dose of Gonadorelin partially increased LH release only for the 100 µg dose. We conclude that the e-Synch system could be used to automatically release Gonadorelin in a dose and volume that induces a surge of LH of similar magnitude than after i.m. injection of 100 μg of Gonadorelin. Also, the dose of Gonadorelin delivered by e-Synch is more critical than the volume of solution used.

An automated system for cattle reproductive management under the IoT framework. Part I: the e-Synch system and cow responses

The objective of this manuscript was to present the e-Synch system, integrating an intravaginal electronically controlled hormone delivery and sensing device with an IoT platform for remote programming and monitoring. Secondary objectives were to demonstrate system functionality and cow responses to e-Synch. External components of e-Synch include a 3D-printed case with retention wings, a flexible wideband antenna, and silicone membrane for pressure balancing. Internal components include a central control board, battery, wireless charging coil, and two silicone hormone reservoirs connected to individual peristaltic pumps. An accelerometer and a high-accuracy temperature sensor are integrated in the custom printed circuit board (PCB). The IoT platform includes a gateway consisting of Raspberry PI 3 and a CC1352 radiofrequency module that collects sensor data at 915 mHz. Data is transferred to the Google Cloud utilizing the IoT Core service through TCP/IP, and then is pulled by the Pub/Sub service. After routing to a BigQuery table by the Dataflow service, data visualization is provided by Data Studio. Drug delivery protocols are selected using an IOS device app that connects to e-Synch through Bluetooth. Experiments with lactating Holsteins cows were conducted to demonstrate proof-of-concept system functionality and evaluate cow responses. Despite unstable communication and signal discontinuity because of signal strength attenuation by body tissue, devices (n=6) communicated with the IoT platform in 89% (24/27) of use instances. Temperature and accelerometer data were received for at least one 15 min period during an 8 h insertion period from all devices that communicated with the IoT platform. Variation in accelerometer data (± 8.565 m/s2) was consistent with cow activity during experimentation and mean vaginal temperature of 39.1 °C (range 38.6 to 39.5 °C) demonstrated sensor functionality. Hormone release was confirmed in all instances of device use except for one. Cow behavior evaluated through signs of discomfort and pain, and tail raising scores was mostly unaltered by e-Synch. Vaginal integrity and mucus scores also remained unaltered during and after device insertion. In conclusion, the e-Synch device integrated with a controlling app and IoT platform might be used to automate intravaginal hormone delivery and sensing for controlling the estrous cycle of cattle.

Erratum to "An automated controlled-release device for intravaginal hormone delivery" (JDS Commun. 1:15-20)

[This corrects the article DOI: 10.3168/jdsc.2020-18816.].

Comparison of 5 Versus 7-Day Ovsynch + Progesterone Releasing Intravaginal Device Protocols (PRID) and a Modified G7G with an Option of Heat Detection Protocol for 1st Service in Lactating Dairy Cows

The aim of this study was to evaluate the efficacy of two timed-AI (TAI) protocols (Group G5D, GnRH and PRID -5d- PGF2a -1d- PGF2a -1d- GnRH, n = 105 and Group G7D, GnRH and PRID-7d- PGF2a -1d- PGF2a -1d- GnRH, n = 98) and a modified G7G protocol combining heat detection (HD) and AI or TAI if HD failed (Group HD, GnRH and PRID -7d- PGF2a -1d- PGF2a -5d- HD or 5d TAI if no HD, n = 92). Pregnancy per AI (P/AI) did not differ between G5D and G7D protocol (G5D: 33.8% vs. G7D: 35.2%, P = 0.85). Cows assigned to G5D and G7D group were pooled as TAI group (GTAI) and further compared to GHD. Within the GHD, more primiparous cows exhibited estrous signs compared to multiparous cows (70.4% vs. 46.2%, P = 0.03). Furthermore, 49 cows (53.3%) were served after HD, whereas 43 cows (46.7%) were served after TAI. There was no difference in P/AI between cows served after HD (51.6%) or after TAI (43.0%, P = 0.49). GHD showed higher P/AI at 1st service compared to GTAI (49.1% vs. 36.4%, P = 0.04), whilst, median days to pregnancy did not differ between the two groups. Overall, P/AI of primiparous cows tended to be better in comparison with multiparous cows (48.3% vs. 37.2%, P = 0.06). In conclusion, there was no significant difference regarding the efficacy of 5- and 7-day Ovsynch + PRID protocols. Moreover, a modified G7G protocol, with intermediate heat detection, resulted in overall better P/AI compared to TAI protocols and appears as a promising strategy to optimize estrus detection for 1st AI.