Adolescents completed the five PDS questions about physical development, scored from 1 (no) to 4 (development seems complete) (Petersen et al., 1988 ). Reliability of the PDS was high (α=0.77 for boys, α=.81 for girls). Few (3%) adolescents had missing PDS scores. We developed a coding system to convert the PDS to a 5-point scale in order to parallel the physical exam Tanner stages (available upon request). Although inter-related, puberty is not a single event. Therefore, our coding system differentially captured gonadal and adrenal hormonal signals of physical development. In girls, growth spurt, breast development, and menarche are associated with gonadal hormonal signals. In boys, growth spurt, deepening of voice and facial hair growth are associated with gonadal hormones. For both sexes, pubic/body hair and skin changes are associated with adrenal hormones.
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Hormones
Hormones
Hormones are chemical substances produced in the body that regulate the function of cells and organs.
They play a crucial role in a wide range of physiological processes, including growth, development, metabolism, and reproduction.
Hormones are secreted by endocrine glands, such as the thyroid, adrenal, and pituitary glands, and are transported through the bloodstream to target tissues.
Imbalances in hormone levels can lead to various health conditions, making the study of hormones an important area of medical research.
PubCompare.ai helps researchers optimize their hormone research by providing AI-driven protocol comparisons, enabling them to easily locate the best protocols from literature, pre-prints, and patents, and ensure reproducible, accurate findings.
They play a crucial role in a wide range of physiological processes, including growth, development, metabolism, and reproduction.
Hormones are secreted by endocrine glands, such as the thyroid, adrenal, and pituitary glands, and are transported through the bloodstream to target tissues.
Imbalances in hormone levels can lead to various health conditions, making the study of hormones an important area of medical research.
PubCompare.ai helps researchers optimize their hormone research by providing AI-driven protocol comparisons, enabling them to easily locate the best protocols from literature, pre-prints, and patents, and ensure reproducible, accurate findings.
Most cited protocols related to «Hormones»
Adolescent
Boys
Breast
Face
Gonadal Hormones
Gonads
Hair
Hormones
Human Body
Menarche
Physical Examination
Puberty
Pubic Bone
Skin
Woman
Biological Assay
Hormones
Malignant Neoplasm of Breast
Paraffin Embedding
Woman
All statistical analyses were carried out in SPSS version 16.0 (SPSS, Inc., Chicago, IL) and R version 2.6.0 (www.r-project.org ). For breast cancer subtype prediction with qRT-PCR data, the 50-gene PAM50 classifier, as described in detail by Parker et al. (25 (link)), was used to assign breast cancer subtypes to the 357 training samples. The algorithm maps the gene expression in each specimen to centroids (a multidimensional average expression of the 50 discriminatory genes) that were previously constructed from prototypical examples of the five breast cancer subtypes (luminal A, luminal B, HER2-enriched, basal-like, and normal breast-like) (25 (link)). We assigned a subtype to each tumor specimen tested by calculating the distances to each of the subtype centroids with the Spearman rank correlation test. Tumors for which the difference between Spearman rank correlation coefficients for the luminal A and B centroids was less than 0.1 were considered borderline.
Expression of ER and ER-associated genes is a characteristic of luminal breast cancers as defined by microarray expression profiling (1 (link)–3 (link)). Approximately 30% of tumors in the luminal B cluster expressed HER2 and associated genes, and in this study, we defined tumors that expressed hormone receptor proteins (ER or PR) and were positive for HER2 as being of the luminal–HER2-positive subtype. However, the remaining 70% of luminal B tumors primarily differed from better prognosis luminal A tumors by virtue of higher expression of proliferation genes (15 (link),18 (link)). We investigated whether addition of the proliferation marker Ki67 to the immunopanel of ER, PR, and HER2 could distinguish luminal B tumors (ie, hormone receptor–positive, HER2-negative, and Ki67-high tumors) from luminal A tumors (ie, hormone receptor–positive, HER2-negative, and Ki67-low tumors). In the UBC-WashU series, we used the 50-gene PAM50 classifier to identify tumors as being either luminal A or luminal B, to determine the optimal cut-point value for the Ki67 index. We then compared quantitative data from visual assessment of Ki67 immunohistochemical labeling against these gene expression profile–based assignments for hormone receptor–positive, HER2-negative tumors. The optimal cutoff value for Ki67 was selected by use of the receiver operating characteristic (ROC) method, by minimizing the sum of the observed false-positive and false-negative errors with bootstrapping methodology (33 ). In this fashion, the cutoff value was selected against the gold standard of gene expression profiling, as opposed to assigning a cut point against clinical outcome (which can be difficult to extrapolate to other patient populations with differences in treatment and risk).
The immunopanel thereby defined (ie, ER, PR, HER2, and Ki67) was used to assign tumors of the BCCA validation series to breast cancer subtypes and to assess clinicopathological characteristics and the relation to patient outcome. We estimated 95% confidence intervals (CIs) with bootstrapping methodology (32 (link)) for the reported percentages of luminal subtypes as defined by the immunopanel. Differences in clinicopathological characteristics, including age, tumor grade, tumor size, and lymph node status, among breast cancer subtypes were examined by use of χ2 tests. For univariate survival analysis, relapse-free survival and breast cancer–specific survival were estimated by use of Kaplan–Meier curves (34 (link)), and the statistical significance of survival differences was assessed with a log-rank test (35 (link)). For relapse-free survival, survival time was censored at death if the cause was not breast cancer or if the patient was alive without relapse on June 30, 2004. For breast cancer–specific survival, survival time was censored at death if the cause was not breast cancer or if the patient was still alive on June 30, 2004 (the date for outcome data collection). Patients with unknown cause of death were excluded from breast cancer–specific survival analysis. For multivariable survival analyses, Cox regression models (36 ) were used to estimate the association between the Ki67 index and breast cancer subtypes, with adjustment for with standard clinicopathological variables, including age at diagnosis (as a continuous variable), histological grade (grade 3 vs grade 2 or 1), tumor size (>2 vs ≥2 cm), lymphovascular invasion (positive vs negative), and number of positive axillary lymph nodes as a percentage of the total numbers examined (coded in three categories, in which 0%–25% was compared with 0%, and >25% was compared with 0%). We classified patients with breast cancer in the British Columbia population by using the percentage of positive lymph nodes as a continuous variable in the Cox model because this variable was shown to be more prognostic than a categorical variable of one to three positive lymph nodes vs four or more than positive lymph nodes (37). Only patients with information for all the covariates were included in the Cox regression analyses. Smoothed plots of weighted Schoenfeld residuals were used to test proportional hazard assumptions (38 ), and no evidence that these assumptions were invalid was observed. All statistical tests were two-sided, and P values of less than .05 were considered statistically significant. The data were assembled to provide more than 80% power for testing hypotheses regarding the biomarkers in all patients combined and for patient subgroups that were defined by the adjuvant therapies received.
Expression of ER and ER-associated genes is a characteristic of luminal breast cancers as defined by microarray expression profiling (1 (link)–3 (link)). Approximately 30% of tumors in the luminal B cluster expressed HER2 and associated genes, and in this study, we defined tumors that expressed hormone receptor proteins (ER or PR) and were positive for HER2 as being of the luminal–HER2-positive subtype. However, the remaining 70% of luminal B tumors primarily differed from better prognosis luminal A tumors by virtue of higher expression of proliferation genes (15 (link),18 (link)). We investigated whether addition of the proliferation marker Ki67 to the immunopanel of ER, PR, and HER2 could distinguish luminal B tumors (ie, hormone receptor–positive, HER2-negative, and Ki67-high tumors) from luminal A tumors (ie, hormone receptor–positive, HER2-negative, and Ki67-low tumors). In the UBC-WashU series, we used the 50-gene PAM50 classifier to identify tumors as being either luminal A or luminal B, to determine the optimal cut-point value for the Ki67 index. We then compared quantitative data from visual assessment of Ki67 immunohistochemical labeling against these gene expression profile–based assignments for hormone receptor–positive, HER2-negative tumors. The optimal cutoff value for Ki67 was selected by use of the receiver operating characteristic (ROC) method, by minimizing the sum of the observed false-positive and false-negative errors with bootstrapping methodology (33 ). In this fashion, the cutoff value was selected against the gold standard of gene expression profiling, as opposed to assigning a cut point against clinical outcome (which can be difficult to extrapolate to other patient populations with differences in treatment and risk).
The immunopanel thereby defined (ie, ER, PR, HER2, and Ki67) was used to assign tumors of the BCCA validation series to breast cancer subtypes and to assess clinicopathological characteristics and the relation to patient outcome. We estimated 95% confidence intervals (CIs) with bootstrapping methodology (32 (link)) for the reported percentages of luminal subtypes as defined by the immunopanel. Differences in clinicopathological characteristics, including age, tumor grade, tumor size, and lymph node status, among breast cancer subtypes were examined by use of χ2 tests. For univariate survival analysis, relapse-free survival and breast cancer–specific survival were estimated by use of Kaplan–Meier curves (34 (link)), and the statistical significance of survival differences was assessed with a log-rank test (35 (link)). For relapse-free survival, survival time was censored at death if the cause was not breast cancer or if the patient was alive without relapse on June 30, 2004. For breast cancer–specific survival, survival time was censored at death if the cause was not breast cancer or if the patient was still alive on June 30, 2004 (the date for outcome data collection). Patients with unknown cause of death were excluded from breast cancer–specific survival analysis. For multivariable survival analyses, Cox regression models (36 ) were used to estimate the association between the Ki67 index and breast cancer subtypes, with adjustment for with standard clinicopathological variables, including age at diagnosis (as a continuous variable), histological grade (grade 3 vs grade 2 or 1), tumor size (>2 vs ≥2 cm), lymphovascular invasion (positive vs negative), and number of positive axillary lymph nodes as a percentage of the total numbers examined (coded in three categories, in which 0%–25% was compared with 0%, and >25% was compared with 0%). We classified patients with breast cancer in the British Columbia population by using the percentage of positive lymph nodes as a continuous variable in the Cox model because this variable was shown to be more prognostic than a categorical variable of one to three positive lymph nodes vs four or more than positive lymph nodes (37). Only patients with information for all the covariates were included in the Cox regression analyses. Smoothed plots of weighted Schoenfeld residuals were used to test proportional hazard assumptions (38 ), and no evidence that these assumptions were invalid was observed. All statistical tests were two-sided, and P values of less than .05 were considered statistically significant. The data were assembled to provide more than 80% power for testing hypotheses regarding the biomarkers in all patients combined and for patient subgroups that were defined by the adjuvant therapies received.
Axilla
Biological Markers
Breast
Diagnosis
ERBB2 protein, human
Gene Expression
Genes
Genes, Neoplasm
Gold
Hormones
Malignant Neoplasm of Breast
Microtubule-Associated Proteins
Neoplasms
Nodes, Lymph
Patients
Pharmaceutical Adjuvants
Prognosis
Proteins
Relapse
ARID1A protein, human
Drainage
Hormones
Lymph Node Dissection
Lymphography
Nodes, Lymph
Pelvis
Physicians
Population at Risk
Prostate
Radiation Oncologists
Radiotherapy
Sentinel Lymph Node
Therapeutics
X-Ray Computed Tomography
Frozen leaf material (about 100 mg FW.) was ground in liquid nitrogen with the mixer mill MM400 (Retsch GmbH, Haan, Germany) in a 2 ml Eppendorf tube, and then extracted with 1 ml of extraction solvent (methanol:isopropanol, 20:80 (v/v) with 1% of glacial acetic acid) using ultra sonication (4-7°C). The labeled forms of the compounds d4-SA, d6-ABA, d5-JA, d5-IAA, d2-GA1, d2-GA4, d2-GA9, d2-GA19, d2-GA20, d2-GA24, d4-ACC, d6-2iP, d6-IPA, d5-Z and d5-ZR were added as internal standards. D5-Z and d5-ZR were used as internal standards for DHZ and DHZR, respectively. After centrifugation (10,000 rpm for 15 min at 4°C), the supernatant was collected and the pellet was re-extracted with 0.5 ml of extraction solvent and the extraction repeated three times again. Then, supernatants were combined and dried completely under a nitrogen stream and re-dissolved in 300 μl of methanol, centrifuged (10,000 rpm for 5 min) and filtered through a 0.22 μm PTFE filter (Waters, Milford, MA, USA). Samples (5 μl) were then analyzed by UPLC/ESI-MS/MS. Hormones were determined in ten independent samples for each treatment. Quantification was done by the creation of calibration curves including each of the 17 unlabeled analyte compounds (SA, ABA, JA, IAA, GA1, GA4, GA9, GA19, GA20, GA24, ACC, 2iP, IPA, Z, ZR, DHZ and DHZR). Ten standard solutions were prepared ranging from 0.05 to 1000 ng ml-1 and for each solution a constant amount of internal standard (as described above) was added. Calibration curves for each analyte were generated using Analyst™ software (Applied Biosystems, Inc., California, USA). The limit of detection (LOD, S/N = 3) and the limit of quantification (LOQ, S/N = 10) were also calculated with the aid of this software.
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Acetic Acid
Centrifugation
compound 17
Freezing
Hormones
Isopropyl Alcohol
Methanol
Nitrogen
Plant Leaves
Polytetrafluoroethylene
Solvents
Tandem Mass Spectrometry
Most recents protocols related to «Hormones»
Example 16
Direct analysis of chemicals in animal tissue using probes of the invention was performed as shown in
Lipid profiles were obtained for human prostate tissues (1 mm2×15 μm,
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Adrenal Glands
Animals
Epinephrine
Homo sapiens
Hormones
Ions
Lipids
Mass Spectrometry
Methanol
Neoplasms
Norepinephrine
Phosphatidylcholines
Phospholipids
Pigs
Prostate
Solvents
Sphingomyelins
Tissues
Example 3
At the time of diagnosis with prostate cancer, subjects are invited to participate in a trial. A subject sample, e.g., blood, is obtained. Periodically, throughout the monitoring, watchful waiting, or active treatment of the subject, e.g., chemotherapy, radiation therapy, e.g., radiation of the prostate, surgery, e.g., surgical prostate resection, hormone therapy, a new subject sample is obtained. At the end of the study, all subject samples are tested for the level of FLNA and/or PSA, and optionally other markers. The subject samples are matched to the medical records of the subjects to correlate FLNA and/or PSA levels, as appropriate, with prostate cancer status at the time of diagnosis, rate of progression of disease, response of subjects to one or more interventions, and transitions between androgen dependent and independent status. Other markers, such as the expression level of keratin 19 and/or filamin B, the age of the subjects, or the prostate volume of the subjects, can also be analyzed in addition to filamin A and/or PSA.
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Androgens
BLOOD
Diagnosis
Disease Progression
Filamin A
Filamin B
Hormones
Keratin-19
Operative Surgical Procedures
Pharmacotherapy
Prostate
Prostate Cancer
Prostatectomy
Radiotherapy
Therapeutics
Example 20
Fertility—Progesterone is one of the most important hormones for pregnancy with myriad functions from ensuring implantation of the egg into a healthy uterine wall, to ensuring embryo survival and prevention of immune rejection of the developing baby. Many other hormones act in concert with progesterone, like Follicular Stimulating Hormone (FSH) and Luteinizing Hormone (LH) and can be used to assess optimal fertility windows on a monthly basis. And in fact an over dominant production of estrogen can lead to progesterone deficiency and thus difficulty getting or staying pregnant. It is important that women not only monitor FSH and LH to determine optimal fertility for getting pregnant, but ensure that sufficient levels or progesterone are being produced to ensure pregnancy and viability of the fetus. A study from the British Medical Journal, 2012, demonstrated that a single progesterone level test can help discriminate between viable and nonviable pregnancies. Among women who had an ultrasound, 73 percent had nonviable pregnancies. But among women with progesterone levels below 3 to 6 nanograms per milliliter, the probability of a nonviable pregnancy rose to more than 99 percent (Gallos L et al. British Medical J, 2012).
Perimenopause—Monitoring hormone levels during the menopausal transition may help women better understand important changes in their body and allow them to make more informed decisions about health, diet, and lifestyle. According to Hale G E (Best Pract Res Clin Obstet Gynaecol, 2009), data from endocrine studies on women throughout the menopausal transition show changes in levels of steroid hormones and gonadotropins (Progesterone, Estrodiol, LH, FSH and AMH) and follicle-stimulating hormone undergoes the first detectable change while menstrual cycles remain regular. Erratic and less predictable changes in steroid hormones follow, especially with the onset of irregular cycles. Later serum hormone studies on the inhibins and anti-Mullerian hormone established that diminishing ovarian follicle number contributes to the endocrine changes with advancing reproductive age.
Many fertility issues revolve around genetic, anatomical or other disorders that may either prevent a woman from becoming pregnant and/or staying pregnant. Some of these disorders include hormonal imbalances, diabetes, a short or insufficient cervix, and acute or chronic infections. A cascade of genes has been implicated in the occurrence of getting and staying pregnant. These genes have been studied using genotyping, gene expression, and proteomic analysis to assess a woman's ability to stay pregnant.
In some embodiments the disclosed device focuses on detecting levels of Progesterone, LH, FSH, Estrodiol, AMH, genotyping, gene expression through RNA and methylome sequencing, qPCR and proteomic analysis for fertility and menopause management from menstrual blood or cervicovaginal fluid.
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BLOOD
Cervix Uteri
Chronic Infection
Diabetes Mellitus
Diet
Embryo
Endocrine System Diseases
Epigenome
Estrogens
Fertility
Fetal Viability
Follicle-stimulating hormone
Gene Expression
Genes
Genes, vif
Gonadotropins
Hormones
Human Body
Human Follicle Stimulating Hormone
Infant
Inhibin
Luteinizing hormone
Medical Devices
Menopause
Menstrual Cycle
Menstruation
Mullerian-Inhibiting Hormone
Ovarian Follicle
Ovum Implantation
Perimenopause
Pregnancy
Progesterone
Reproduction
Steroids
System, Endocrine
Transcription, Genetic
Ultrasonography
Uterus
Woman
The following patients were eligible for analysis: (1) CR, the diagnostic criteria: Clinical symptoms, physical examination, and confirmation of the unilateral disc herniation via cervical CT or magnetic resonance imaging (MRI); (2) Patients aged >18 years; (3) Lower cervical radicular pain lasting ≤3 months; (4) Numerical rating scale, NRS≥ 4.
The following patients were excluded from analysis: (1) Severe heart disease; (2) Severe spinal deformity; (3) Hypersensitivity to local anesthetics or hormones; (4) Coagulation dysfunction; (5) Systemic infection or skin infection at the puncture site; (6) Patients with abnormal mental behavior, severe anxiety, or depression; (7) Lactating and pregnant women; (8) History of cervical surgery; (9) Cervical spondylotic myelopathy; (10) Moderate and severe foraminal stenosis.
The following patients were excluded from analysis: (1) Severe heart disease; (2) Severe spinal deformity; (3) Hypersensitivity to local anesthetics or hormones; (4) Coagulation dysfunction; (5) Systemic infection or skin infection at the puncture site; (6) Patients with abnormal mental behavior, severe anxiety, or depression; (7) Lactating and pregnant women; (8) History of cervical surgery; (9) Cervical spondylotic myelopathy; (10) Moderate and severe foraminal stenosis.
Anxiety
Cellulitis
Coagulation, Blood
Congenital Abnormality
Diagnosis
Heart Diseases
Hormones
Hypersensitivity
Intervertebral Disk Displacement
Local Anesthetics
Mentally Ill Persons
Neck
Neck Pain
Operative Surgical Procedures
Patients
Physical Examination
Pregnant Women
Punctures
Sepsis
Spinal Cord Diseases
Spondylosis, Cervical
Stenosis
Tooth Root
This was a retrospective study and was approved by the Ethical Committee of our hospital. We reviewed cervical spondylosis patients undergoing surgery in our hospital between January 2014 and December 2021 in our orthopedic department. The basic information of patients was inquired according to the case system. The disease time was determined according to the patient's complaint in the case system. In this work, the inclusion criteria were as follows: [1 (link)] diagnosis of cervical spondylosis; [2 (link)] patients with preoperative cervical CT and X-ray within 1 week before surgery; and [3 (link)] accept cervical surgery at our orthopedic department. The exclusion criteria were as follows: [1 (link)] patients with spine infection, spine tumor, spine trauma and metabolic bone disease; [2 (link)] merged cervical spine posterior longitudinal ligament ossification or multiple osteosclerosis; [3 (link)] long-term use of hormones or combined with immune diseases; [4 (link)] patient with nervous system disorders such as demyelinating disease; [5 (link)] a history of previous spinal surgery; [6 (link)] diagnosed with osteoporosis and treated with medication; and [7 (link)] incomplete radiologic data.
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Cervical Vertebrae
Demyelinating Diseases
Hormones
Immune System Diseases
Infection
Metabolic Bone Disease
Neck
Nervous System Disorder
Operative Surgical Procedures
Orthopedic Surgical Procedures
Ossification of Posterior Longitudinal Ligament
Osteoporosis
Osteosclerosis
Patients
Pharmaceutical Preparations
Radiography
Spinal Injuries
Spinal Neoplasms
Spondylosis, Cervical
Vertebral Column
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
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17β-estradiol is a natural estrogen hormone produced by the ovaries, adrenal glands, and other tissues in the body. It is a key component in various laboratory and research applications, serving as a substrate, reference standard, or analytical tool for the study of estrogen-related processes and pathways.
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Progesterone is a steroid hormone that plays a crucial role in the female reproductive system. It is a key component in the regulation of the menstrual cycle and supports the maintenance of pregnancy. Progesterone is commonly used in various lab equipment and scientific research applications.
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SPSS is a software package used for statistical analysis. It provides a graphical user interface for data manipulation, statistical analysis, and visualization. SPSS offers a wide range of statistical techniques, including regression analysis, factor analysis, and time series analysis.
More about "Hormones"
Hormones are essential chemical messengers that regulate a wide range of physiological processes within the body.
These potent biomolecules, produced by endocrine glands like the thyroid, adrenal, and pituitary, circulate through the bloodstream to target specific tissues and cells.
Hormone imbalances can lead to various health conditions, making the study of these regulatory compounds a critical area of medical research.
Researchers investigating hormones often utilize techniques and materials like fetal bovine serum (FBS), penicillin/streptomycin, SAS 9.4 statistical software, insulin, DMEM cell culture medium, and the MCF-7 breast cancer cell line.
Experiments may also involve the use of key hormones like 17β-estradiol and progesterone.
Understanding the complex roles of hormones in growth, development, metabolism, and reproduction is paramount.
Optimizing hormone research through the use of AI-driven protocol comparisons, as offered by PubCompare.ai, can help researchers identify the best methods from literature, preprints, and patents.
This ensures reproducible, accurate findings and accelerates advancements in the field of endocrinology.
Whether exploring thyroid function, insulin signaling, or steroid hormone dynamics, leveraging intelligent platforms like PubCompare.ai can streamline the research process and lead to more informed decisions.
These potent biomolecules, produced by endocrine glands like the thyroid, adrenal, and pituitary, circulate through the bloodstream to target specific tissues and cells.
Hormone imbalances can lead to various health conditions, making the study of these regulatory compounds a critical area of medical research.
Researchers investigating hormones often utilize techniques and materials like fetal bovine serum (FBS), penicillin/streptomycin, SAS 9.4 statistical software, insulin, DMEM cell culture medium, and the MCF-7 breast cancer cell line.
Experiments may also involve the use of key hormones like 17β-estradiol and progesterone.
Understanding the complex roles of hormones in growth, development, metabolism, and reproduction is paramount.
Optimizing hormone research through the use of AI-driven protocol comparisons, as offered by PubCompare.ai, can help researchers identify the best methods from literature, preprints, and patents.
This ensures reproducible, accurate findings and accelerates advancements in the field of endocrinology.
Whether exploring thyroid function, insulin signaling, or steroid hormone dynamics, leveraging intelligent platforms like PubCompare.ai can streamline the research process and lead to more informed decisions.