Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges.

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.

Recurrent Subdural Hematoma: An Institutional Experience.

Background Chronic subdural hematoma (CSDH) is a common neurosurgical problem, which offers a good outcome following surgery. In many cases, burr hole irrigation and drainage under local anesthesia can provide satisfactory results. However, recurrence can be a cause for concern for both the surgeon and the patient. While recurrence is not a frequent phenomenon, studies have reported rates of up to 31.6%. Aims and objectives In this study, our objective is to examine a comprehensive range of potential risk factors and provide valuable insights into identifying patients at a higher risk of recurrence to aid in surgical decision-making. Methodology This study employed a prospective and retrospective design, conducted between 2017 and 2021, at Sri Ramachandra Institute of Higher Education and Research. The study received ethical approval from the Institutional Ethics Committee. The research aimed to assess patients who underwent surgery for CSDH, with a particular focus on those who experienced recurrence. Results The average age of patients with recurrence was 71.5 years compared to 65.2 years in the no-recurrence group, but this difference did not show a significant statistical correlation. A significant male predominance was observed, with 27 men and four women affected (out of a total of 147 men and 73 women in the study), resulting in a statistically significant p-value of 0.01. On multivariate analysis, heterogenous subtypes were a significant predictor of recurrence (OR: 8.88, 95% CI: 6.96-16.54, p = 0.01). The mean midline shift in those with recurrence was 11.4 mm compared to 7.09 mm in those without recurrence. This was a statistically significant correlation with a p-value of 0.02. Regarding those with recurrence, 24 patients underwent evacuation using two burr holes, with one placed in the frontal region and another in the parietal region. All of these patients had a subdural drain placed, which was removed on postoperative day 2. The remaining eight patients underwent a mini-craniotomy for evacuation. We had four cases of refractory CSDH, all of whom underwent the second evacuation using burr holes. Three of them underwent evacuation via craniotomy, while the family of the fourth patient did not give consent for the procedure. Conclusion Patient-related factors such as gender, bilateral presentation, and the presence of hypertension and radiological factors such as the presence of heterogenous subtype and a significant midline shift are clues toward a higher chance of recurrence.