Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review.

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.

Effects of motor fatigue on cortical activation level and functional connectivity during upper limb resistance training.

This study aimed to clarify the effects of motor fatigue on cortical activation levels and functional connectivity during upper limb resistance training using functional near-infrared spectroscopy (fNIRS). Ten healthy college students participated in a high intensity upper limb resistance training and fNIRS was used to measure the changes of oxyhemoglobin concentration changes (HbO) in bilateral sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and dorsolateral prefrontal cortex (DLPFC). The integral value (IV) of blood oxygen signal was calculated as an indicator of cortical activation level and the whole brain correlation analysis was used to calculate cortical functional connectivity. The results showed that as motor fatigue deepened, the activation levels of bilateral DLPFC and PMC in early stage were significantly higher than those in later stage (P<0.05), and the functional connectivity strength of the motor related cortex areas between the hemispheres was significantly reduced, which was manifested by the functional connectivity strength of LSMC-RSMC and LPMC-RSMC showed a significant decrease in middle stage compared with that in early stage (P<0.05) and that the functional connectivity strength of LPMC-RSMC and RSMC-SMA showed a significant decrease in later stage compared with that in early stage (P<0.05). In each stage, the motor related cortex areas maintained high activation levels and the cerebral cortex showed extensive functional connectivity.Clinical Relevance- The clinical relevance of this study is to deepen the understanding of the neural processes related to upper limb resistance training based on motor fatigue, and provide a clinical basis for optimizing resistance training strategies related to motor dysfunction patients with altered brain function under fatigue.

The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model.

PURPOSE: By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations. METHODS: Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed. RESULTS: In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score. CONCLUSION: The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.

Prognostic Factors Analysis of Metastatic Recurrence in Cervical Carcinoma Patients Treated with Definitive Radiotherapy: A Retrospective Study Using Mixture Cure Model.

OBJECTIVES: This study aims to identify prognostic factors associated with metastatic recurrence-free survival of cervical carcinoma (CC) patients treated with radical radiotherapy and assess the cure probability of radical radiotherapy from metastatic recurrence. METHODS: Data were from 446 cervical carcinoma patients with radical radiotherapy for an average follow up of 3.96 years. We applied a mixture cure model to investigate the association between metastatic recurrence and prognostic factors and the association between noncure probability and factors, respectively. A nonparametric test of cure probability under the framework of a mixture cure model was used to examine the significance of cure probability of the definitive radiotherapy treatment. Propensity-score-matched (PSM) pairs were generated to reduce bias in subgroup analysis. RESULTS: Patients in advanced stages (p = 0.005) and those with worse treatment responses in the 3rd month (p = 0.004) had higher metastatic recurrence rates. Nonparametric tests of the cure probability showed that 3-year cure probability from metastatic recurrence was significantly larger than 0, and 5-year cure probability was significantly larger than 0.7 but no larger than 0.8. The empirical cure probability by mixture cure model was 79.2% (95% CI: 78.6-79.9%) for the entire study population, and the overall median metastatic recurrence time for uncured patients (patients susceptible to metastatic recurrence) was 1.60 (95% CI: 1.51-1.69) years. Locally advanced/advanced stage was a risk factor but non-significant against the cure probability (OR = 1.078, p = 0.088). The interaction of age and activity of radioactive source were statistically significant in the incidence model (OR = 0.839, p = 0.025). In subgroup analysis, compared with high activity of radioactive source (HARS), low activity of radioactive source (LARS) significantly contributed to a 16.1% higher cure probability for patients greater than 53 years old, while cure probability was 12.2% lower for the younger patients. CONCLUSIONS: There was statistically significant evidence in the data showing the existence of a large amount of patients cured by the definitive radiotherapy treatment. HARS is a protective factor against metastatic recurrence for uncured patients, and young patients tend to benefit more than the elderly from the HARS treatment.

Convergence or Divergence: Preferences for Establishing an Unemployment Subsidy During the COVID-19 Period by Taxing Across Earnings Redistribution in Urban China.

With the advancement of marketization, China has achieved rapid economic growth and economic class differentiation. This research analyzes the data from China's livelihood survey, divides the urban Chinese into five socio-economic classes, and tests their preferences and tendencies for income redistribution. It obtains the general attitude differences in subsidy policy and income inequality during COVID-19. Our conclusion are consistent with the existing literature to a great extent; that is, personal factors (self-interest and belief in fairness) play a crucial role in the attitude of Chinese citizens. In the analysis of situational factors, the results show that the higher the level of marketization, the people are more likely to have stronger negative emotions about subsidy or redistribution policies. Further analysis shows that people with the lowest income are susceptible to the fact that income inequality has become significant and show a strong willingness to support the government's redistribution policy. In contrast, middle-class people tend to favor the government's redistribution policy, although they will not benefit much from the redistribution policy. Therefore, they lack the motivation to support the government in vigorously implementing the subsidy policy. Significantly, high-income people are indifferent, as they lack such motivation even more. The difference in redistribution preferences between upper-class and lower-class groups signals polarization in Chinese society, especially income redistribution.

Quality evaluation of HPV vaccine-related online messages in China: a cross-sectional study.

Since 2019, three types of HPV vaccine have been approved for use in mainland China. High quality messages are crucial for vaccine acceptance, but little is known about the online information quality concerning HPV vaccine in China. "HPV vaccine" and "cervical cancer vaccine" in the form of Chinese were used as keywords through search engines from personal computer (PC), portable mobile device (PMD), and WeChat Public Accounts in 2019. Readability, information content, as well as DISCERN scores were evaluated for each message included. Characteristics associated with quality indicators of the messages were also analyzed. A total of 294 messages from PC engines (104, 35%), PMD engines (128, 43%) and WeChat (62, 21%) were assessed. Most (269, 91%) messages required at least undergraduate readability level. The most frequently reported theme was HPV vaccine and its function (273, 93%), while the least was information regarding quality, safety and side effects (129, 44%). The frequency of messages with at least one error was 132 (45%). The median of sum DISCERN scores was 42 (IQR = 14), and only one (< 1%) message showed good DISCERN quality. Messages retrieved from PC engines and those with pictures were of better overall quality. The overall quality of HPV vaccine-related online messages in Chinese websites was not optimal. Government and health professionals should promote information quality construction to improve the status of HPV vaccination messages online.