Anshoo Agarwal

Background: Iron deficiency anemia (IDA) represents a significant public health concern, particularly among female populations. Various demographic factors, including age and socioeconomic status, have a substantial impact on overall health outcomes, contributing to the prevalence of IDA. The primary objective of this study was to assess the knowledge and awareness of iron deficiency anemia among the adult population and to examine its correlation with various sociodemographic factors. Methods: A cross-sectional study was conducted in the Northern Region of Saudi Arabia between October and December 2024. Data were collected using a structured questionnaire from 385 participants aged 18 years and older. The Chi-square test was utilized to assess the association between categorical variables. Results: In this study, 42.5% of participants demonstrated good knowledge of IDA, 48.1% had moderate knowledge, and 9.4% showed poor knowledge. Knowledge levels were significantly associated with gender, age, education, and self-perceived IDA status. Women, older individuals, and those with higher levels of education had a greater knowledge of IDA. Regarding attitude, 93% of participants had a positive attitude, while only 7% exhibited a negative attitude. Interestingly, none of the explanatory variables were significantly linked to attitude, suggesting that positive attitude toward IDA was consistent across all demographic groups. Conclusions: This study highlights the need for targeted health initiatives focusing on diet, supplementation, symptom recognition, and prevention to effectively reduce the burden of IDA. Prioritizing education through symposiums and medical programs in high-prevalence regions is crucial.

Background: AI has been adopted in dentistry for diagnosis, decision making, and therapy prognosis prediction. This systematic review aimed to identify AI models in dentistry, assess their performance, identify their shortcomings, and discuss their potential for adoption and integration in dental practice in the future. Methodology: The sources of the papers were the following electronic databases: PubMed, Scopus, and Cochrane Library. A total of 20 out of 947 needed further studies, and this was encompassed in the present meta-analysis. It identified diagnostic accuracy, predictive performance, and potential biases. Results: AI models demonstrated an overall diagnostic accuracy of 82%, primarily leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs). These models have significantly improved the diagnostic precision for dental caries compared with traditional methods. Moreover, they have shown potential in detecting and managing conditions such as bone loss, malignant lesions, vertical root fractures, apical lesions, salivary gland disorders, and maxillofacial cysts, as well as in performing orthodontic assessments. However, the integration of AI systems into dentistry poses challenges, including potential data biases, cost implications, technical requirements, and ethical concerns such as patient data security and informed consent. AI models may also underperform when faced with limited or skewed datasets, thus underscoring the importance of robust training and validation procedures. Conclusions: AI has the potential to revolutionize dentistry by significantly improving diagnostic accuracy and treatment planning. However, before integrating this tool into clinical practice, a critical assessment of its advantages, disadvantages, and utility or ethical issues must be established. Future studies should aim to eradicate existing barriers and enhance the model’s ease of understanding and challenges regarding expense and data protection, to ensure the effective utilization of AI in dental healthcare.

Benign gallbladder disorders are frequently observed and generally resolve without long-term effects. However, some of these conditions are associated with an increased risk of gallbladder cancer, while others can lead to malignancy. This review aims to inform surgeons of the potential improvements in treatment strategies enabled by earlier recognition of GBC and to assist pathologists in refining histopathological assessments to reduce fatalities associated with delayed diagnoses. The immune markers identified so far are among the most widely explored in gallbladder cancer-related diseases. Diagnosis often occurs at advanced stages when standard treatments fail to deliver results. This limited response in late-stage gallbladder cancer highlights the need for improved prognostic and therapeutic strategies. Although progress has been made, there is still no prognostic marker with full specificity and sensitivity for gallbladder cancer. Various molecular markers have been investigated for this role, with p53 and HER2 showing significant potential. Alterations in these immune markers contribute to gallbladder carcinogenesis. Further comparative studies on their expression in normal, precancerous, and cancerous tissues are essential. Accurate prognostic markers may support tailored treatments and help reduce gallbladder cancer mortality.