An assessment of the impact of structure and type of microplastics on ultrafiltration technology for microplastic remediation.

Microplastic, which is of size less than 5 mm, is gaining a lot of attention as it has become a new arising contaminant because of its ecophysiology impact on the aquatic environment. These microplastics are found in freshwater or drinking water and are the major carriers of pollutants. Removal of this microplastic can be done through the primary treatment process, secondary treatment process, and tertiary treatment process. One approach for microplastic remediation is ultrafiltration technology, which involves passing water through a membrane with small pores to filter out the microplastics. However, the efficiency of this technology can be affected by the structure and type of microplastics present in the water. New strategies can be created to improve the technology and increase its efficacy in removing microplastics from water by knowing how various types and shapes of microplastics react during ultrafiltration. The filter-based technique, that is, ultrafiltration has achieved the best performance for the removal of microplastic. But with the ultrafiltration, too some microplastic that are of sizes less than of ultrafiltration membrane passes through the filter and enters the food chain. Accumulation of this microplastic on the membrane also leads to membrane fouling. Through this review article, we have assessed the impact of the structure, size, and type of MPs on ultrafiltration technology for microplastic remediation, with that how these factors affect the efficiency of the filtration process and challenges occur during filtration.

Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types.

BACKGROUND: Missed fractures are the most common diagnostic errors in musculoskeletal imaging and can result in treatment delays and preventable morbidity. Deep learning, a subfield of artificial intelligence, can be used to accurately detect fractures by training algorithms to emulate the judgments of expert clinicians. Deep learning systems that detect fractures are often limited to specific anatomic regions and require regulatory approval to be used in practice. Once these hurdles are overcome, deep learning systems have the potential to improve clinician diagnostic accuracy and patient care. QUESTIONS/PURPOSES: This study aimed to evaluate whether a Food and Drug Administration-cleared deep learning system that identifies fractures in adult musculoskeletal radiographs would improve diagnostic accuracy for fracture detection across different types of clinicians. Specifically, this study asked: (1) What are the trends in musculoskeletal radiograph interpretation by different clinician types in the publicly available Medicare claims data? (2) Does the deep learning system improve clinician accuracy in diagnosing fractures on radiographs and, if so, is there a greater benefit for clinicians with limited training in musculoskeletal imaging? METHODS: We used the publicly available Medicare Part B Physician/Supplier Procedure Summary data provided by the Centers for Medicare & Medicaid Services to determine the trends in musculoskeletal radiograph interpretation by clinician type. In addition, we conducted a multiple-reader, multiple-case study to assess whether clinician accuracy in diagnosing fractures on radiographs was superior when aided by the deep learning system compared with when unaided. Twenty-four clinicians (radiologists, orthopaedic surgeons, physician assistants, primary care physicians, and emergency medicine physicians) with a median (range) of 16 years (2 to 37) of experience postresidency each assessed 175 unique musculoskeletal radiographic cases under aided and unaided conditions (4200 total case-physician pairs per condition). These cases were comprised of radiographs from 12 different anatomic regions (ankle, clavicle, elbow, femur, forearm, hip, humerus, knee, pelvis, shoulder, tibia and fibula, and wrist) and were randomly selected from 12 hospitals and healthcare centers. The gold standard for fracture diagnosis was the majority opinion of three US board-certified orthopaedic surgeons or radiologists who independently interpreted the case. The clinicians' diagnostic accuracy was determined by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, and specificity. Secondary analyses evaluated the fracture miss rate (1-sensitivity) by clinicians with and without extensive training in musculoskeletal imaging. RESULTS: Medicare claims data revealed that physician assistants showed the greatest increase in interpretation of musculoskeletal radiographs within the analyzed time period (2012 to 2018), although clinicians with extensive training in imaging (radiologists and orthopaedic surgeons) still interpreted the majority of the musculoskeletal radiographs. Clinicians aided by the deep learning system had higher accuracy diagnosing fractures in radiographs compared with when unaided (unaided AUC: 0.90 [95% CI 0.89 to 0.92]; aided AUC: 0.94 [95% CI 0.93 to 0.95]; difference in least square mean per the Dorfman, Berbaum, Metz model AUC: 0.04 [95% CI 0.01 to 0.07]; p < 0.01). Clinician sensitivity increased when aided compared with when unaided (aided: 90% [95% CI 88% to 92%]; unaided: 82% [95% CI 79% to 84%]), and specificity increased when aided compared with when unaided (aided: 92% [95% CI 91% to 93%]; unaided: 89% [95% CI 88% to 90%]). Clinicians with limited training in musculoskeletal imaging missed a higher percentage of fractures when unaided compared with radiologists (miss rate for clinicians with limited imaging training: 20% [95% CI 17% to 24%]; miss rate for radiologists: 14% [95% CI 9% to 19%]). However, when assisted by the deep learning system, clinicians with limited training in musculoskeletal imaging reduced their fracture miss rate, resulting in a similar miss rate to radiologists (miss rate for clinicians with limited imaging training: 9% [95% CI 7% to 12%]; miss rate for radiologists: 10% [95% CI 6% to 15%]). CONCLUSION: Clinicians were more accurate at diagnosing fractures when aided by the deep learning system, particularly those clinicians with limited training in musculoskeletal image interpretation. Reducing the number of missed fractures may allow for improved patient care and increased patient mobility. LEVEL OF EVIDENCE: Level III, diagnostic study.

Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs.

Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation.

A Successful Pharmacist-Based Quality Initiative to Reduce Inappropriate Stress Ulcer Prophylaxis Use in an Academic Medical Intensive Care Unit.

Stress ulcer prophylaxis (SUP) is often inappropriately utilized, particularly in critically ill patients. The objective of this study is to find an effective way of reducing inappropriate SUP use in an academic medical intensive care unit (ICU). Medical ICU patients receiving SUP were identified over a 1-month period, and their charts were reviewed to determine whether American Society of Health-System Pharmacists guidelines were followed. Inappropriate usage was calculated as inappropriate patient-days and converted to incidence per 100 patient-days. Two interventions were implemented: (1) Pharmacists reviewed indications for SUP on each patient during daily team rounds and daily medication reconciliation and (2) residents rotating on ICU services were educated on a bimonthly basis. Postintervention data were obtained in a similar fashion. Prior to intervention, the incidence of inappropriate SUP usage was calculated to be 26.75 per 100 patient-days (n = 1099 total patient-days). Total cost attributable to the inappropriate use was $2433. Post intervention, we were able to decrease the inappropriate incidence of SUP usage to 7.14 per 100 patient-days (n = 1149 total patient-days). In addition, total cost of inappropriate use was reduced to $239.80. Our study highlights an effective multidisciplinary approach to reduce the inappropriate use of SUP in an academic medical ICU. We were able to reduce the incidence of inappropriate use of SUP by 73.31% ( P < .001). Furthermore, we were able to decrease the costs by approximately $2200/month.