The relationship of ovarian endometrioma and its size to the preoperative serum anti-Mullerian hormone level

Objectives: The aim of this study is to evaluate the impact of ovarian endometrioma according to its size on the serumanti-Mullerian hormone (AMH) levels compared to that of other benign ovarian cysts.Material and methods: The current study retrospectively evaluated preoperative serum AMH level and its association to presentingovarian cyst size which were measured in clinical setting. Women with surgically diagnosed endometrioma or other benignovarian cysts were included. All patients underwent transvaginal or transrectal ultrasonography to determine the size of theovarian cysts. Preoperative serum AMH level was checked and evaluated according to histologic type of the cyst, which wereendometrioma or other benign ovarian cysts, respectively. Both groups were classified into ≤ 4 cm, > 4 cm and ≤ 8 cm, > 8 cmand ≤ 12 cm, > 12 cm according to the diameter of cyst and analyzed the difference of mean AMH levels in both groups.Results: There was no significant difference in preoperative serum AMH level between the two groups (3.36 ± 2.3 versus3.76 ± 2.64, p = 0.331). The difference of preoperative AMH levels according to categorized cyst size also was not statisticallysignificant in both groups.Conclusions: Preoperative serum AMH levels were not statistically different between endometrioma and other benignovarian cyst groups and were not related to the size of endometrioma

Risk factors related to the recurrence of endometrioma in patients with long-term postoperative medical therapy

Objectives: The purpose of this study was to identify clinical risk factors for the recurrence of ovarian endometrioma after ovarian cystectomy in Korean women with long-term postoperative medical therapy.Material and Methods: A total of 134 patients who were surgically treated for endometriotic cysts at Pusan National University Hospital were included in this retrospective study. All patients received long-term postoperative medical treatment for at least 12 months after the first-line conservative surgery. Several epidemiologic variables were analyzed as possible risk factors for recurrence. Endometrioma recurrence was considered when a cystic mass was observed on transvaginal or transrectal sonography. Statistical analysis was performed using independent t-tests for parametric continuous variables.Results: The mean follow-up period for the 134 patients was 56.5 ± 14.3 months (range, 36–120 months) and the mean duration of the medical therapy was 17.9 ± 17.3 months (range, 12–120 months). The overall recurrence rate was 35/134 (26.12%). Our univariate analysis showed statistically significant differences between the recurrent and non-recurrent groups in terms of weight (P = 0.013), body mass index (P = 0.007), age at the time of surgery (P = 0.013), the diameter of the largest cyst (P = 0.001), the presence of dysmenorrhea (P < 0.0001), and postoperative pregnancy (P = 0.016). Multivariate analysis showed that body mass index (OR 1.153, 95% CI 1.003–1.326, P = 0.046), age at the time of surgery (OR 0.924, 95% CI 0.860–0.992, P = 0.029), and presence of dysmenorrhea (OR 12.226, 95% CI 3.543–42.188, P < 0.0001) were significantly correlated with the recurrence of endometrioma.Conclusions: We found that patients with dysmenorrhea after surgery, and a younger age of the patient at the time of surgery were the highest risk factors associated with the recurrence of endometrioma, despite long-term postoperative medication

Compact-LWE: Enabling Practically Lightweight Public Key Encryption for Leveled IoT Device Authentication

Leveled authentication allows resource-constrained IoT devices to be authenticated at different strength levels according to the particular types of communication. To achieve efficient leveled authentication, we propose a lightweight public key encryption scheme that can produce very short ciphertexts without sacrificing its security. The security of our scheme is based on the Learning With Secretly Scaled Errors in Dense Lattice (referred to as Compact-LWE) problem. We prove the hardness of Compact-LWE by reducing Learning With Errors (LWE) to Compact-LWE. However, unlike LWE, even if the closest vector problem (CVP) in lattices can be solved, Compact-LWE is still hard, due to the high density of lattices constructed from Compact-LWE samples and the relatively longer error vectors. By using a lattice-based attack tool, we verify that the attacks, which are successful on LWE instantly, cannot succeed on Compact-LWE, even for a small dimension parameter like $n=13$, hence allowing small dimensions for short ciphertexts. On the Contiki operating system for IoT, we have implemented our scheme, with which a leveled Needham-Schroeder-Lowe public key authentication protocol is implemented. On a small IoT device with 8MHZ MSP430 16-bit processor and 10KB RAM, our experiment shows that our scheme can complete 50 encryptions and 500 decryptions per second at a security level above 128 bits, with a public key of 2368 bits, generating 176-bit ciphertexts for 16-bit messages. With two small IoT devices communicating over IEEE 802.15.4 and 6LoWPAN, the total time of completing an authentication varies from 640ms (the 1st authentication level) to 8373ms (the 16th authentication level), in which the execution of our encryption scheme takes only a very small faction from 46ms to 445ms

Functional Encryption for Computational Hiding in Prime Order Groups via Pair Encodings

Lewko and Waters introduced the computational hiding technique in Crypto\u2712. In their technique, two computational assumptions that achieve selective and co-selective security proofs lead to adaptive security of an encryption scheme. Later, pair encoding framework was introduced by Attrapadung in Eurocrypt\u2714. The pair encoding framework generalises the computational hiding technique for functional encryption (FE). It has been used to achieve a number of new FE schemes such as FE for regular languages and unbounded attribute based encryption allowing multi-use of attributes. Nevertheless, the generalised construction of Attrapadung\u27s pair encoding for those schemes is adaptively secure only in composite order groups, which leads to efficiency loss. It remains a challenging task to explore constructions in prime order groups for gaining efficiency improvement, which leaves the research gap in the existing literature. In this work, we aim to address this drawback by proposing a new generalised construction for pair encodings in prime order groups. Our construction will lead to a number of new FE schemes in prime order groups, which have been previously introduced only in composite order groups by Attrapadung

Protecting the Visual Fidelity of Machine Learning Datasets Using QR Codes

Machine learning is becoming increasingly popular in a variety of modern technology. However, research has demonstrated that machine learning models are vulnerable to adversarial examples in their inputs. Potential attacks include poisoning datasets by perturbing input samples to mislead a machine learning model into producing undesirable results. Such perturbations are often subtle and imperceptible from a human\u27s perspective. This paper investigates two methods of verifying the visual fidelity of image based datasets by detecting perturbations made to the data using QR codes. In the first method, a verification string is stored for each image in a dataset. These verification strings can be used to determine whether an image in the dataset has been perturbed. In the second method, only a single verification string stored and is used to verify whether an entire dataset is intact