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People on Reddit generally have positive opinions about hair loss treatments.

Data visualization tools are crucial for understanding and advancing androgenetic alopecia research.

The AI model DIET-AI effectively diagnoses skin diseases as well as doctors.

The document concluded that biomedical knowledge changes over time and the study evaluated a new method to track these changes.

Visualizing data helps guide future androgenetic alopecia research and policies.

Syntax-based neural networks can match Transformers in handling unseen sentences.

Pre-trained Transformers need extreme retraining to perform well on DarkNet data.

AnnoPharma effectively identifies substances causing adverse drug reactions in medical abstracts.

Reviewers criticized the study for assuming drugs with similar side-effects work the same way and questioned the validity of its findings due to potential biases and data quality issues.

Researchers created LabeledIn, a detailed list of drug uses, showing the importance of human input in making such lists.

The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.

A model called PM-DBiGRU was developed for analyzing sentiments in drug reviews, and it performed better than other models, but struggled with complex sentences and situations requiring background knowledge.

The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.

Drug repositioning is becoming more targeted and efficient with new technologies, offering personalized treatment options and growing interest in the field.

A machine learning model called CATNIP can predict new uses for existing drugs, like using antidepressants for Parkinson's disease and a thyroid cancer drug for diabetes.

The research provides a gene-based framework for hair biology, highlighting the Hippo pathway's importance and suggesting links between hair disorders, cancer pathways, and the immune system.

Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.

Hair loss in Androgenetic alopecia (AGA) is due to altered cell sensitivity to hormones, not increased hormone levels. Hair growth periods shorten over time, causing hair to become thinner and shorter. This is linked to miscommunication between cell pathways in hair follicles. There's also a change in gene expression related to blood vessels and cell growth in balding hair follicles. The exact molecular causes of AGA are still unclear.

Kalya Research is an AI tool that effectively finds and analyzes alternative medicine literature, saving researchers time.

Long-COVID has diverse, long-term health impacts, especially in young people.

Patients using social media have mixed feelings about alopecia treatments, noting hair growth but also frustration with treatment recurrence.

Patients with alopecia areata might have a lower risk of stroke and possibly heart attack.

Social media can help compare the effectiveness and safety of new arthritis medications.

Patients generally feel positive about alopecia areata treatments, but emotions vary by treatment type.

Women inventors focus more on women's health, but there are few of them.

Curcuma aeruginosa Roxb. may help treat hair loss by affecting specific biological pathways.

Most UK survey participants had negative side effects from botulinum toxin injections, with many not fully recovering physically, emotionally, or financially.

The peer review highlighted the need for clearer data handling, questioned the study's validity, and recognized improvements from the original version.

Reviewers suggested the study on finding new drug uses through social media side-effects needs better methods and clearer limitations.

The study improved and was accepted despite initial concerns about data clarity, methodology, and potential overfitting.