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We describe a framework for constructing an efficient non-interactive key exchange (NIKE) protocol for n parties for any n ≥ 2. Our approach is based on the problem of computing isogenies between isogenous elliptic curves, which is believed to be difficult. We do not obtain a working protocol because of a missing step that is currently an open mathematical problem. What we need to complete our protocol is an efficient algorithm that takes as input an abelian variety presented as a product of isogenous elliptic curves, and outputs an isomorphism invariant of the abelian variety.

Most patients with refractory secondary/tertiary hyperparathyroidism have monoclonal parathyroid tumors. Inactivating mutations of CDKN1B, encoding the p27 cyclin-dependent kinase inhibitor, were reported to cause hyperparathyroidism in a multiple endocrine neoplasia type 1-like syndrome. Further, there was decreased expression of CDKN1B in parathyroid tumors of patients with chronic kidney disease. We sequenced the entire coding region and splice sites of CDKN1B in 50 parathyroid tumors from 35 patients to see if inactivating mutations could cause monoclonal tumorigenesis in refractory secondary/tertiary hyperparathyroidism. No frameshift, nonsense, or other clearly inactivating mutations were found, nor was there evidence of homozygous deletion or loss of heterozygosity. The absence of clonal inactivating mutations suggests that CDKN1B is not a classical tumor-suppressor gene in secondary/tertiary parathyroid tumors.Kidney International (2008) 73, 1137–1140; doi:10.1038/ki.2008.28; published online 20 February 2008 [ABSTRACT FROM AUTHOR]

In this paper, we present a variant of Diem's O˜(q)${\widetilde{O}(q)}$ index calculus algorithm to attack the discrete logarithm problem (DLP) in Jacobians of genus 3 non-hyperelliptic curves over a finite field 𝔽q. We implement this new variant in C++ and study the complexity in both theory and practice, making the logarithmic factors and constants hidden in the O˜-notation precise. Our variant improves the computational complexity at the cost of a moderate increase in memory consumption, but we also improve the computational complexity even when we limit the memory usage to that of Diem's original algorithm. Finally, we examine how parallelization can help to reduce both the memory cost per computer and the running time for our algorithms.

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2., (© 2022. The Author(s).)

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

In this study, we report the simultaneous use of gold and silver nanoparticles to set a multicolor multiplex lateral flow immunoassay (xLFIA). Silver nanoparticles (AgNPs), spherical in shape and characterized by a brilliant yellow color, were obtained by a new viable one-step synthetic protocol. AgNPs were stable over time and acceptably robust to conditions used for fabricating LFIA devices. These AgNPs were employed as a colorimetric probe in combination with two different kinds of gold nanoparticles (AuNPs) to set a visual xLFIA for detecting allergens. Surface plasmon resonance peaks of probes (AgNPs, spherical and desert rose-like AuNPs) were centered at 420, 525, and 620 nm, respectively. Therefore, the xLFIA output was easily interpreted through a "yellow magenta cyan (YMC)" color code. The prospect of the YMC xLFIA was demonstrated by simultaneously detecting three major allergens in bakery products. Antibodies directed towards casein, ovalbumin, and hazelnut allergenic proteins were individually adsorbed onto metal nanoparticles to produce three differently colored specific probes. These were inserted in a LFIA comprising three lines, each responsive for one allergen. The trichromatic xLFIA was able to detect allergenic proteins at levels as low as 0.1 mg/l and enabled the easy identification of the allergens in commercial biscuits based on the color of the probes. Graphical Abstract.

Background: One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud., Methods: Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process., Results: Our algorithm for training over encrypted data takes 0.4-3.2 hours per iteration of gradient descent., Conclusions: We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.

Homomorphic Encryption (HE) is a powerful cryptographic primitive to address privacy and security issues in outsourcing computation on sensitive data to an untrusted computation environment. Comparing to secure Multi-Party Computation (MPC), HE has advantages in supporting non-interactive operations and saving on communication costs. However, it has not come up with an optimal solution for modern learning frameworks, partially due to a lack of efficient matrix computation mechanisms. In this work, we present a practical solution to encrypt a matrix homomorphically and perform arithmetic operations on encrypted matrices. Our solution includes a novel matrix encoding method and an efficient evaluation strategy for basic matrix operations such as addition, multiplication, and transposition. We also explain how to encrypt more than one matrix in a single ciphertext, yielding better amortized performance. Our solution is generic in the sense that it can be applied to most of the existing HE schemes. It also achieves reasonable performance for practical use; for example, our implementation takes 9.21 seconds to multiply two encrypted square matrices of order 64 and 2.56 seconds to transpose a square matrix of order 64. Our secure matrix computation mechanism has a wide applicability to our new framework E2DM, which stands for encrypted data and encrypted model. To the best of our knowledge, this is the first work that supports secure evaluation of the prediction phase based on both encrypted data and encrypted model, whereas previous work only supported applying a plain model to encrypted data. As a benchmark, we report an experimental result to classify handwritten images using convolutional neural networks (CNN). Our implementation on the MNIST dataset takes 28.59 seconds to compute ten likelihoods of 64 input images simultaneously, yielding an amortized rate of 0.45 seconds per image.

Edible insects emerged as an alternative source of high-quality proteins. Therefore, the effect of an extraction procedure for the recovery of migratory locust (Locusta migratoria) protein concentrate (MLPC) on the compositional characteristics and techno-functional properties was studied. The influence of pH value (2-10) and salt concentration (0, 1 and 3% w/v) on techno-functional properties was evaluated. Proteins were identified and characterized by RP-HPLC, SDS-PAGE and LC-MS/MS. The initial crude protein content of the whole locusts (65.9% on dry base) could be enhanced to 82.3% (MLPC). Solubility profiles of MLPC showed maximum solubility at pH9 (100%). Promising functionality comparable to egg white protein in terms of emulsifying activity at pH5, foamability at pH3 and 3% NaCl, and foam stability at pH9 were found. Consequently, MLPC offers a nutritious protein source with good functional properties at certain conditions, which could be used as food ingredient in a variety of food systems., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In 2016, we organized the third Critical Assessment of Data Privacy and Protection competition as a community effort to bring together biomedical informaticists, computer privacy and security researchers, and scholars in ethical, legal, and social implications (ELSI) to assess the latest advances on privacy-preserving techniques for protecting human genomic data. Teams were asked to develop novel protection methods for emerging genome privacy challenges in three scenarios: Track (1) data sharing through the Beacon service of the Global Alliance for Genomics and Health. Track (2) collaborative discovery of similar genomes between two institutions; and Track (3) data outsourcing to public cloud services. The latter two tracks represent continuing themes from our 2015 competition, while the former was new and a response to a recently established vulnerability. The winning strategy for Track 1 mitigated the privacy risk by hiding approximately 11% of the variation in the database while permitting around 160,000 queries, a significant improvement over the baseline. The winning strategies in Tracks 2 and 3 showed significant progress over the previous competition by achieving multiple orders of magnitude performance improvement in terms of computational runtime and memory requirements. The outcomes suggest that applying highly optimized privacy-preserving and secure computation techniques to safeguard genomic data sharing and analysis is useful. However, the results also indicate that further efforts are needed to refine these techniques into practical solutions., Competing Interests: The authors declare that they have no competing financial interests.

Background: Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data., Methods: We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing., Results: We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation., Conclusions: The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

Background: One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, the task was to encrypt the data using homomorphic encryption in a way that allows it to be stored securely in the cloud, and enables the data owner to query the dataset for the presence of specific mutations, without revealing any information about the dataset or the queries to the cloud., Methods: We devise a novel string matching protocol to enable privacy-preserving queries on homomorphically encrypted data. Our protocol combines state-of-the-art techniques from homomorphic encryption and private set intersection protocols to minimize the computational and communication cost., Results: We implemented our protocol using the homomorphic encryption library SEAL v2.1, and applied it to obtain an efficient solution to the iDASH competition task. For example, using 8 threads, our protocol achieves a running time of only 4 s, and a communication cost of 2 MB, when querying for the presence of 5 mutations from an encrypted dataset of 100,000 mutations., Conclusions: We demonstrate that homomorphic encryption can be used to enable an efficient privacy-preserving mechanism for querying the presence of particular mutations in realistic size datasets. Beyond its applications to genomics, our protocol can just as well be applied to any kind of data, and is therefore of independent interest to the homomorphic encryption community.

Motivation: We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information., Results: To demonstrate PRINCESS' performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster)., Availability and Implementation: https://github.com/achenfengb/PRINCESS_opensource., Contact: shw070@ucsd.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Background: In soybean, at least 16 seed proteins have been identified as causing allergenic reactions in sensitive individuals. As a soybean genebank accession low in the immunodominant protein P34 (Gly m Bd 30K) has recently been found, introgression of the low-P34 trait into adapted soybean germplasm has been attempted in order to improve the safety of food products containing soybean protein. Therefore, marker-assisted selection and proteomics were applied to identify and characterize low-P34 soybeans., Results: In low-P34 lines selected from a cross-population, concentrations of the P34 protein as identified with a polyclonal antibody were reduced by 50-70% as compared to P34-containing controls. Using 2D electrophoresis and immunoblotting, the reduction of P34 protein was verified in low-P34 lines. This result was confirmed by liquid chromatographic-tandem mass spectrometric analysis, which revealed either a reduction or complete absence of the authentic P34 protein as suggested from presence or absence of a unique peptide useful for discriminating between conventional and low-P34 lines., Conclusion: Marker-assisted selection proved useful for identifying low-P34 soybean lines for the development of hypoallergenic soy foods. The status of the P34 protein in low-P34 lines needs further characterization. In addition, the food safety relevance of low-P34 soybeans should be tested in clinical studies. © 2016 Society of Chemical Industry., (© 2016 Society of Chemical Industry.)

The outsourcing of genomic data into public cloud computing settings raises concerns over privacy and security. Significant advancements in secure computation methods have emerged over the past several years, but such techniques need to be rigorously evaluated for their ability to support the analysis of human genomic data in an efficient and cost-effective manner. With respect to public cloud environments, there are concerns about the inadvertent exposure of human genomic data to unauthorized users. In analyses involving multiple institutions, there is additional concern about data being used beyond agreed research scope and being prcoessed in untrused computational environments, which may not satisfy institutional policies. To systematically investigate these issues, the NIH-funded National Center for Biomedical Computing iDASH (integrating Data for Analysis, 'anonymization' and SHaring) hosted the second Critical Assessment of Data Privacy and Protection competition to assess the capacity of cryptographic technologies for protecting computation over human genomes in the cloud and promoting cross-institutional collaboration. Data scientists were challenged to design and engineer practical algorithms for secure outsourcing of genome computation tasks in working software, whereby analyses are performed only on encrypted data. They were also challenged to develop approaches to enable secure collaboration on data from genomic studies generated by multiple organizations (e.g., medical centers) to jointly compute aggregate statistics without sharing individual-level records. The results of the competition indicated that secure computation techniques can enable comparative analysis of human genomes, but greater efficiency (in terms of compute time and memory utilization) are needed before they are sufficiently practical for real world environments.

Hypoparathyroidism (HP) arises most commonly from parathyroid (PT) gland damage associated with neck surgery, and is typically treated with oral calcium and active vitamin D. Such treatment effectively increases levels of serum calcium (sCa), but also brings risk of hypercalciuria and renal damage. There is thus considerable interest in using PTH or PTH analogs to treat HP. To facilitate study of this disease and the assessment of new treatment options, we developed two mouse models of acquired HP, and used them to assess efficacy of PTH(1-34) as well as a long-acting PTH analog (LA-PTH) in regulating blood calcium levels. In one model, we used PTHcre-iDTR mice in which the diphtheria toxin (DT) receptor (DTR) is selectively expressed in PT glands, such that systemic DT administration selectively ablates parathyroid cells. For the second model, we generated GFP-PT mice in which green fluorescent protein (GFP) is selectively expressed in PT cells, such that parathyroidectomy (PTX) is facilitated by green fluorescence of the PT glands. In the PTHcre-iDTR mice, DT injection (2 × 5 μg/kg, i.p.) resulted in moderate yet consistent reductions in serum PTH and sCa levels. The more severe hypoparathyroid phenotype was observed in GFP-PT mice following GFP-guided PTX surgery. In each model, a single subcutaneous injection of LA-PTH increased sCa levels more effectively and for a longer duration (>24 hours) than did a 10-fold higher dose of PTH(1-34), without causing excessive urinary calcium excretion. These new mouse models thus faithfully replicate two degrees of acquired HP, moderate and severe, and may be useful for assessing potential new modes of therapy. © 2015 American Society for Bone and Mineral Research., (© 2015 American Society for Bone and Mineral Research.)

Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size., Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets., Availability and Implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: shw070@ucsd.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: The rapid development of genome sequencing technology allows researchers to access large genome datasets. However, outsourcing the data processing o the cloud poses high risks for personal privacy. The aim of this paper is to give a practical solution for this problem using homomorphic encryption. In our approach, all the computations can be performed in an untrusted cloud without requiring the decryption key or any interaction with the data owner, which preserves the privacy of genome data., Methods: We present evaluation algorithms for secure computation of the minor allele frequencies and χ2 statistic in a genome-wide association studies setting. We also describe how to privately compute the Hamming distance and approximate Edit distance between encrypted DNA sequences. Finally, we compare performance details of using two practical homomorphic encryption schemes--the BGV scheme by Gentry, Halevi and Smart and the YASHE scheme by Bos, Lauter, Loftus and Naehrig., Results: The approach with the YASHE scheme analyzes data from 400 people within about 2 seconds and picks a variant associated with disease from 311 spots. For another task, using the BGV scheme, it took about 65 seconds to securely compute the approximate Edit distance for DNA sequences of size 5K and figure out the differences between them., Conclusions: The performance numbers for BGV are better than YASHE when homomorphically evaluating deep circuits (like the Hamming distance algorithm or approximate Edit distance algorithm). On the other hand, it is more efficient to use the YASHE scheme for a low-degree computation, such as minor allele frequencies or χ2 test statistic in a case-control study.

Increasingly, confidential medical records are being stored in data centers hosted by hospitals or large companies. As sophisticated algorithms for predictive analysis on medical data continue to be developed, it is likely that, in the future, more and more computation will be done on private patient data. While encryption provides a tool for assuring the privacy of medical information, it limits the functionality for operating on such data. Conventional encryption methods used today provide only very restricted possibilities or none at all to operate on encrypted data without decrypting it first. Homomorphic encryption provides a tool for handling such computations on encrypted data, without decrypting the data, and without even needing the decryption key. In this paper, we discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data. We describe how to privately conduct predictive analysis tasks on encrypted data using homomorphic encryption. As a proof of concept, we present a working implementation of a prediction service running in the cloud (hosted on Microsoft's Windows Azure), which takes as input private encrypted health data, and returns the probability for suffering cardiovascular disease in encrypted form. Since the cloud service uses homomorphic encryption, it makes this prediction while handling only encrypted data, learning nothing about the submitted confidential medical data., (Copyright © 2014 Elsevier Inc. All rights reserved.)

CYP27B1, encoding 25-hydroxyvitamin D-1alpha-hydroxylase, converts 25-hydroxyvitamin D to its active form, 1,25-dihydroxyvitamin D, and is expressed primarily in the kidney but also in nontraditional sites including the parathyroid glands. Whereas the role of locally produced 1,25-dihydroxyvitamin D is not yet clear, it is possible that it contributes importantly to vitamin D-mediated inhibition of parathyroid cell growth, so CYP27B1 can be considered a candidate parathyroid tumor suppressor gene in that its acquired inactivation in a parathyroid cell could confer a tumorigenic growth advantage. Expression of CYP27B1 has also been reported to be altered in parathyroid neoplasms. Because detection of inactivating mutations is the central criterion for validating a candidate tumor suppressor, we directly sequenced the coding region and all splice sites of CYP27B1 in 31 sporadic parathyroid adenomas and 31 parathyroid tumors from patients with refractory secondary/tertiary hyperparathyroidism. No nonsense, frameshift, or other inactivating mutations were found, and there was no sign of homozygous deletion. Our findings indicate that CYP27B1 does not commonly serve as a classical tumor suppressor gene in the development of sporadic parathyroid adenomas or of refractory secondary/tertiary hyperparathyroidism.

Objective: To report the case of a man who presented with profoundly elevated parathyroid hormone levels in the setting of hypercalcemia, a palpable neck mass, renal disease, and metabolic bone disease., Methods: We describe the clinical, imaging, and laboratory findings of the patient, including results from genetic testing of the CDC73 gene (HRPT2), and review the relevant literature., Results: A 28-year-old man with a history of childhood abdominal neuroblastoma treated with chemotherapy and field radiation therapy presented with a 2-week history of persistent left scapular pain and swelling. He had a freely mobile, 1-cm, homogeneous, nontender, firm nodule in the right anterior neck. Parathyroid hormone concentration at hospital admission was 1127 pg/mL. Single-photon emission computed tomography after intravenous administration of technetium Tc 99m-labeled sestamibi revealed an intense focus of abnormal radiotracer uptake on early and delayed images in the right anterior inferior neck. Computed tomography imaging of the chest and neck revealed a 1.9-cm, smooth, calcified nodule posterior to the right lobe of the thyroid gland and diffusely osteopenic bones with trabecular resorption and numerous scattered lucent regions consistent with brown tumors. On bilateral neck exploration, a right inferior parathyroid mass and the left superior parathyroid gland were excised. The remaining 2 parathyroid glands were identified intraoperatively and appeared normal. Genetic testing of the CDC73 gene did not detect germline mutations., Conclusions: This case highlights the overlap between the clinical findings seen in primary hyperparathyroidism and parathyroid carcinoma. Enhanced understanding of the genetic and molecular bases of primary hyperparathyroidism and parathyroid carcinoma should aid in the diagnosis of these diseases and the care of affected patients.

Objective: Sequence alterations in untranslated regions (UTRs) of genes are important contributors to human diseases, including hereditary thrombophilia, hereditary hyperferritinaemia-cataract and fragile X mental retardation syndromes. Recently, functional studies of the 3'-UTR of the PTH gene, encoding parathyroid hormone, have highlighted it as a potential target for pathogenic mutations in patients with parathyroid dysfunction. Regulation of PTH gene expression occurs in part through protein binding to a specific 26 nucleotide instability element in the 3'-UTR of PTH mRNA, in a sequence-dependent manner. Thus, the PTH 3'-UTR has emerged as an important potential contributor to parathyroid dysfunction. Therefore, we sought to rigorously examine the PTH 3'-UTR in patients with primary and secondary parathyroid disorders, including primary parathyroid hyperplasia, secondary parathyroid hyperplasia, sporadic parathyroid adenoma and familial hypoparathyroidism of unknown genetic basis., Patients and Design: Twenty-one parathyroid glands from 14 patients with primary parathyroid hyperplasia, 40 sporadic parathyroid adenomas from 40 patients, 42 parathyroid glands from 29 patients with secondary parathyroid hyperplasia and peripheral blood leucocytes from 24 affected members of eight kindreds with familial hypoparathyroidism of unknown genetic basis were examined for mutations in the 3'-UTR of the PTH gene., Results: No alterations from the normal sequence were detected in any of the 127 samples examined., Conclusions: Based on the absence of identifiable DNA sequence alterations in these forms of parathyroid dysfunction, it is unlikely that mutation of the PTH 3'-UTR contributes frequently to their pathogenesis.