Krüppel-like factor 15 is a key suppressor of podocyte fibrosis under rotational force-driven pressure.

Krüppel-like factor 15 (KLF15) is a well-known transcription factor associated with podocyte injury and fibrosis. Recently, hypertensive nephropathy was discovered to be closely related to podocyte injury and fibrosis. However, methods to stimulate hypertension in vitro are lacking. Here, we constructed an in vitro model mimicking hypertension using a rotational force device to identify the role of KLF15 in fibrosis due to mechanically induced hypertensive injury. First, we found that KLF15 expression was decreased in patients with hypertensive nephropathy. Then, an in vitro study of hypertension due to rotational force was conducted, and an increase in fibrosis markers and decrease in KLF15 levels were determined after application of 4 mmHg pressure in primary cultured human podocytes. KLF15 and tight junction protein levels increased with retinoic acid treatment. siRNA-mediated inhibition of KLF15 exacerbated pressure-induced fibrosis injury, and KLF15 expression after treatment with angiotensin II was similar to that observed after treatment with the blood pressure modeling device. Furthermore, the reduced KLF15 levels after mechanical pressure application were restored after the administration of an antihypertensive drug. KLF15 expression was also low in vivo. We confirmed the protective role of KLF15 in fibrosis using a mechanically induced in vitro model of hypertensive injury.

Novel blood pressure and pulse pressure estimation based on pulse transit time and stroke volume approximation.

BACKGROUND: Non-invasive continuous blood pressure monitors are of great interest to the medical community due to their value in hypertension management. Recently, studies have shown the potential of pulse pressure as a therapeutic target for hypertension, but not enough attention has been given to non-invasive continuous monitoring of pulse pressure. Although accurate pulse pressure estimation can be of direct value to hypertension management and indirectly to the estimation of systolic blood pressure, as it is the sum of pulse pressure and diastolic blood pressure, only a few inadequate methods of pulse pressure estimation have been proposed. METHODS: We present a novel, non-invasive blood pressure and pulse pressure estimation method based on pulse transit time and pre-ejection period. Pre-ejection period and pulse transit time were measured non-invasively using electrocardiogram, seismocardiogram, and photoplethysmogram measured from the torso. The proposed method used the 2-element Windkessel model to model pulse pressure with the ratio of stroke volume, approximated by pre-ejection period, and arterial compliance, estimated by pulse transit time. Diastolic blood pressure was estimated using pulse transit time, and systolic blood pressure was estimated as the sum of the two estimates. The estimation method was verified in 11 subjects in two separate conditions with induced cardiovascular response and the results were compared against a reference measurement and values obtained from a previously proposed method. RESULTS: The proposed method yielded high agreement with the reference (pulse pressure correlation with reference R ≥ 0.927, diastolic blood pressure correlation with reference R ≥ 0.854, systolic blood pressure correlation with reference R ≥ 0.914) and high estimation accuracy in pulse pressure (mean root-mean-squared error ≤ 3.46 mmHg) and blood pressure (mean root-mean-squared error ≤ 6.31 mmHg for diastolic blood pressure and ≤ 8.41 mmHg for systolic blood pressure) over a wide range of hemodynamic changes. CONCLUSION: The proposed pulse pressure estimation method provides accurate estimates in situations with and without significant changes in stroke volume. The proposed method improves upon the currently available systolic blood pressure estimation methods by providing accurate pulse pressure estimates.

Development of generalizable automatic sleep staging using heart rate and movement based on large databases.

Purpose: With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography. Methods: A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen's κ as key metrics. Results: The fine-tuned model showed accuracy of 76.6% with Cohen's κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen's κ of 0.673 in another external validation dataset. Conclusion: These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.

Introduction of Supervisor-Type Pediatric Hospitalists in a Tertiary Children's Hospital: Experiences in a Hematology/Oncology Ward.

(1) Background: Hospitalists are healthcare providers who focus on hospitalized patients, but research on the roles of pediatric hospitalists is lacking. This study investigates the role of a supervisor-type hospitalist in a pediatric hematology/oncology ward at a tertiary children's hospital, assessing the impact on satisfaction levels among patient caregivers, resident physicians, and nurses. (2) Methods: A retrospective analysis and online surveys were conducted to assess satisfaction levels before and after the introduction of hospitalists in the Department of Pediatric Hematology/Oncology at Seoul National University Children's Hospital in the Republic of Korea. (3) Results: The introduction of hospitalists led to a 19.3% reduction in prescription error interventions over six months. Unexpected transfers to the intensive care unit decreased from 1.4% to 0.7% (p = 0.229). Patient caregivers reported elevated satisfaction levels with physicians (rated 8.47/10), and there was a significant enhancement in overall satisfaction among nurses (increasing from 3.23 to 4.23/5, p < 0.001). The majority of resident physicians (83.3%) expressed contentment with the hospitalist system, with 77% indicating an interest in transitioning to a hospitalist role. However, these resident physicians also expressed concerns regarding job stability. (4) Conclusions: Supervisor-type pediatric hospitalists have the potential to elevate satisfaction levels not only among patient caregivers but also among nurses and resident physicians, showing promise in improving medical care quality. Nonetheless, ensuring favorable perception and securing job stability within the hospitalist system are pivotal for achieving successful implementation.

Usage of the Internet of Things in Medical Institutions and its Implications.

OBJECTIVES: The purpose of this study was to explore new ways of creating value in the medical field and to derive recommendations for the role of medical institutions and the government. METHODS: In this paper, based on expert discussion, we classified Internet of Things (IoT) technologies into four categories according to the type of information they collect (location, environmental parameters, energy consumption, and biometrics), and investigated examples of application. RESULTS: Biometric IoT diagnoses diseases accurately and offers appropriate and effective treatment. Environmental parameter measurement plays an important role in accurately identifying and controlling environmental factors that could be harmful to patients. The use of energy measurement and location tracking technology enabled optimal allocation of limited hospital resources and increased the efficiency of energy consumption. The resulting economic value has returned to patients, improving hospitals' cost-effectiveness. CONCLUSIONS: Introducing IoT-based technology to clinical sites, including medical institutions, will enhance the quality of medical services, increase patient safety, improve management efficiency, and promote patient-centered medical services. Moreover, the IoT is expected to play an active role in the five major tasks of facility hygiene in medical fields, which are all required to deal with the COVID-19 pandemic: social distancing, contact tracking, bed occupancy control, and air quality management. Ultimately, the IoT is expected to serve as a key element for hospitals to perform their original functions more effectively. Continuing investments, deregulation policies, information protection, and IT standardization activities should be carried out more actively for the IoT to fulfill its expectations.

Renoprotective Effect of KLF2 on Glomerular Endothelial Dysfunction in Hypertensive Nephropathy.

Kruppel-like factor 2 (KLF2) regulates endothelial cell metabolism; endothelial dysfunction is associated with hypertension and is a predictor of atherosclerosis development and cardiovascular events. Here, we investigated the role of KLF2 in hypertensive nephropathy by regulating KLF2 expression in human primary glomerular endothelial cells (hPGECs) and evaluating this expression in the kidney tissues of a 5/6 nephrectomy mouse model as well as patients with hypertension. Hypertension-mimicking devices and KLF2 siRNA were used to downregulate KLF2 expression, while the expression of KLF2 was upregulated by administering simvastatin. After 4 mmHg of pressure was applied on hPGECs for 48 h, KLF2 mRNA expression decreased, while alpha-smooth muscle actin (αSMA) mRNA expression increased. Apoptosis and fibrosis rates were increased under pressure, and these phenomena were aggravated following KLF2 knockdown, but were alleviated after simvastatin treatment; additionally, these changes were observed in angiotensin II, angiotensin type-1 receptor (AT1R) mRNA, and interleukin-18 (IL-18), but not in angiotensin type-2 receptor mRNA. Reduced expression of KLF2 in glomerular endothelial cells due to hypertension was found in both 5/6 nephrectomy mice and patients with hypertensive nephropathy. Thus, our study demonstrates that the pressure-induced apoptosis and fibrosis of glomerular endothelial cells result from angiotensin II, AT1R activation, and KLF2 inhibition, and are associated with IL-18.

Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases.

Although various predictors and methods for BP estimation have been proposed, differences in study designs have led to difficulties in determining the optimal method. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and photoplethysmogram (PPG), from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate (HR), and various PPG morphology features, and BP estimation models constructed using linear regression (LR), random forest (RF), artificial neural network (ANN), and recurrent neural network (RNN) were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ± 6.92 mmHg for systolic BP, and -0.05 ± 3.99 mmHg for diastolic BP were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three standards for accuracy of BP monitors. The results indicate that the proposed method can contribute to the realization of ubiquitous non-invasive continuous BP monitoring.

SERS decoding of micro gold shells moving in microfluidic systems.

In this study, in situ surface-enhanced Raman scattering (SERS) decoding was demonstrated in microfluidic chips using novel thin micro gold shells modified with Raman tags. The micro gold shells were fabricated using electroless gold plating on PMMA beads with diameter of 15 microm. These shells were sophisticatedly optimized to produce the maximum SERS intensity, which minimized the exposure time for quick and safe decoding. The shell surfaces produced well-defined SERS spectra even at an extremely short exposure time, 1 ms, for a single micro gold shell combined with Raman tags such as 2-naphthalenethiol and benzenethiol. The consecutive SERS spectra from a variety of combinations of Raman tags were successfully acquired from the micro gold shells moving in 25 microm deep and 75 microm wide channels on a glass microfluidic chip. The proposed functionalized micro gold shells exhibited the potential of an on-chip microfluidic SERS decoding strategy for micro suspension array.

Analysis of Pulse Arrival Time as an Indicator of Blood Pressure in a Large Surgical Biosignal Database: Recommendations for Developing Ubiquitous Blood Pressure Monitoring Methods.

As non-invasive continuous blood pressure monitoring (NCBPM) has gained wide attraction in the recent decades, many pulse arrival time (PAT) or pulse transit time (PTT) based blood pressure (BP) estimation studies have been conducted. However, most of the studies have used small homogeneous subject pools to generate models of BP based on particular interventions for induced hemodynamic change. In this study, a large open biosignal database from a diverse group of 2309 surgical patients was analyzed to assess the efficacy of PAT, PTT, and confounding factors on the estimation of BP. After pre-processing the dataset, a total of 6,777,308 data pairs of BP and temporal features between electrocardiogram (ECG) and photoplethysmogram (PPG) were extracted and analyzed. Correlation analysis revealed that PAT or PTT extracted from the intersecting-tangent (IT) point of PPG showed the highest mean correlation to BP. The mean correlation between PAT and systolic blood pressure (SBP) was -0.37 and the mean correlation between PAT and diastolic blood pressure (DBP) was -0.30, outperforming the correlation between BP and PTT at -0.12 for SBP and -0.11 for DBP. A linear model of BP with a simple calibration method using PAT as a predictor was developed which satisfied international standards for automatic oscillometric BP monitors in the case of DBP, however, SBP could not be predicted to a satisfactory level due to higher errors. Furthermore, multivariate regression analyses showed that many confounding factors considered in previous studies had inconsistent effects on the degree of correlation between PAT and BP.

Bidirectional Recurrent Auto-Encoder for Photoplethysmogram Denoising.

Photoplethysmography (PPG) has become ubiquitous with the development of smart watches and the mobile healthcare market. However, PPG is vulnerable to various types of noises that are ever present in uncontrolled environments, and the key to obtaining meaningful signals depends on successful denoising of PPG. In this context, algorithms have been developed to denoise PPG, but many were validated in controlled settings or are reliant on multiple steps that must all work correctly. This paper proposes a novel PPG denoising algorithm based on bidirectional recurrent denoising auto-encoder (BRDAE) that requires minimal pre-processing steps and have the benefit of waveform feature accentuation beyond simple denoising. The BRDAE was trained and validated on a dataset with artificially augmented noise, and was tested on a large open database of PPG signals collected from patients enrolled in intensive care units as well as from PPG data collected intermittently during the daily routine of nine subjects over 24 h. Denoising with the trained BRDAE improved signal-to-noise ratio of the noise-augmented data by 7.9 dB during validation. In the test datasets, the denoised PPG showed statistically significant improvement in heart rate detection as compared with the original PPG in terms of correlation to reference and root-mean-squared error. These results indicate that the proposed method is an effective solution for denoising the PPG signal, and promises values beyond traditional denoising by providing PPG feature accentuation for pulse waveform analysis.

Recent advances in electrochemical non-enzymatic glucose sensors - A review.

This review encompasses the mechanisms of electrochemical glucose detection and recent advances in non-enzymatic glucose sensors based on a variety of materials ranging from platinum, gold, metal alloys/adatom, non-precious transition metal/metal oxides to glucose-specific organic materials. It shows that the discovery of new materials based on unique nanostructures have not only provided the detailed insight into non-enzymatic glucose oxidation, but also demonstrated the possibility of direct detection in whole blood or interstitial fluids. We critically evaluate various aspects of non-enzymatic electrochemical glucose sensors in terms of significance as well as performance. Beyond laboratory tests, the prospect of commercialization of non-enzymatic glucose sensors is discussed.

Investigation of Key Circuit Constituents Affecting Drug Sequestration During Extracorporeal Membrane Oxygenation Treatment.

We quantified the influence of the elements of the extracorporeal oxygenation (ECMO) circuit on drug sequestration by focusing on the interactions between materials and drugs. Tubing of three different brands (Tygon/Maquet/Terumo) and oxygenators of two different brands (Maquet/Terumo) were used. Drugs included dexmedetomidine, meropenem, and heparin, which were dissolved in deionized water. Tubing was cut into approximately 7 cm sections and allowed drug solutions enclosed inside by clamping both ends. The oxygenator housing, gas membrane, and heat exchanger were dissected into approximately 1 g pieces and submerged into drug solutions. The experimental samples were then immersed in a water bath at 37°C for 1, 6, 12, and 24 h. After 24 h, the dexmedetomidine concentration was significantly reduced in all three types of tubing (<30.1%), the oxygenator heat exchanger from Maquet Inc. (41.8%), and the gas exchanger from Terumo Inc. (8.6%), while no significant losses were found for meropenem and heparin compared with the control group. The heparin concentration within the Maquet gas exchanger, on the contrary, increased significantly compared with the control group at 1 and 12 h (p < 0.05). Our in vitro study reveals that material selection is a vital part of ECMO development.

Application of genetic algorithm for hemodialysis schedule optimization.

BACKGROUND: The conventional hemodialysis (HD) schedule has been used for decades, even though new modalities have been introduced. Many reasons limit practices of frequent dialysis, such as patients' environments and unknown optimal schedules for each patient. This research provides a theoretical recommendation of HD schedule through genetic algorithm (GA). METHODS: An end-stage renal disease (ESRD) with various dialysis conditions was modeled through a classic variable-volume two-compartment kinetic model to simulate an anuric patient, and GA was implemented to search for an optimal HD schedule for each individual considering and ignoring burden consumption of each dialysis session. The adequacy of the optimized HD schedules through GA was assessed with time average concentration (TAC) and time average deviation (TAD). RESULTS: While ignoring the burden of dialysis sessions, GA returned schedules with slightly improved values of adequacy criteria (EKRc and std Kt/V), compared to the conventional regular uniform HD schedules. The optimized HD schedules also showed decreased TAC and TAD values compared to the conventional regular uniform HD schedules. It showed that frequent dialysis resulted in more effective treatment and higher fitness values. However, when burden was considered, less frequent dialysis schedules showed better fitness value. CONCLUSIONS: Through this research, GA confirmed that at least 12h of dialysis should be conducted for a week. The optimized schedules from GA indicated that evenly distributing the intervals amongst sessions is efficient, and that scheduling a session at the start and end of a week is optimal to overcome a long weekend interval. The theoretical optimal schedule of HD may help distribution of frequent dialysis and provide more schedule options to patients.

A flow cytometry-based submicron-sized bacterial detection system using a movable virtual wall.

Detection of pathogenic bacteria requires a sensitive, accurate, rapid, and portable device. Given that lethal microbes are of various sizes, bacterial sensors based on DC (direct current) impedance on chips should be equipped with channels with commensurate cross sections. When it comes to counting and interrogation of individual bacteria on a microfluidic chip, very narrow channels are required, which are neither easy nor cost-effective to fabricate. Here, we report a flow cytometry-based submicron-sized bacterial detection system using a movable virtual wall made of a non-conducting fluid. We show that the effective dimension of a microfluidic channel can be adjusted by varying the respective flow rates of a sample solution as well as the liquid wall therein. Using such a virtual wall, we have successfully controlled the channel width and detected submicron-sized Francisella tularensis, a lethal, tularemia-causing bacterium. Since the system is capable of monitoring changes in DC impedance and fluorescence simultaneously, we were also able to discriminate between different types of bacterial mixtures containing F. tularensis and E. coli BL21 that have different gamuts of size distributions. The proposed flow cytometry-based system represents a promising way to detect bacteria including, but not limited to, submicron-sized pathogenic microbes.

A label-free DC impedance-based microcytometer for circulating rare cancer cell counting.

Quantification of circulating tumor cells (CTCs) in blood samples is believed to provide valuable evidence of cancer progression, cancer activity status, response to therapy in patients with metastatic cancer, and possible cancer diagnosis. Recently, a number of researchers reported that CTCs tend to lose their epithelial cell adhesion molecule (EpCAM) by an epithelial-mesenchymal transition (EMT). As such, label-free CTC detection methods are attracting worldwide attention. Here, we describe a label-free DC impedance-based microcytometer for CTCs by exploiting the difference in size between CTCs and blood cells. This system detects changes in DC impedance between two polyelectrolytic gel electrodes (PGEs) under low DC voltages. Using spiked ovarian cancer cell lines (OVCAR-3) in blood as a model system, we were able to count the cells using a microcytometer with 88% efficiency with a flow rate of 13 µl min(-1) without a dilution process. Furthermore, we examined blood samples from breast cancer patients using the cytometer, and detected CTCs in 24 out of 24 patient samples. Thus, the proposed DC impedance-based microcytometer presents a facile and fast way of CTC evaluation regardless of their biomarkers.