Patient-reported outcomes of taste alterations and quality of life in patients with nasopharyngeal carcinoma.

Objective: To investigate patient-reported outcomes of taste alterations and quality of life (QoL) in patients with nasopharyngeal carcinoma (NPC). Methods: In this single-center retrospective study medical records of 191 patients with NPC undergoing chemoradiotherapy (CRT) in the Department of Radiotherapy, Jiangsu Cancer Hospital, the Affiliated Cancer Hospital of Nanjing Medical University from January 2021 to December 2021 were reviewed. A total of 120 patients met eligibility criteria and were included. The taste alterations and QoL at multiple time points during radiotherapy (RT) were compared. Results: There were significant differences in the intensity of taste, discomfort, phantogeusia and parageusia or overall taste alterations at multiple time points during CRT (p-Value<0.001). These four parameters were significantly higher two or four weeks after CRT, or at the end of CRT compared to before CRT (p-Value <0.001). The intensity of taste, discomfort, phantogeusia and parageusia or overall taste alterations were all significantly higher four weeks after CRT compared to two weeks after CRT (p-Value <0.001), and at the end of CRT compared to four weeks after CRT (p-Value <0.001). The chemotherapy-induced taste alteration scale (CiTAS) scores were highest at the end of CRT (p-Value <0.001). There were significant differences in QoL at multiple time points during CRT (p-Value <0.001), and each parameter differed significantly at various time points (p <0.05). The QoL of all areas at the end of CRT were significantly higher than those before CRT, or two or four weeks after CRT (p-Value <0.001). Conclusions: In patients with NPC undergoing CRT, taste alterations increasingly worsen as treatment progresses, with poor QoL outcomes.

Prediction of Return of Spontaneous Circulation in a Pediatric Swine Model of Cardiac Arrest Using Low-Resolution Multimodal Physiological Waveforms.

Monitoring physiological waveforms, specifically hemodynamic variables (e.g., blood pressure waveforms) and end-tidal CO2 (EtCO2), during pediatric cardiopulmonary resuscitation (CPR) has been demonstrated to improve survival rates and outcomes when compared to standard depth-guided CPR. However, waveform guidance has largely been based on thresholds for single parameters and therefore does not leverage all the information contained in multimodal data. We hypothesize that the combination of multimodal physiological features improves the prediction of the return of spontaneous circulation (ROSC), the clinical indicator of short-term CPR success. We used machine learning algorithms to evaluate features extracted from eight low-resolution (4 samples per minute) physiological waveforms to predict ROSC. The waveforms were acquired from the 2nd to 10th minute of CPR in pediatric swine models of cardiac arrest (N = 89, 8-12 kg). The waveforms were divided into segments with increasing length (both forward and backward) for feature extraction, and machine learning algorithms were trained for ROSC prediction. For the full CPR period (2nd to 10th minute), the area under the receiver operating characteristics curve (AUC) was 0.93 (95% CI: 0.87-0.99) for the multivariate model, 0.70 (0.55-0.85) for EtCO2 and 0.80 (0.67-0.93) for coronary perfusion pressure. The best prediction performances were achieved when the period from the 6th to the 10th minute was included. Poor predictions were observed for some individual waveforms, e.g., right atrial pressure. In conclusion, multimodal waveform features carry relevant information for ROSC prediction. Using multimodal waveform features in CPR guidance has the potential to improve resuscitation success and reduce mortality.

A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of Neurological Injury.

Background: Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets. Methods: Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download. Results: Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome. Conclusions: The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.

Extracting social determinants of health events with transformer-based multitask, multilabel named entity recognition.

OBJECTIVE: Social determinants of health (SDOH) are nonclinical, socioeconomic conditions that influence patient health and quality of life. Identifying SDOH may help clinicians target interventions. However, SDOH are more frequently available in narrative notes compared to structured electronic health records. The 2022 n2c2 Track 2 competition released clinical notes annotated for SDOH to promote development of NLP systems for extracting SDOH. We developed a system addressing 3 limitations in state-of-the-art SDOH extraction: the inability to identify multiple SDOH events of the same type per sentence, overlapping SDOH attributes within text spans, and SDOH spanning multiple sentences. MATERIALS AND METHODS: We developed and evaluated a 2-stage architecture. In stage 1, we trained a BioClinical-BERT-based named entity recognition system to extract SDOH event triggers, that is, text spans indicating substance use, employment, or living status. In stage 2, we trained a multitask, multilabel NER to extract arguments (eg, alcohol "type") for events extracted in stage 1. Evaluation was performed across 3 subtasks differing by provenance of training and validation data using precision, recall, and F1 scores. RESULTS: When trained and validated on data from the same site, we achieved 0.87 precision, 0.89 recall, and 0.88 F1. Across all subtasks, we ranked between second and fourth place in the competition and always within 0.02 F1 from first. CONCLUSIONS: Our 2-stage, deep-learning-based NLP system effectively extracted SDOH events from clinical notes. This was achieved with a novel classification framework that leveraged simpler architectures compared to state-of-the-art systems. Improved SDOH extraction may help clinicians improve health outcomes.

Oncology nurses' and oncologists' experience of addressing sexual health concerns in breast cancer patients: A qualitative study.

PURPOSE: This study aimed to explore the experiences of Chinese oncology nurses and oncologists who provide sexual health education for breast cancer patients in their practical work. METHODS: This was a qualitative study using semistructured face-to-face interviews. Eleven nurses and eight oncologists who provided sexual health education to breast cancer patients were purposively recruited from eight hospitals in seven provinces of China. Data were analyzed using the thematic analysis method. RESULTS: Four main themes emerged: the surface of sexual health, stress and benefit finding, cultural sensitivity and communication, needs and changes. Both oncology nurses and oncologists found it difficult to solve sexual health problems, which were beyond their responsibilities and competencies. They felt helpless about the limitations of external support. Nurses hoped oncologists could participate in more sexual health education. CONCLUSIONS: Oncology nurses and oncologists experienced great challenges in educating breast cancer patients about sexual health. They are eager to obtain more formal education and learning resources for sexual health education. Specific training to improve the sexual health education competence of healthcare professionals is needed. Furthermore, more support is needed to create conditions to encourage patients to reveal their sexual challenges. It is necessary for oncology nurses and oncologists to communicate on sexual health in breast cancer patients, and to promote interdisciplinary communication and share responsibility.

Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.

OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. MATERIALS: This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014-2018 at a quaternary children's hospital. METHODS: We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians. RESULTS: The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956). CONCLUSIONS: Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.

Analysis of risky driving behaviors among bus drivers in China: The role of enterprise management, external environment and attitudes towards traffic safety.

Several studies have shown that enterprise management (e.g. culture, salary) and external environment (e.g. traffic congestion) predict risky driving behaviors and accident involvement. However, this process has not been systematically investigated in bus drivers. The present study uses structural equation model to assess whether enterprise management and external environment are associated with risky self-reported driving behaviors both directly and indirectly, through the effects of attitudes towards traffic safety in a large sample of bus drivers. Three hundred and thirty-one bus drivers (mean age = 39.5, SD = 5.6 years) completed a structured and anonymous questionnaire measuring enterprise management, external environment, attitudes toward traffic safety, and self-reported risky driving behaviors (i.e., speeding, fatigue driving, running the light) in the last 6 months. Structural equation modeling analysis revealed that enterprise management, and external environment were associated with risky driving behaviors both directly and indirectly. In particular both of them were directly correlated with bus drivers' attitudes toward traffic safety which, in turn, were related to the five types of self-reported risky driving behaviors. The present findings suggest that measures related to the impact factors could be carried out to reduce the probabilities of the risky driving behaviors among bus drivers, such as improving the salary level of bus drivers, setting up bus lanes and priority signals to alleviate road congestion, optimizing shift schedules, implementing effective safety education, etc. These findings can provide the empirical basis for evidence-based road safety interventions in the context of public transport.

Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.

OBJECTIVE: Social determinants of health (SDOH) are non-medical factors that can profoundly impact patient health outcomes. However, SDOH are rarely available in structured electronic health record (EHR) data such as diagnosis codes, and more commonly found in unstructured narrative clinical notes. Hence, identifying social context from unstructured EHR data has become increasingly important. Yet, previous work on using natural language processing to automate extraction of SDOH from text (a) usually focuses on an ad hoc selection of SDOH, and (b) does not use the latest advances in deep learning. Our objective was to advance automatic extraction of SDOH from clinical text by (a) systematically creating a set of SDOH based on standard biomedical and psychiatric ontologies, and (b) training state-of-the-art deep neural networks to extract mentions of these SDOH from clinical notes. DESIGN: A retrospective cohort study. SETTING AND PARTICIPANTS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. The corpus comprised 3,504 social related sentences from 2,670 clinical notes. METHODS: We developed a framework for automated classification of multiple SDOH categories. Our dataset comprised narrative clinical notes under the "Social Work" category in the MIMIC-III Clinical Database. Using standard terminologies, SNOMED-CT and DSM-IV, we systematically curated a set of 13 SDOH categories and created annotation guidelines for these. After manually annotating the 3,504 sentences, we developed and tested three deep neural network (DNN) architectures - convolutional neural network (CNN), long short-term memory (LSTM) network, and the Bidirectional Encoder Representations from Transformers (BERT) - for automated detection of eight SDOH categories. We also compared these DNNs to three baselines models: (1) cTAKES, as well as (2) L2-regularized logistic regression and (3) random forests on bags-of-words. Model evaluation metrics included micro- and macro- F1, and area under the receiver operating characteristic curve (AUC). RESULTS: All three DNN models accurately classified all SDOH categories (minimum micro-F1 = 0.632, minimum macro-AUC = 0.854). Compared to the CNN and LSTM, BERT performed best in most key metrics (micro-F1 = 0.690, macro-AUC = 0.907). The BERT model most effectively identified the "occupational" category (F1 = 0.774, AUC = 0.965) and least effectively identified the "non-SDOH" category (F = 0.491, AUC = 0.788). BERT outperformed cTAKES in distinguishing social vs non-social sentences (BERT F1 = 0.87 vs. cTAKES F1 = 0.06), and outperformed logistic regression (micro-F1 = 0.649, macro-AUC = 0.696) and random forest (micro-F1 = 0.502, macro-AUC = 0.523) trained on bag-of-words. CONCLUSIONS: Our study framework with DNN models demonstrated improved performance for efficiently identifying a systematic range of SDOH categories from clinical notes in the EHR. Improved identification of patient SDOH may further improve healthcare outcomes.

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records.

OBJECTIVES: To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data. MATERIALS AND METHODS: In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease <6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients. RESULTS: At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve. CONCLUSIONS: I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.

The relationship between occupational stress, job burnout and quality of life among surgical nurses in Xinjiang, China.

BACKGROUND: Nursing is a high-risk occupation that involves exposure to stress. The physical and mental health of nurses is directly related to the quality of medical services, so the quality of life of nurses cannot be ignored. This study is a Chinese nursing study that investigated occupational stress, job burnout, and quality of life of surgical nurses in Xinjiang, China. METHODS: This study employed the cluster random sampling method and carried out a questionnaire survey among 488 surgical nurses from five hospitals from May 2019 to September 2019. The study analyzed the relationship between occupational stress, job burnout and quality of life. The Effort-Reward Imbalance questionnaire (ERI), Maslach Burnout Inventory General Survey (MBI-GS) and the 36-item Short Form Health Survey (SF-36) were used to evaluate occupational stress, job burnout and quality of life among surgical nurses. RESULTS: A total of 550 questionnaires were distributed in this study, and 488 were retrieved, with an effective recovery rate of 88.73 %. The results revealed that the quality of life score among surgical nurses was not high, and differences were observed in the quality of life score of patients according to gender, age, title, and frequency of night shifts (P < 0.05). There was a positive correlation between occupational stress and job burnout. Higher levels of occupational stress and job burnout were associated with a poorer quality of life score. Occupational stress and job burnout were identified as risk factors for quality of life, and the interaction between high levels of stress and burnout seriously reduced quality of life. The structural equation model revealed that occupational stress and job burnout had a direct impact on quality of life, occupational stress had a direct impact on job burnout, and job burnout was identified as a mediating factor in the relationship between occupational stress and quality of life. CONCLUSIONS: Surgical nurses have a high level of occupational stress and burnout, and low quality of life score. Quality of life is correlated with occupational stress and job burnout. According to the individual characteristics and psychological state of nurses, managers can implement personalized intervention measures promptly and effectively to relieve their tension and burnout, and improve the quality of life of surgical nurses.

Lycorine inhibits cell proliferation and induced oxidative stress-mediated apoptosis via regulation of the JAK/STAT3 signaling pathway in HT-3 cells.

Human cervical cancer is the fourth most common carcinoma in women in the world. The JAK/STAT3 signaling pathways crucially regulate cell growth and apoptosis. It is a significant target signaling pathway for the development of novel antitumor medicine. This study intended to explore whether lycorine could prevent HT-3 proliferation and induce apoptosis by targeting the JAK/STAT3 signaling cascade. The HT-3 cells were treated with various lycorine dosages and we analyzed cell growth, lipid peroxidation, antioxidants, mitochondrial membrane potential (ΔΨm), DNA damage, apoptosis markers by different in vitro methodologies. Our results revealed that lycorine substantially reserved cell growth via decreased antioxidants, augmented reactive oxygen species (ROS) generation which leads to loss of ΔΨm, increased nuclear crumbling and chromatin condensation, thus resulting in representative increased apoptotic cell death. Furthermore, we analyzed that the molecular mechanical action of lycorine considerably repressed JAK1/STAT3 transactional activation and decrease its downstream molecules Bcl-2, and enhances the expressional activity of Bax, cytochrome c, caspase 3 and 9 in HT-3 cells. Finally, the fact that N-acetylcysteine inhibits lycorine-induced ROS-mediated apoptosis was confirmed in HT-3 cells. Thus, the results indicate that lycorine efficiently enhances apoptosis and inhibits HT-3 cell proliferation. These outcomes collectively proposed that lycorine could be a beneficial chemotherapeutic agent for treating and managing human cervical carcinoma.

The application value of operating room ventilation with laminar airflow for surgical site infection: A protocol for a systematic review and meta-analysis.

BACKGROUND: The presence of biological particles in the air inside operating theatres has the potential to cause severe surgical site infections. Recently, laminar airflow systems have been regarded as a means to reducing surgical site infections using airborne microbes. Still, other publications have argued the benefits of laminar airflow systems, stating the likelihood of adverse effects. Therefore, we will conduct this systematic study to evaluate the applicational value of adopting laminar airflow systems in operating theatres to minimize surgical site infections. METHODS: Reporting of this study adheres to the guidelines of Preferred Reporting Items for Systematic Review and Meta-analysis Protocols. The authors will perform a systematic search on MEDLINE, Web of Science, EMBASE, the China national knowledge infrastructure, and the Cochrane Library from their commencement until June 2021. The search will identify relevant randomized and non-randomized controlled trials that evaluates the applicational value of using laminar airflow ventilation in surgical theatres to minimize surgical site infections. There are no restrictions on language. Two authors will independently screen the identified studies, perform data extraction, and use an appropriate method to evaluate the bias risk in the included studies. RESULTS: The work done in the present study will enhance the existing literature on the applicational value of laminar airflow ventilation in surgical theatre to reduce surgical site infections. CONCLUSION: The outcomes are a reference for healthcare practitioners and patients when making informed decisions regarding care during surgeries.

Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.

OBJECTIVE: Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. METHODS: This case-control study included patients aged 10-75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). RESULTS: The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P < .001). The best-predicting model utilized 1726 variables with AUC = 0.932 (95% CI, 0.922-0.941). The model was robust across multiple prediction windows and subgroups by demographics, points of historical most recent clinical contact, and depression diagnosis history. CONCLUSIONS: Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.

Correlation Between High Expression of FOXA2 and Improved Overall Survival in Ovarian Cancer Patients.

BACKGROUND The aim of the present work was to evaluate FOXA2 expression in ovarian cancer and to use integrated bioinformatics analysis to correlate it with patient prognosis. MATERIAL AND METHODS FOXA2 expression was evaluated in multiple cancers in The Cancer Genome Atlas database. A protein-protein interaction (PPI) network relevant to FOXA2 was constructed using the Search Tool for Retrieval of Interacting Genes/Proteins (STRIN). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed of FOXA2 and relevant genes. Correlations between overall survival (OS), disease-free survival, and FOXA2 expression were evaluated. An immunohistochemical assay (IHC) was used to test for FOXA2 protein expression in 79 ovarian cancer specimens. RESULTS FOXA2 mRNA was upregulated in colorectal, stomach, liver, and endometrial cancers. In the PPI network, 21 protein nodes and 533 edges were constructed with a local clustering coefficient of 0.698, which indicated significant PPI enrichment (P<0.01). FOXA2 and relevant genes were mainly enriched in the signaling pathways regulating pluripotency of stem cells, cancer, and AMPK. A survival analysis indicated that OS was significantly longer in patients with higher versus lower FOXA2 protein expression (HR=0.73, P<0.01). The IHC assay showed that the FOXA2 protein was mainly positively expressed in the nucleoplasm of tumor cells with brown-yellow staining. Of the 79 ovarian cancer samples, 31 (39.2%) highly expressed FOXA2. The FOXA2 gene was correlated with International Federation of Gynecology and Obstetrics staging and with lymph node metastasis (both P<0.05). CONCLUSIONS Upregulation of the FOXA2 gene was correlated with improved OS in patients with ovarian cancer and it can be used as a prognostic biomarker and potential treatment target.

Relationship between Mental Health, the CLOCK Gene, and Sleep Quality in Surgical Nurses: A Cross-Sectional Study.

Nursing is a high-risk occupation with high exposure to stress. The physical and mental health of nurses is directly related to the quality of medical services. Therefore, the sleep quality of nurses should not be ignored. In this study, the method of cluster random sampling was adopted from May to September 2019, and a questionnaire survey was conducted among 521 surgical nurses from five affiliated hospitals of Xinjiang Medical University. The relationship between mental health and sleep quality was analyzed, and 20% of the participants with sleep disorders were randomly selected. The sleep disorders used 1 : 1 matching, finally providing a sample with 60 cases and 60 controls for measurement of the CLOCK gene (rs1801260, rs6850524), to analyze the effect of the interaction between mental health and the CLOCK gene on sleep. The mental health and sleep quality of the surgical nurses were evaluated using the Symptom Checklist 90 (SCL-90) and Pittsburgh Sleep Quality Index (PSQI). The study found that surgical nurses had poor sleep, and there were differences associated with age, years working, frequency of night shifts, and incidence of sleep disorders under marital status (p < 0.05). The PSQI scores of the positive psychological symptoms were higher than those of the negative psychological symptoms. The rank sum test was used to compare the sleep quality scores of different genotypes in CLOCK rs1801260 and rs6850524; the results indicated that the PSQI scores were different among different genotypes at the rs1801260 and rs6850524 loci. The logistic regression results suggested that CLOCK gene rs1801260 (TC) and positive psychological symptoms were influential factors for sleep disorders, and the interaction of positive psychological symptoms∗rs1801260 (TT) was a risk factor for sleep disorders (OR = 10.833, 95% CI: 2.987-39.288). The sleep quality of nurses is not only affected by demographic characteristics but also affected by mental health status and the CLOCK gene.

Effects of Occupational Stress and Circadian CLOCK Gene Polymorphism on Sleep Quality of Oil Workers in Xinjiang, China.

BACKGROUND This study investigated the effect of occupational stress and circadian clock gene polymorphism on sleep disorder of oil workers in Xinjiang, China. MATERIAL AND METHODS We enrolled 2300 Xinjiang oil workers who had been working for at least 1 year. The Chinese revised version of the Occupational Stress Questionnaire (OSI-R), the Pittsburgh Sleep Quality Index (PSQI), and General Survey Questionnaire were used. A total of 308 subjects were selected for stress hormone measurements and gene polymorphism analysis of the circadian clock genes CLOCK, PER2, and PER3. RESULTS The occupational stress scores were influenced by sex, smoking, marital status, age, and work type. Different work shift groups and different professional title groups had statistically significant sleep disorder incidences (P<0.05). The middle and high occupational stress groups had significantly higher subjective sleep quality, total PSQI scores, daytime dysfunction factor scores, and sleep disorder than in the low occupational stress group (P<0.05). CLOCK gene rs1801260 locus carrying TC genotype (OR=0.412, 95% CI=0.245-0.695), and CLOCK gene rs6850524 locus carrying GC and CC genotypes decreased sleep disorder risk (OR1=0.357, 95% CI1=0.245-0.695; OR2=0.317, 95% CI2=0.128-0.785). The main factors affecting the sleep quality of oil workers were length of service, individual strain capacity, glucocorticoid levels, Per3 gene, and the rs6850524 loci of CLOCK gene. CONCLUSIONS Occupational stress has an adverse effect on the sleep quality of workers. CLOCK gene and Per3 gene may increase risk of sleep disorders.

Research progress on solutions to the sneak path issue in memristor crossbar arrays.

Since the emergence of memristors (or memristive devices), how to integrate them into arrays has been widely investigated. After years of research, memristor crossbar arrays have been proposed and realized with potential applications in nonvolatile memory, logic and neuromorphic computing systems. Despite the promising prospects of memristor crossbar arrays, one of the main obstacles for their development is the so-called sneak-path current causing cross-talk interference between adjacent memory cells and thus may result in misinterpretation which greatly influences the operation of memristor crossbar arrays. Solving the sneak-path current issue, the power consumption of the array will immensely decrease, and the reliability and stability will simultaneously increase. In order to suppress the sneak-path current, various solutions have been provided. So far, some reviews have considered some of these solutions and established a sophisticated classification, including 1D1M, 1T1M, 1S1M (D: diode, M: memristor, T: transistor, S: selector), self-selective and self-rectifying memristors. Recently, a mass of studies have been additionally reported. This review thus attempts to provide a survey on these new findings, by highlighting the latest research progress realized for relieving the sneak-path issue. Here, we first present the concept of the sneak-path current issue and solutions proposed to solve it. Consequently, we select some typical and promising devices, and present their structures and properties in detail. Then, the latest research activities focusing on single-device structures are introduced taking into account the mechanisms underlying these devices. Finally, we summarize the properties and perspectives of these solutions.