Reply to van der Pluijm et al. Comment on “Weitzman et al. Resistance to Antimalarial Monotherapy Is Cyclic. J. Clin. Med. 2022, 11, 781”

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Resistance to Antimalarial Monotherapy Is Cyclic

Malaria is a prevalent parasitic disease that is estimated to kill between one and two million people—mostly children—every year. Here, we query PubMed for malaria drug resistance and plot the yearly citations of 14 common antimalarials. Remarkably, most antimalarial drugs display cyclic resistance patterns, rising and falling over four decades. The antimalarial drugs that exhibit cyclic resistance are quinine, chloroquine, mefloquine, amodiaquine, artesunate, artemether, sulfadoxine, doxycycline, halofantrine, piperaquine, pyrimethamine, atovaquone, artemisinin, and dihydroartemisinin. Exceptionally, the resistance of the two latter drugs can also correlate with a linear rise. Our predicted antimalarial drug resistance is consistent with clinical data reported by the Worldwide Antimalarial Resistance Network (WWARN) and validates our methodology. Notably, the cyclical resistance suggests that most antimalarial drugs are sustainable in the end. Furthermore, cyclic resistance is clinically relevant and discourages routine monotherapy, in particular, while resistance is on the rise. Finally, cyclic resistance encourages the combination of antimalarial drugs at distinct phases of resistance

Deep convolution neural network for screening carotid calcification in dental panoramic radiographs

Ischemic stroke, a leading global cause of death and disability, is commonly caused by carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approach that achieved true labels for each corner, and reached a sensitivity (recall) of 0.82 and a specificity of 0.97 for individual arteries, and a recall of 0.87 and specificity of 0.97 for individual patients. Applying and integrating the algorithm in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences. Author summary Stroke is a leading global cause of death and disability. One major cause of stroke is carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Traditional approaches for CAC detection are doppler ultrasound screening and angiography computerized tomography (CT), medical procedures that incur financial expenses, and are time consuming and discomforting to the patient. Of note, angiography CT involves the injection of contrast material and exposure to X-ray ionizing irradiation. In recent years researchers have shown that CAC can also be detected by analyzing routine panoramic dental radiographs, a non-invasive, cheap and easily accessible procedure. This study takes us one step further, in developing artificial intelligence (AI)-based algorithms trained to detect such calcifications in panoramic dental radiographs. The models developed are based on deep learning convolutional neural networks and transfer learning, that enable an accurate automated detection of carotid calcifications, with a recall of 0.82 and a specificity of 0.97. Statistical approaches for assessing predictions per individual (i.e.: predicting the risk of calcification in at least one artery) were developed, showing a recall of 0.87 and specificity of 0.97. Applying and integrating this approach in healthcare units may significantly contribute to identifying at-risk patients

Mid-Infrared Mapping of Four-Layer Graphene Polytypes Using Near-Field Microscopy

The mid-infrared (MIR) spectral region attracts attention for accurate chemical analysis using photonic devices. Few-layer graphene (FLG) polytypes are promising platforms, due to their broad absorption in this range and gate-tunable optical properties. Among these polytypes, the noncentrosymmetric ABCB/ACAB structure is particularly interesting, due to its intrinsic bandgap (8.8 meV) and internal polarization. In this study, we utilize scattering-scanning near-field microscopy to measure the optical response of all three tetralayer graphene polytypes in the 8.5–11.5 μm range. We employ a finite dipole model to compare these results to the calculated optical conductivity for each polytype obtained from a tight-binding model. Our findings reveal a significant discrepancy in the MIR optical conductivity response of graphene between the different polytypes than what the tight-binding model suggests. This observation implies an increased potential for utilizing the distinct tetralayer polytypes in photonic devices operating within the MIR range for chemical sensing and infrared imaging

Sample size sufficiency analysis.

Ischemic stroke, a leading global cause of death and disability, is commonly caused by carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on panoramic dental radiographs taken from 500 patients, manually labelling each of the patients’ sides (each radiograph was treated as two sides), which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approach that achieved true labels for each corner, and reached a sensitivity (recall) of 0.82 and a specificity of 0.97 for individual arteries, and a recall of 0.87 and specificity of 0.97 for individual patients. Applying and integrating the algorithm in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.</div

Gradient-weighted class activation mapping (Grad-CAM) of a correct prediction of CAC and “clean” classes.

The colors indicate the region that has the greatest impact on the CAC prediction. The color range is presented below (ranging from red, representing the regions with the greatest impact on the CAC prediction, to blue, representing the regions with the lowest impact). It can be noticed that the classifier indeed focused on the region of the calcification signs. Other Grad-CAM of CAC and clean predications can be found in the S3 Fig and S4 Fig.</p

Sample size assessment: F1 score as a function of the sample size, based on random subsampling of 40%-80% of the original data.

Sample size assessment: F1 score as a function of the sample size, based on random subsampling of 40%-80% of the original data.</p

The probabilities and quantities of prediction CAC or “Clean” on three types of patients based on the performance of a single side.

The cells in the table consist of the probabilities and the patient numbers are presented in italic in parentheses.</p

Summary—performances ‘per corner ‘and ‘per patient’.

Summary—performances ‘per corner ‘and ‘per patient’.</p