Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.

Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography. Cheminformatics, chemometrics, and hybrid approaches were also employed for the modeling and were more reliable in retention prediction over conventional models. Quantitative Structure Retention Relationship (QSRR) was also a potential method for predicting retention in different chromatographic techniques and determining the separation method for analytes. These techniques demonstrated the aids of incorporating QSRR with AI-driven techniques acquiring more precise retention predictions. This review aims at recent exploration of different AI-driven approaches employed for retention prediction in different chromatographic techniques, and due to the lack of summarized literature, it also aims at providing a comprehensive literature that will be highly useful for the society of scientists exploring the field of AI in analytical chemistry.

Current trends in chromatographic prediction using artificial intelligence and machine learning.

Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry.

Inside-out access: a new method of lead placement for patients with central venous occlusions.

BACKGROUND: Physicians will increasingly encounter patients who require rhythm management devices but have venous obstructions that prevent conventional access. Alternate access options, such as thoracotomy or transiliac approaches, exist but are associated with greater cost and morbidity. OBJECTIVE: The purpose of this study is to describe a novel method of vascular access that allows prepectoral placement of conventional pacing and defibrillation leads in patients with complex central venous occlusions. METHODS: Eight patients with central venous occlusions were referred for device implantation. Inside-out central venous access (IOCVA) was obtained via a percutaneous femoral approach. A catheter-dilator system was advanced via the right atrium to the most central point of venous occlusion. The occluded vein segment was punctured with a directionally guided needle, which was advanced along intravascular or extravascular tissue planes to the subclavian region. A solid wire needle was oriented toward the skin surface and advanced through the soft tissues until it exists from the body. The wire was used to pull rigid dilators through the occluded segment. Standard transvenous leads were implanted though the newly created channel. RESULTS: All patients with total central venous occlusions (4 superior vena cava, 4 brachiocephalic and bilateral subclavian) had successful, prepectoral device implants (4 left-sided, 1 single-chamber, 4 dual-chamber, 3 biventricular). No procedure-related complications occurred. All patients had normal device function at follow-up of 485 ± 542 days. CONCLUSION: IOCVA is an effective method of pacemaker and defibrillator implantation for patients with central venous occlusions. Further clinical evaluation of this novel method is needed.

Recalls and safety alerts affecting automated external defibrillators.

CONTEXT: Automated external defibrillators (AEDs) play a key role in the community resuscitation of persons with cardiac arrest and are of proven clinical benefit. Although AEDs are complex medical devices designed to function during life-threatening emergencies, little is known about their reliability. OBJECTIVES: To determine the number and rate of AED recalls and safety alerts, to identify trends in these rates, and to identify the types of malfunctions prompting AED and AED accessory advisories. DESIGN AND SETTING: Analysis of weekly US Food and Drug Administration (FDA) Enforcement Reports between January 1996 and December 2005 was performed to identify all recalls and safety alerts (collectively referred to as "advisories") involving AEDs and AED accessories. Confirmed AED device malfunctions were identified by reviewing AED-related adverse events reported to the FDA. MAIN OUTCOME MEASURES: Number of AEDs and AED accessories subject to FDA recall or safety alert between January 1996 and December 2005; annual AED advisory rates; and number of confirmed fatal AED-related device malfunctions reported to the FDA. RESULTS: During 2.78 million AED device-years of observation, 52 advisories (median [25th and 75th percentiles], 4.5 [3.0 and 5.0] per year) affecting 385,922 AEDs and AED accessories were issued. The mean (SE) annual number of AEDs affected by advisories was 5.1 (1.5) devices per 100 AED device-years. Overall, 21.2% of AEDs distributed during the study period were recalled, most often because of electrical or software problems. The AED advisory rate did not significantly increase during the study period, although the annual number of AED advisories (P for trend =.02) and AED advisory devices (P for trend = .01) did increase. Confirmed fatal AED-related device malfunctions occurred in 370 patients. CONCLUSIONS: Automated external defibrillators and AED accessory advisories occur frequently and affect many devices. Actual AED malfunctions do occur occasionally, although the number of observed malfunctions is small compared with the number of lives saved by these important devices. As the prevalence of AEDs continues to increase, the number of devices affected by advisories can also be expected to increase. Efforts should be directed at developing a reliable system to locate and repair potentially defective devices in a timely fashion.