Near‐infrared spectroscopy data for foot skin oxygen saturation in healthy subjects

Abstract

Our objective was to evaluate normative data for near-infrared spectroscopy (NIRS) in 110 healthy volunteers by Fitzpatrick skin type (FST) and region of the foot. We obtained measurements of the dorsum and plantar foot using a commercially available device (SnapshotNIR, Kent Imaging, Calgary Canada). On the dorsum of the foot, people with FST6 had significantly lower oxygen saturation compared to FST1-5 (p < 0.001), lower oxyhaemoglobin compared to FST2-5 (p = 0.001), but there was no difference in deoxyhaemoglobin. No differences were found on the plantar foot. When comparing dorsal and plantar foot, there was higher oxyhaemoglobin (0.40 ± 0.09 vs. 0.51 ± 0.12, p < 0.001) and deoxyhaemoglobin (0.16 ± 0.05 vs. 0.21 ± 0.05, p < 0.001) on the plantar foot, but no differences in oxygen saturation (dorsal 70.7 ± 10.8, plantar 70.0 ± 9.5, p = 0.414). In 6.4% of feet, there were black areas, for which no NIRS measurements could be generated. All areas with no data were on the dorsal foot and only found in FST 5–6. People with FST6 had significantly larger areas with no data compared to FST 5 (22.2 cm² ± 20.4 vs. 1.9 cm² ± 0.90, p = 0.007). These findings should be considered when using NIRS technology. Skin pigmentation should be evaluated in future NIRS studies.

Characterization of a novel bacteriophage endolysin (LysAB1245) with extended lytic activity against distinct capsular types associated with <i>Acinetobacter baumannii</i> resistance

by Rosesathorn Soontarach, Potjanee Srimanote, Buppa Arechanajan, Alisa Nakkaew, Supayang Piyawan Voravuthikunchai, Sarunyou Chusri

Capsular polysaccharides are considered as major virulence factors associated with the ability of multidrug-resistant (MDR) Acinetobacter baumannii to cause severe infections. In this study, LysAB1245, a novel bacteriophage-encoded endolysin consisting of a lysozyme-like domain from phage T1245 was successfully expressed, purified, and evaluated for its antibacterial activity against distinct capsular types associated with A. baumannii resistance. The results revealed a broad spectrum activity of LysAB1245 against all clinical MDR A. baumannii isolates belonging to capsular type (KL) 2, 3, 6, 10, 47, 49, and 52 and A. baumannii ATCC 19606. At 2 h following the treatment with 1.7 unit/reaction of LysAB1245, more than 3 log reduction in the numbers of bacterial survival was observed. In addition, LysAB1245 displayed rapid bactericidal activity within 30 min (nearly 3 log CFU/mL of bacterial reduction). Thermostability assay indicated that LysAB1245 was stable over a broad range of temperature from 4 to 70°C, while pH sensitivity assay demonstrated a wide range of pH from 4.5 to 10.5. Furthermore, both minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of LysAB1245 against all MDR A. baumannii isolates and A. baumannii ATCC 19606 were 4.21 μg/mL (0.1 unit/reaction). Conclusively, these results suggest that LysAB1245 possesses potential application for the treatment of nosocomial MDR A. baumannii infections.

Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network

by Yufeng Zhang, Keith D. Aaronson, Jonathan Gryak, Emily Wittrup, Cristian Minoccheri, Jessica R. Golbus, Kayvan Najarian

Background

Timely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization.

Methods

Michigan Medicine heart failure patients from 2013–2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness of the model were evaluated with the mean and standard deviation of the area under the receiver operating curve (AUC), the area under the precision-recall curve (AUPRC), and the F1 score of the ensemble. We inferred membership functions from the model for continuous clinical variables, extracted decision rules, and then evaluated their relative importance.

Results

The model was trained and validated using data from 557 heart failure hospitalizations from 300 patients, of whom 193 received advanced therapies. The mean (standard deviation) of AUC, AUPRC, and F1 scores of the proposed model initialized with clinical knowledge was 0.747 (0.080), 0.642 (0.080), and 0.569 (0.067), respectively, showing superior predictive performance or increased interpretability over other machine learning methods. The model learned critical risk factors predicting the need for advanced therapies in the subsequent hospitalization. Furthermore, our model displayed transparent rule sets composed of these critical concepts to justify the prediction.

Conclusion

These results demonstrate the ability to successfully predict the need for advanced heart failure therapies by generating transparent and accessible clinical rules although further research is needed to prospectively validate the risk factors identified by the model.