Machine learning-based prediction of cerebral oxygen saturation based on multi-modal cerebral oximetry data.

This study develops machine learning-based algorithms that facilitate accurate prediction of cerebral oxygen saturation using waveform data in the near-infrared range from a multi-modal oxygen saturation sensor. Data were obtained from 150,000 observations of a popular cerebral oximeter, Masimo O3™ regional oximetry (Co., United States) and a multi-modal cerebral oximeter, Votem (Inc., Korea). Among these observations, 112,500 (75%) and 37,500 (25%) were used for training and test sets, respectively. The dependent variable was the cerebral oxygen saturation value from the Masimo O3™ (0-100%). The independent variables were the time of measurement (0-300,000 ms) and the 16-bit decimal amplitudes values (infrared and red) from Votem (0-65,535). For the right part of the forehead, the root mean square error of the random forest (0.06) was much smaller than those of linear regression (1.22) and the artificial neural network with one, two or three hidden layers (2.58). The result was similar for the left part of forehead, that is, random forest (0.05) vs logistic regression (1.22) and the artificial neural network with one, two or three hidden layers (2.97). Machine learning aids in accurately predicting of cerebral oxygen saturation, employing the data from a multi-modal cerebral oximeter.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

The Effect of Fear of COVID-19 Infection and Anxiety on Loneliness: Moderated Mediation Effects of Gratitude.

OBJECTIVE: This study was conducted to identify factors related to loneliness during the coronavirus disease-2019 (COVID-19) pandemic and focused on how the fear of COVID-19 infection affects loneliness and the conditional effect of gratitude as a moderator in the relationship among the fear of COVID-19, anxiety, and loneliness. METHODS: For the analysis of this study, a survey was conducted among 1,500 individuals aged 19 to 69 years living in three metropolitan areas in South Korea. Questionnaires included sociodemographic data, psychological experience and stress associated with COVID-19, Generalized Anxiety Disorder Scale-7, UCLA Loneliness Scale-3, and Gratitude Questionnaire-6. An analysis was conducted by applying SPSS PROCESS macro models 4 and 7. RESULTS: First, anxiety mediated the relationship between the fear of COVID-19 infection and loneliness. Second, the effect of the fear of COVID-19 infection on loneliness through anxiety was moderated by gratitude. The higher the gratitude, the more the fear of COVID-19 infection is buffered in the path to anxiety, and the lower the indirect effect on loneliness. CONCLUSION: This suggests that in the context of the COVID-19 pandemic, interventions for psychological problems such as anxiety and loneliness can be carried out through gratitude, a significant protective variable.

Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI.

This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes.

Psychological and Personality Characteristics Associated With COVID-19 Vaccination Behavior in Korean General Population.

BACKGROUND: This study characterized coronavirus disease 2019 (COVID-19) vaccination behavior in the Korean general population using cluster analysis and explored related psychological factors. METHODS: We categorized 1,500 individuals based on their attitudes toward COVID-19 vaccination using hierarchical clustering and identified their level of vaccine acceptance. We examined the associations between vaccine acceptance and behavioral and psychological characteristics. RESULTS: Clustering revealed three groups according to vaccine acceptance: 'totally accepting' (n = 354, 23.6%), 'somewhat accepting' (n = 523, 34.9%), and 'reluctant' (n = 623, 41.5%). Approximately 60% of all participants who belonged to the 'totally accepting' and 'somewhat accepting' groups were willing to receive a COVID-19 vaccine despite concerns about its side effects. High vaccine acceptance was associated with older age, regular influenza vaccination, and trust in formal sources of information. Participants with high vaccine acceptance had higher levels of gratitude, extraversion, agreeableness, and conscientiousness, and lower levels of depression, anxiety, and neuroticism. CONCLUSIONS: People weighed the benefits of COVID-19 vaccination against the risk of side effects when deciding to receive the COVID-19 vaccine. Our findings also indicate that this vaccination behavior may be affected by coping mechanisms and psychological factors.

GAN-based patient information hiding for an ECG authentication system.

Various biometrics such as the face, irises, and fingerprints, which can be obtained in a relatively simple way in modern society, are used in personal authentication systems to identify individuals. These biometric data are extracted from an individual's physiological data and yield high performance in identifying an individual using unique data patterns. Biometric identification is also used in portable devices such as mobile devices because it is more secure than cryptographic token-based authentication methods. However, physiological data could include personal health information such as arrhythmia related patterns in electrocardiogram (ECG) signals. To protect sensitive health information from hackers, the biomarkers of certain diseases or disorders that exist in ECG signals need to be hidden. Additionally, to implement the inference models for both arrhythmia detection and personal authentication in a mobile device, a lightweight model such as a multi-task deep learning model should be considered. This study demonstrates a multi-task neural network model that simultaneously identifies an individual's ECG and arrhythmia patterns using a small network. Finally, the computational efficiency and model size of the single-task and multi-task models were compared based on the number of parameters. Although the multi-task model has 20,000 fewer parameters than the single-task model, they yielded similar performance, which demonstrates the efficient structure of the multi-task model.

Relationship Between Economic Loss and Anxiety During the Coronavirus Disease 2019 Pandemic: Moderating Effects of Knowledge, Gratitude, and Perceived Stress.

Objectives: The prolonged coronavirus disease 2019 (COVID-19) pandemic has caused individuals to suffer economic losses, in particular due to the implementation of intensive quarantine policies. Economic loss can cause anxiety and has a negative psychological impact on individuals, worsening their mental health and satisfaction with life. We examined the protective and risk factors that can influence the relationship between economic loss and anxiety during the COVID-19 pandemic. Methods: Panel data from 911 participants were collected in April and May 2020 and again 6 months later. We analyzed the relationship between economic loss and anxiety and investigated the moderating effects of knowledge about COVID-19, gratitude, and perceived stress. Moreover, we investigated whether there were any changes in moderating effects over time or in different demographic groups. Results: In the early stages of the spread of COVID-19, gratitude (B = -0.0211, F = 4.8130, p < 0.05) and perceived stress (B = 0.0278, F = 9.3139, p < 0.01) had moderating effects on the relationship between economic loss and anxiety. However, after 6 months, only perceived stress had a significant moderating effect (B = 0.0265, F = 7.8734, p < 0.01). Conclusion: In the early stages of COVID-19, lower levels of gratitude and higher perceived stress led to greater anxiety. In later stages of the prolonged pandemic, only perceived stress had a continued moderating effect on the relationship between economic loss and anxiety. This study suggests that psychological interventions to reduce perceived stress are needed to treat the possible adverse effects of the spread of infectious diseases on mental health.

Comparison of Psychosocial Distress in Areas With Different COVID-19 Prevalence in Korea.

Objectives: This study aimed to compare psychosocial distress in areas in Korea with different prevalence of coronavirus disease 2019 (COVID-19) after validating a questionnaire on psychological experiences and stress associated with the disease outbreak. Methods: Using an online-based survey, psychosocial distress associated with COVID-19 was investigated in three regions, which were selected according to their prevalence of COVID-19. A total of 1,500 people from an online public panel in the three regions participated in the study. The questionnaire included sociodemographic information, psychosocial experience and stress related to COVID-19, and the perceived stress scale (PSS), patient health questionnaire-9 (PHQ-9), and generalized anxiety disorder-7 (GAD-7). Those questionnaires and scales were compared by level of prevalence of COVID-19 after validating the questionnaire on psychosocial distress associated with COVID-19. Results: The 19 items on psychosocial experience associated with COVID-19 comprised 5 subscales, with favorable Cronbach's α ranging from 0.69 to 0.88. Six stress items related to COVID-19 had a Cronbach's α of 0.79. Disturbance in eating and sleeping, difficulty with outside activities, stress from COVID-19, and PSS scores were greater in the areas where COVID-19 was highly prevalent. Economic problems, daily activity changes, and anger toward society were higher in the higher-prevalence regions. Discussion: Psychosocial distress associated with COVID-19 was closely related to the prevalence of the disease in the areas where participants lived. Psychosocial interventions for distress associated with COVID-19 should be developed and prepared for people during this lengthy pandemic.

Validation of the Korean Version of the 15-Item Community Assessment of Psychic Experiences in a College Population.

OBJECTIVE: To investigate the reliability and validity of the Korean version of the Community Assessment of Psychic Experiences-15 item positive scale (CAPE-15) in college students. METHODS: This study had two stages: initial screening with self-report questionnaires including the CAPE-15, and semi-structured interviews to investigate the instrument's diagnostic validity. The initial screening involved 1,749 college students. The modified Korean version of Prodromal Questionnaire-16 item (mKPQ-16) was also administered. The criteria for ultra-high risk (UHR) of psychosis in the Comprehensive Assessment of At-Risk Mental States (CAARMS) were the gold standard for diagnosis. RESULTS: Twelve of the interviewed subjects met the CAARMS criteria for UHR of psychosis. The area under the receiver operating characteristic curve was highest (0.936) for the CAPE-15 distress score (p<0.001). The use of 6 as the cutoff for the CAPE-15 distress score resulted in the best balance of sensitivity (91.7%) and specificity (85.2%), with a favorable positive predictive value of 32.4%. The coefficients of correlation between the CAPE-15 and mKPQ-16 were significant. CONCLUSION: The Korean version of the CAPE-15 is a good instrument for screening for psychosis risk in collegiate settings. The validation of this scale could contribute to the early identification of psychosis in the Korean community.

Validation of the Korean version of the 16-Item Prodromal Questionnaire in a Non-Help-Seeking College Population.

OBJECTIVE: To examine the reliability and validity of the Korean version of the 16-item Prodromal Questionnaire (KPQ-16) in non-help-seeking university students. METHODS: Among 2,246 university students participated in the initial screening, 257 subjects with KPQ-16 scores ≥4 were interviewed. The criteria for ultra-high risk (UHR) of psychosis of the Comprehensive Assessment of At-Risk Mental States (CAARMS) were the gold standard for diagnosis. An exploratory modified version of the questionnaire (mKPQ-16), to which three items from the Eppendorf Schizophrenia Inventory were added, was also used to compensate for items on thought and cognitive problems. RESULTS: Seventeen subjects met the CAARMS criteria for UHR of psychosis. The area under the receiver operating characteristic curve (AUROC) was highest for the mKPQ-16 total score (AUROC=0.831, p<0.001). The use of cutoff total scores of 7 for the mKPQ-16 and 6 for the KPQ-16 resulted in the best balance of sensitivity (76.5% and 64.7%, respectively) and specificity (75.4% and 71.2%, respectively). CONCLUSION: The Korean versions of the PQ-16 are good instruments for screening for psychosis risk in university students. This validation of a questionnaire version with a small number of items may make it feasible to screen large numbers of young adults in the community.

CRISPR-Cas9-mediated generation of obese and diabetic mouse models.

Mouse models of obesity (ob/ob) and diabetes (db/db) in which the leptin (Lep) and leptin receptor (Lepr) genes have been mutated, respectively, have contributed to a better understanding of human obesity and type 2 diabetes and to the prevention, diagnosis, and treatment of these metabolic diseases. In this study, we report the first CRISPR-Cas9-induced Lep and Lepr knockout (KO) mouse models by co-microinjection of Cas9 mRNA and sgRNAs that specifically targeted Lep or Lepr in C57BL/6J embryos. Our newly established Lep and Lepr KO mouse models showed phenotypic disorders nearly identical to those found in ob/ob and db/db mice, such as an increase in body weight, hyperglycemia, and hepatic steatosis. Thus, Cas9-generated Lep and Lepr KO mouse lines will be easier for genotyping, to maintain the lines, and to use for future obesity and diabetes research.

Solid-state carbon NMR characterization and investigation of intrinsic dissolution behavior of fluconazole polymorphs, anhydrate forms I and II.

Solid-state (13)C nuclear magnetic resonance ((13)C-SSNMR) revealed significant differences in the chemical shift of specific carbon atoms between two fluconazole polymorphs, reflecting a change in molecular conformation. A single resonance signal without splitting was observed in the spectrum of anhydrate form II, while the spectrum of anhydrate form I showed signals with splitting, indicating the presence of two dissimilar molecular conformations in the unit cell of anhydrate form I. The Fourier transform infrared (FT-IR) and Raman spectra associated with the triazole group, the 2,4-difluorobenzyl group, and the propane backbone provided also evidence of structural differences between forms I and II accompanying with (13)C-SSNMR. (1)H T(1) relaxation times, measured using saturation recovery experiments, showed that the metastable anhydrate form II was more mobile than the stable form I. The solubility and intrinsic dissolution tests showed that the anhydrate form II was more soluble and dissolved faster than the anhydrate form I.

Preparation and pharmaceutical characterization of amorphous cefdinir using spray-drying and SAS-process.

The aim of this study was to investigate the effects of micronization and amorphorization of cefdinir on solubility and dissolution rate. The amorphous samples were prepared by spray-drying (SD) and supercritical anti-solvent (SAS) process, respectively and their amorphous natures were confirmed by DSC, PXRD and FT-IR. Thermal gravimetric analysis was performed by TGA. SEM was used to investigate the morphology of particles and the processed particle had a spherical shape, while the unprocessed crystalline particle had a needle-like shape. The mean particle size and specific surface area were measured by dynamic light scattering (DLS) and BET, respectively. The DLS result showed that the SAS-processed particle was the smallest, followed by SD and the unprocessed cefdinir. The BET result was the same as DLS result in that the SAS-processed particle had the largest surface area. Therefore, the processed cefdinir, especially the SAS-processed particle, appeared to have enhanced apparent solubility, improved intrinsic dissolution rate and better drug release when compared with SD-processed and unprocessed crystalline cefdinir due not only to its amorphous nature, but also its reduced particle size. Conclusions were that the solubility and dissolution rate of crystalline cefdinir could be improved by physically modifying the particles using SD and SAS-process. Furthermore, SAS-process was a powerful methodology for improving the solubility and dissolution rate of cefdinir.

Clathrin-independent internalization of normal cellular prion protein in neuroblastoma cells is associated with the Arf6 pathway.

To understand the role of clathrin-mediated endocytosis in the internalization of normal cellular prion protein (PrP(c)) in neuronal cells, N2a cells were depleted of clathrin by RNA interference. PrP(c) internalization via the constitutive endocytic pathway in the absence of Cu(2+) and the stimulated pathway in the presence of Cu(2+) were measured in both control and clathrin-depleted cells. Depletion of clathrin had almost no effect on the internalization of PrP(c) either in the presence or absence of Cu(2+), in contrast to the marked reduction observed in transferrin uptake. By contrast, the internalization of PrP(c) was inhibited by the raft-disrupting drugs filipin and nystatin, and by the dominant-negative dynamin-1 mutant dynamin-1 K44A, both in the presence and absence of Cu(2+). The internalized PrP(c) was found to colocalize with cargo that traffic in the Arf6 pathway and in large vacuoles in cells expressing the Arf6 dominant-active mutant. These results show that PrP(c) is internalized in a clathrin-independent pathway that is associated with Arf6.

Exchange of clathrin, AP2 and epsin on clathrin-coated pits in permeabilized tissue culture cells.

Clathrin and clathrin adaptors on clathrin-coated pits exchange with cytosolic clathrin and clathrin adaptors in vivo. This exchange might require the molecular chaperone Hsc70 and J-domain-protein auxilin, which, with ATP, uncoat clathrin-coated vesicles both in vivo and in vitro. We find that, although Hsc70 and ATP alone could not uncoat clathrin-coated pits, further addition of auxilin caused rapid uncoating of clathrin but not AP2 and epsin. By contrast, cytosol uncoats clathrin, AP2 and epsin from pits in permeabilized cells, and, concomitantly, these proteins in the cytosol rebind to the same pits, establishing that, like in vivo, these proteins exchange in permeabilized cells. Dissociation and exchange of clathrin in permeabilized cells can be prevented by inhibiting Hsc70 activity. The presence of clathrin-exchange in the permeabilized system substantiates our in vivo observations, and is consistent with the view that Hsc70 and auxilin are involved in the clathrin-exchange that occurs as clathrin-coated pits invaginate in vivo.