Introduction Obsessive-compulsive disorder (OCD) is a persistent psychiatric condition that causes significant clinical and functional impairments. Recent research suggests a link between deficiencies in micronutrients, particularly vitamin B12, folic acid, and elevated homocysteine, and the development of OCD. This study investigates the blood levels of these micronutrients and their relationship to OCD severity, with an emphasis on potential gender differences. Methods This cross-sectional study included 300 drug-free OCD patients. Serum levels of vitamin B12, folic acid, and homocysteine were measured using established biochemical methods. The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) was used to assess clinical severity. Data were examined to determine relationships between micronutrient levels and OCD severity and differences between male and female patients. Results This study found that women had higher levels of vitamin B12 (405.3 ± 15.1 vs. 360.4 ± 14.3) and folic acid (7.18 ± 0.27 vs. 5.76 ± 0.25) but lower levels of homocysteine (9.35 ± 0.64 vs. 14.4 ± 0.60) compared to men. Higher folic acid levels were linked to study participants having higher levels of education (at a college or university, where subjects are studied at an advanced level) compared to those with primary-level education. Lower vitamin B12 levels were linked to family mental health history and noncommunicable diseases. Women exhibited lower levels of substance use but higher rates of self-harm and suicide attempts. Elevated homocysteine levels were linked to longer illness duration and more severe OCD symptoms. Conclusion These findings suggest that imbalances in micronutrients, particularly vitamin B12, folic acid, and homocysteine, may contribute to OCD severity and treatment resistance. Gender-specific variations in micronutrient levels could provide valuable insights into personalized OCD therapy choices. Further longitudinal studies are needed to understand these relationships and their potential as therapeutic targets.
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.