Soft phenotyping for sepsis via EHR time-aware soft clustering.

OBJECTIVE: Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories, identifying sepsis sub-phenotypes is crucial to advance our understanding of sepsis characterization, to choose targeted treatments and optimal timing of interventions, and to improve prognostication. Prior studies have described different sub-phenotypes of sepsis using organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships among the sub-phenotypes for clustering procedures. METHODS: We developed a time-aware soft clustering algorithm guided by clinical variables to identify sepsis sub-phenotypes using data available in the EHR. RESULTS: We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. CONCLUSION: Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more accurate information on sepsis-related organ dysfunction and sepsis recovery trajectories which can be important to inform management decisions and sepsis prognosis.

Functional connectome hierarchy of thalamus impacts fatigue in acute stroke patients.

This study aimed to explore the topographic features of thalamic subregions, functional connectomes and hierarchical organizations between thalamus and cortex in poststroke fatigue patients. We consecutively recruited 121 acute ischemic stroke patients (mean age: 59 years) and 46 healthy controls matched for age, sex, and educational level. The mean age was 59 years (range 19-80) and 38% of acute stroke patients were females. Resting-state functional and structural magnetic resonance imaging were conducted on all participants. The fatigue symptoms were measured using the Fatigue Severity Scale. The thalamic functional subdivisions corresponding to the canonical functional network were defined using the winner-take-all parcellation method. Thalamic functional gradients were derived using the diffusion embedding analysis. The results suggested abnormal functional connectivity of thalamic subregions primarily located in the temporal lobe, posterior cingulate gyrus, parietal lobe, and precuneus. The thalamus showed a gradual increase from the medial to the lateral in all groups, but the right thalamus shifted more laterally in poststroke fatigue patients than in non- poststroke fatigue patients. Poststroke fatigue patients also had higher gradient scores in the somatomotor network and the right medial prefrontal and premotor thalamic regions, but lower values in the right lateral prefrontal thalamus. The findings suggested that poststroke fatigue patients had altered functional connectivity and thalamocortical hierarchical organizations, providing new insights into the neural mechanisms of the thalamus.

Federated clustered multi-domain learning for health monitoring.

Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.

The Top 100 Cited Articles on Acupuncture in the Last 20 Years: A Bibliometric Analysis.

BACKGROUND: Acupuncture has gained increasing international attention in recent decades. The act of incorporating acupuncture treatment into the routine treatment of COVID-19 in China drove us to review the 100 most influential articles of the last 20 years to learn about the current status and trends of acupuncture. METHOD: Articles related to acupuncture from January 1, 2001, to July 4, 2022, were searched in the Clarivate Analytics Web of Science Core Collection database. The top 100 most cited publications were selected, and information was extracted. Software such as VOSviewer, GraphPad Prism, Scimago Graphica, and CiteSpace were used to visualize and analyze the extracted data. RESULT: The 100 most cited articles were identified, with an average of 218 citations (range: 131-625). The majority of the top 100 articles were from the USA (n = 53). The institution that published the most highly cited papers was Harvard University (n = 16). The most influential team was Klaus Linde's group. Pain was the top-ranked journal in terms of the number of publications. The largest clusters for co-occurrence keyword analysis focused on acupuncture and electroacupuncture analgesia and brain imaging responses to acupuncture stimulation via functional MRI. The two highest strength burst keywords were "randomized controlled trials" and "osteoarthritis," with "randomized controlled trials" being a consistent burst keyword from 2011 to the present. CONCLUSION: This study provides insight into articles of historical significance in the field of acupuncture through bibliometric analysis. These data should provide clinicians and researchers with insight into future directions related to acupuncture.Die Akupunktur hat in den vergangenen Jahrzehnten zunehmend die internationale Aufmerksamkeit auf sich gezogen. Die Einbeziehung der Akupunktur in die Routinebehandlung von COVID-19 in China hat uns dazu veranlasst, die 100 einflussreichsten Artikel der letzten 20 Jahre zu überprüfen, um etwas über den aktuellen Stand und die Trends in der Akupunktur zu erfahren.Artikel mit Bezug zu Akupunktur vom 1. Januar 2001 bis 4. Juli 2022 wurden in der Clarivate Analytics Web of Science Core Collection Datenbank gesucht. Die 100 am häufigsten zitierten Veröffentlichungen wurden ausgewählt und die Informationen wurden extrahiert. Visualisierung und Analyse der extrahierten Daten erfolgten mithilfe der Software VOSviewer, GraphPad Prism, Scimago Graphica und CiteSpace.Es wurden die 100 am häufigsten zitierten Artikel ermittelt mit durchschnittlich 218 Zitierungen (Spanne: 131 bis 625). Der Großteil der 100 meistzitierten Artikel stammte aus den Vereinigten Staaten (n = 53). Die Institution, die die meisten zitierten Artikel veröffentlichte, war die Harvard-Universität (n = 16). Das einflussreichste Team war die Gruppe von Klaus Linde. Was die Zahl der Veröffentlichungen betrifft, war Pain die am häufigsten zitierte Zeitschrift. Bei der Analyse der Koinzidenz von Schlüsselwörtern lag der Fokus der größten Cluster auf Akupunktur-und Elektroakupunktur-Analgesie sowie auf den Reaktionen auf Akupunkturstimulation in den bildgebenden Untersuchungen des Gehirns mittels funktioneller MRT. Die beiden stärksten Burst-Schlüsselwörter waren "randomisierte kontrollierte Studien" und "Osteoarthritis," wobei "randomisierte kontrollierte Studien" von 2011 bis heute durchgängig ein Burst-Schlüsselwort ist.Diese Studie bietet einen Einblick in historisch bedeutsame Artikel auf dem Gebiet der Akupunktur mittels bibliometrischer analyse. Diese Daten sollen Klinikern und Forschern einen Einblick in zukünftige Richtungen im Zusammenhang mit Akupunktur geben.

Timeline Registration for Electronic Health Records.

Electronic Health Record (EHR) data are captured over time as patients receive care. Accordingly, variations among patients, such as when a patient presents for care during the course of a disease, introduce bias into standard longitudinal EHR data analysis methods. We, therefore, aim to provide an alignment method that reduces this bias. We structure this task as a registration problem. While limited prior research on longitudinal EHR data considered registration, we propose a robust registration method to provide better data alignment by estimating the optimum time shift at each time point. We validate the proposed method for mortality prediction. We utilize a Recurrent Neural Network (RNN), time-varying Cox regression model, and Logistic Regression (LR) for mortality prediction. Results suggest our proposed registration method enhances mortality prediction with at least a 1-2% increase in major evaluation metrics utilized.

Altered Resting-State Brain Activity and Functional Connectivity in Post-Stroke Apathy: An fMRI Study.

Apathy is a common neuropsychiatric disease after stroke and is linked to a lower quality of life while undergoing rehabilitation. However, it is still unknown what are the underlying neural mechanisms of apathy. This research aimed to explore differences in the cerebral activity and functional connectivity (FC) of subjects with post-stroke apathy and those without it. A total of 59 individuals with acute ischemic stroke and 29 healthy subjects with similar age, sex, and education were recruited. The Apathy Evaluation Scale (AES) was used to evaluate apathy at 3 months after stroke. Patients were split into two groups-PSA (n = 21) and nPSA (n = 38)-based on their diagnosis. The fractional amplitude of low-frequency fluctuation (fALFF) was used to measure cerebral activity, as well as region-of-interest to region-of-interest analysis to examine functional connectivity among apathy-related regions. Pearson correlation analysis between fALFF values and apathy severity was performed in this research. The values of fALFF in the left middle temporal regions, right anterior and middle cingulate regions, middle frontal region, and cuneus region differed significantly among groups. Pearson correlation analysis showed that the fALFF values in the left middle temporal region (p < 0.001, r = 0.66) and right cuneus (p < 0.001, r = 0.48) were positively correlated with AES scores in stroke patients, while fALFF values in the right anterior cingulate (p < 0.001, r = -0.61), right middle frontal gyrus (p < 0.001, r = -0.49), and middle cingulate gyrus (p = 0.04, r = -0.27) were negatively correlated with AES scores in stroke patients. These regions formed an apathy-related subnetwork, and functional connectivity analysis unveiled that altered connectivity was linked to PSA (p < 0.05). This research found that abnormalities in brain activity and FC in the left middle temporal region, right middle frontal region, right cuneate region, and right anterior and middle cingulate regions in stroke patients were associated with PSA, revealing a possible neural mechanism and providing new clues for the diagnosis and treatment of PSA.

Assessment of reporting quality in randomized controlled trials of acupuncture for labor pain.

Objective: To evaluate the reporting quality of randomized controlled trials (RCTs) of acupuncture for labor pain, and to explore relevant factors for facilitating reporting transparency and integrity for future RCTs. Method: Eight Chinese and English databases were systematically searched from their inception until August 31, 2021. General characteristics and methodological quality of the included reports were evaluated based on the CONSORT statement and the STRICTA guidelines. Descriptive statistical analysis was performed. Cohen's κ-statistics were calculated to assess the agreement of all items between two reviewers. Results: A total of 84 RCTs were included. Based on the CONSORT statement, a positive reporting rate (greater than 80%) was evident for the items "trial design" "participants" "intervention" "outcomes" "numbers analyzed" and "generalizability". The quality of reporting for the items "randomized in the title or abstract" "sample size" "allocation concealment" "implementation" "blinding" "recruitment" "ancillary analyses" "harms" "interpretation" "registration" and "protocol" was poor with positive rates less than 10%. Based on the STRICTA guidelines, the items "extent to which treatment varied" "number of needle insertions per subject per session" and "control or comparator interventions" had poor reporting quality with positive rates of less than 10%. Substantial agreement was observed for most items and excellent agreement for some items. Conclusion: The reporting quality of RCTs of acupuncture for labor pain is suboptimal generally. Rigorous adherence to the CONSORT statement and the STRICTA guidelines should be emphasized in future studies to improve the quality of acupuncture RCT reports.

Multi-scale fusion for RGB-D indoor semantic segmentation.

In computer vision, convolution and pooling operations tend to lose high-frequency information, and the contour details will also disappear with the deepening of the network, especially in image semantic segmentation. For RGB-D image semantic segmentation, all the effective information of RGB and depth image can not be used effectively, while the form of wavelet transform can retain the low and high frequency information of the original image perfectly. In order to solve the information losing problems, we proposed an RGB-D indoor semantic segmentation network based on multi-scale fusion: designed a wavelet transform fusion module to retain contour details, a nonsubsampled contourlet transform to replace the pooling operation, and a multiple pyramid module to aggregate multi-scale information and context global information. The proposed method can retain the characteristics of multi-scale information with the help of wavelet transform, and make full use of the complementarity of high and low frequency information. As the depth of the convolutional neural network increases without losing the multi-frequency characteristics, the segmentation accuracy of image edge contour details is also improved. We evaluated our proposed efficient method on commonly used indoor datasets NYUv2 and SUNRGB-D, and the results showed that we achieved state-of-the-art performance and real-time inference.

Association of Lesion Location and Fatigue Symptoms After Ischemic Stroke: A VLSM Study.

Background: Poststroke fatigue (PSF) is a common symptom in stroke survivors, yet its anatomical mechanism is unclear. Our study was aimed to identify which brain lesions are related to the PSF in patients with acute stroke. Method: Patients with first-ever acute ischemic stroke consecutively admitted from the first affiliated hospital of the University of Science and Technology of China (USTC) between January 2017 and June 2020. Fatigue was scored using the Fatigue Severity Scale. All the participants were assessed by 3.0 T brain MRI including diffusion-weighted imaging. The infarct lesions were delineated manually and transformed into a standard template. Voxel-based lesion-symptom mapping (VLSM) was applied to investigate the association between lesion location and the occurrence and severity of fatigue. The same analyses were carried out by flipping the left-sided lesions. Multivariate logistic regressions were applied to verify the associations. Results: Of the 361 patients with acute stroke, 142 (39.3%) patients were diagnosed with fatigue in the acute phase and 116 (35.8%) at 6 months after the index stroke. VLSM analysis indicated clusters in the right thalamus which was significantly associated with the occurrence and severity of PSF at 6-month follow-up. In contrast, no significant cluster was found in the acute phase of stroke. The flipped analysis did not alter the results. Multivariate logistic regression verified that lesion load in the right thalamus (OR 2.67, 95% CI 1.46-4.88) was an independent predictor of 6-month PSF. Conclusion: Our findings indicated that lesions in the right thalamus increased the risk of fatigue symptoms 6 months poststroke.

Thyroid Function Affects the Risk of Post-stroke Depression in Patients With Acute Lacunar Stroke.

Objective: This study aimed to investigate whether thyroid function profiles are associated with post-stroke depression (PSD) and evaluate the mediation effect of cerebral small vessel disease (cSVD) on the association of thyroid function profiles and PSD in patients with acute ischemic lacunar stroke. Methods: In this study, 372 patients with confirmed acute ischemic lacunar stroke within 3 days of onset were consecutively recruited. Serum levels of thyroid hormones and thyroid antibodies were detected on admission. Lacunar infarcts, white matter lesions, cerebral microbleeds, and enlarged perivascular spaces were rated using validated scales. The severity of depression was scored with the 24-item Hamilton Depression Scale in the hospital after a week of stroke onset. Multivariate regression was utilized to analyze the association of thyroid function profiles and PSD. Mediation analysis was employed to evaluate the effect of cSVD on the association of thyroid function profiles and PSD. Results: A total of 87 (23.4%) participants were diagnosed with depression after stroke. Serum thyroid-stimulating hormone (TSH) levels were significantly higher in patients with PSD than in those without PSD, while free triiodothyronine (FT3) and free thyroxine (FT4) were not significantly different between the two groups. After adjusting for potential confounders, serum TSH levels were positively associated with the risk of PSD (OR = 1.228; 95% CI: 1.053-1.431, p = 0.009). A similar association was also found between the total cSVD burden score and PSD (OR = 2.137; 95% CI: 1.634-2.793, p < 0.001). Further mediation analysis indicated that 26.37% of the association between TSH and PSD was mediated by cSVD. Conclusions: Serum TSH levels on admission can probably predict depression after acute ischemic lacunar stroke.

Stressful life events can predict post-stroke fatigue in patients with ischemic stroke.

OBJECTIVE: To investigate whether stressful life events (SLEs) can predict post-stroke fatigue (PSF) in patients with acute ischemic stroke (AIS). METHODS: This prospective cohort study included data from patients with AIS who were followed up to 2-year interview. PSF was assessed at admission and at 6 (n = 916), 12 (n = 880), and 24 (n = 857) months with the fatigue severity scale (FSS). SLEs were measured with the Social Readjustment Rating Scale questionnaire at 6, 12 and 24 months' interview. RESULTS: A significant dose-response association was found between SLEs and FSS score across all examined time-points: compared with those did not experience SLEs, FSS score was higher for those experiencing SLEs ≥3 at 6 months (ß 0.53, 95% CI 0.28-0.78), 12 months (ß 0.54, 95% CI 0.30-0.78) and 24 months (ß 0.48, 95% CI 0.29-0.68). Longitudinal analyses indicated a significantly positive relationship between the number of SLEs and FSS score (SLEs: ≥3 vs. 0, ß 0.14, 95% CI 0.09-0.19). Moreover, a distinct interaction of follow-up time and SLE numbers on FSS score was observed (p < 0.05), which means elevated exposure to SLEs during follow-up was associated with a lower rate of fatigue decline. A similar association was found in SLE load analysis. CONCLUSION: Patients with severe fatigue were more likely to report increased number of SLEs in the previous 6 months, which could suggest that a non-specific stressful event leads to an extra burden to an already vulnerable psychological system.

Effect of the Early Administration of Selective Serotonin Reuptake Inhibitors on the Time Course of Poststroke Fatigue: A 2-Year Longitudinal Study.

BACKGROUND: Poststroke fatigue (PSF) is a dynamic process over time. Current evidence for interventions for PSF is limited. Our study investigated the effects of selective serotonin reuptake inhibitors (SSRIs) on the time course of PSF. METHODS: The patients with acute ischaemic stroke were enrolled in this study. All patients were scored with the Fatigue Severity Scale (FSS) at baseline and 6, 12, and 24 months after the index stroke. The time course of PSF was classified as non-PSF, incident PSF, recovered PSF and persistent PSF according to the PSF status at baseline and the 24-month follow-up. Multivariate logistic regression analysis and generalized mixed model were applied to explore the relationships between SSRIs and the time course of PSF. RESULTS: Eight hundred forty-seven patients were analyzed in this study. No significant association between SSRIs and the time course of PSF was observed in all patients (p = 0.076). The subgroup analyses indicated that SSRI antidepressants reduced the risk of incident PSF [Odds Ratio (OR) 0.23; 95% confidence interval (CI) 0.05-0.96, p = 0.044] and persistent PSF (OR 0.28; 95% CI 0.09-0.90, p = 0.033) in the PSD subgroup, but not in the non-PSD group. In the longitudinal analysis, generalized logistical mixed-effect models indicated that early administration of SSRIs reduced the risk (OR 0.955; 95% CI 0.928-0.984, p = 0.002) and severity (ß = -0.018, p = 0.041) of PSF in the PSD subgroup. CONCLUSION: No relationship was identified between the use of SSRIs and the time course of PSF in all patients. However, these drugs might be effective in PSF patients with depression.

Study on the Release Potential of BPA and Steroid Estrogens in the Sediments of Erhai Lake, a Typical Plateau Lake of China.

On-site sampling analysis and laboratory-scale experiments were conducted to study the pollution status and release potential of EDCs in Erhai Lake. We found that nitrogen and phosphorus pollution in Erhai Lake sediment were both at a high level, as well as EDCs pollution. The concentrations of BPA, E2α, E1, E2ß, EE2, and E3 were 36.84 ng/g(DW), 13.04 ng/g(DW), 128.97 ng/g(DW), 52.57 ng/g(DW), 18.48 ng/g(DW) and 5.36 ng/g(DW), respectively. The concentrations of E2α, E1, E2ß and EE2 in the bottom water were higher than the surface water due to the impact of sediment release. The results of the 20 days release test indicated that BPA release from the sediment had a greater correlation with the original concentration and the particle size of sediment, while the steroid EDCs had no obvious correlation with these two factors, probably due to the difference in hydrophobicity between them. Under hydraulic disturbance and aerobic conditions, the release process of EDCs was accompanied by a large amount of microbial degradation, and degradation amount > released amount. BPA was released quickly, 9.56% was released in 20 days, but only 3.37% of steroid EDCs released. In comparison, the release process of steroids was longer and posed a greater threat to aquatic ecology.

Structure and function of the contactin-associated protein family in myelinated axons and their relationship with nerve diseases.

The contactin-associated protein (Caspr) family participates in nerve excitation and conduction, and neurotransmitter release in myelinated axons. We analyzed the structures and functions of the Caspr family-CNTNAP1 (Caspr1), CNTNAP2 (Caspr2), CNTNAP3 (Caspr3), CNTNAP4 (Caspr4) and CNTNAP5 (Caspr5), Caspr1-5 is not only involved in the formation of myelinated axons, but also participates in maintaining the stability of adjacent connections. Caspr1 participates in the formation, differentiation, and proliferation of neurons and astrocytes, and in motor control and cognitive function. We also analyzed the relationship between the Caspr family and neurodegenerative diseases, multiple sclerosis, and autoimmune encephalitis. However, the effects of Caspr on disease course and prognosis remain poorly understood. The effects of Caspr on disease diagnosis and treatment need further investigation.

The Cross-talk between Tristetraprolin and Cytokines in Cancer.

Cytokines are small secreted proteins serving as vital mediators that mediate the host immune responses. Transcription and post-transcription play a critical role in cytokine expression through the regulation of message RNA (mRNA) cytoplasmic localization, translation initiation and decay. Researches have been conducted to reveal regulatory mechanisms of cytokines production in cells involved in cancer. AU-rich element (ARE) can regulate the degradation and translation of mRNA by connecting with specific ARE binding proteins. It is now clear that tristetraprolin (TTP), as the most common ARE binding protein, negatively regulates many aspects of the cytokines through binding to the AREs in the 3'-untranslated region (3'UTR) of mRNA. Furthermore, some certain cytokines have an impact on TTP expression and function. Therefore, the cross-regulation between cytokines and TTP has come into sight. The complicated regulatory networks between cytokines and TTP are closely related to tumorigenesis. In this review, we summarize specific regulatory mechanisms of cytokine mRNAs. We focus on how TTP negatively regulates inflammatory and oncogenic cytokines expression after combining with AREs, we also pay attention to some cytokines mediating the expression of TTP and their cross-talk in various cancers in detail.