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This study found that mice lacking both vitamin D and calcium-sensing receptors in epidermal keratinocytes are predisposed to developing squamous cell carcinoma as they age, due to impaired oxidative stress response and reduced DNA repair capabilities.

This study found that administering minoxidil and PGE₂ to pregnant mice with 22q11.2 deletion syndrome models corrected multiple developmental anomalies, including thymus growth and parathyroid positioning, by targeting prenatal mesenchymal differentiation.

This study reported that pulverizing hair samples significantly improves the precision of testosterone measurement, with higher testosterone concentrations observed in men than in women.

This study presents a DNA-analytical method that can reliably identify and differentiate cashmere, fine wool, yak, and camel hair fibers in both untreated and processed samples.

This study reports that hair analysis could potentially monitor metformin use in diabetic patients, suggesting a possible correlation between daily dose and concentration in dark hair but not in light hair.

This study developed a new method for detecting 30 hormones in anti-aging functional foods, demonstrating good sensitivity and reliability, and identified seven hormones in four out of 14 samples tested.

This study reported that a newly developed UHPLC-MS/MS method detected illegal synthetic hair-growth compounds in 10% of 76 hair-growth products, highlighting the need for ongoing monitoring to protect public health.

Researchers created a reliable method to measure dexpanthenol and resorcinol in hair products.

This study developed and validated a UV spectrophotometric method for accurately estimating Minoxidil in tablet form, showing linearity across concentrations from 1-6µg/ml.

This study developed a validated UV spectrophotometric method that can be used to accurately determine finasteride content in bulk and pharmaceutical formulations.

In this study, homozygous VDR null mutant mice exhibited nonfunctional vitamin D receptors, leading to growth abnormalities and revealing the limited physiological importance of vitamin D pathways outside the classical receptor.

This study observed that carbon paste electrodes catalyze the electrooxidation of minoxidil more efficiently than other electrode types, with a detection limit of approximately 20 ng mL^-1.

In this retrospective observational study, researchers found that the Dean Scale showed better diagnostic accuracy than the CTCAE scale for detecting clinician-reported therapeutic responses to minoxidil in patients with anticancer therapy-induced alopecia, but both scales had limitations in recording clinically significant hair regrowth.

This study found that capped mesoporous silica nanoparticles conjugated with antibodies selectively released a dye when exposed to finasteride, with low detection limits and stable performance even after storage.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

This study developed a simple and rapid method using silver nanoparticles to detect minoxidil concentration in hair-growth formulations, demonstrating a detection limit of 0.35 mM.

In this study, researchers developed an AI-powered platform called VitaDetect, which screens for vitamin deficiencies using image analysis of nails, tongue, and skin, aiming to provide an accessible and early-stage detection tool in resource-limited settings.

This study describes a fast LC/MS/MS method for detecting various synthetic drugs in urine, meeting World Anti-Doping Agency performance limits.

This study reported that the Fe@C/CB electrocatalyst effectively detected Metol in water samples with a low detection limit, high sensitivity, and good stability, suggesting it as a promising tool for environmental monitoring.

This study found that digital droplet PCR (ddPCR) outperformed qPCR in detecting replication competent lentivirus in CAR-T products by offering better sensitivity, specificity, and reproducibility, making it a reliable and rapid method for ensuring patient safety.

This study found that late-onset hypogonadism is relatively common in male patients with long COVID, even among those under 50, and serum free testosterone measurement can help reveal this condition.

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.

This study found that camera trap images revealed mange-like skin lesions in snow leopards and other species in Kyrgyz reserves, with detection rates varying widely and raising concerns about disease spread.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This study highlights the potential of novel non-invasive testing techniques to enhance the diagnosis, treatment, and care of scalp hair diseases, urging future research to improve their accuracy and efficiency.

In this study, researchers developed a method to automatically detect androgen receptor nuclear translocation in dermal papilla cells using fluorescence markers and image analysis, finding that the receptor's nuclear signal peaks about 20 minutes after DHT exposure.

In this study, researchers developed an electrochemical method using gold nanostructures on a glassy carbon electrode to detect minoxidil levels in hair lotions, and found it effectively quantified minoxidil in commercial products, revealing discrepancies in their labeled versus actual concentrations.

This study used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.