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Differences in insomnia-related self-reported outcomes among elderly hospitalized patients

World Journal of Psychiatry 2024年第5期14卷 686-694页

作者： xia Ding Ling-xia Qi Dong-yun sun Department of Gangzha General Medical Center The Affiliated Hospital of Nantong UniversityNantong 226000Jiangsu ProvinceChina Department of Anesthesiology and Surgery The Affiliated Hospital of Nantong UniversityNantong 226000Jiangsu ProvinceChina Department of Ophthalmic Surgery Room The Affiliated Hospital of Nantong UniversityNantong 226000Jiangsu ProvinceChina

Insomnia is among the most common sleep disorders worldwide.Insomnia in older adults is a social and public health problem.Insomnia affects the physical and mental health of elderly hospitalized patients and can aggravate or induce physical illnesses.Understanding subjective feelings and providing reasonable and standardized care for elderly hospitalized patients with insomnia are urgent issues.AIM To explore the differences in self-reported outcomes associated with insomnia among elderly hospitalized patients.METHODS One hundred patients admitted to the geriatric unit of our hospital between June 2021 and December 2021 were included in this study.Self-reported symptoms were assessed using the Athens Insomnia Scale(AIS),Generalized Anxiety Disorder Scale-7(GAD-7),Geriatric Depression Scale-15(GDS-15),Memorial University of Newfoundland Scale of Happiness(MUNSH),Barthel Index Evaluation(BI),Morse Fall Scale(MFS),Mini-Mental State Examination,and the Short Form 36 Health Survey Questionnaire(SF-36).Correlation coefficients were used to analyze the correlation between sleep quality and self-reported symptoms.Effects of insomnia was analyzed using Logistic regression analysis.RESULTS Nineteen patients with AIS≥6 were included in the insomnia group,and the incidence of insomnia was 19%(19/100).The remaining 81 patients were assigned to the non-insomnia group.There were significant differences between the two groups in the GDA-7,GDS-15,MUNSH,BI,MFS,and SF-36 items(P<0.05).Patients in the insomnia group were more likely to experience anxiety,depression,and other mental illnesses,as well as difficulties with everyday tasks and a greater risk of falling(P<0.05).Subjective well-being and quality of life were poorer in the insomnia group than in the control group.The AIS scores positively correlated with the GAD-7,GDS-15,and MFS scores in elderly hospitalized patients with insomnia(P<0.05).Logistic regression analysis showed that GDS-15≥5 was an independent risk factor for insomnia in elderly hospitalized patients(P<0.05).C

关键词： Elderly hospitalized patients Insomnia Self-reported outcomes Symptoms Subjective feelings Correlation

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Pretreating and normalizing metabolomics data for statistical analysis

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Genes & Diseases 2024年第3期11卷 188-205页

作者： Jun sun Yinglin xia Division of Gastroenterology and Hepatology Department of MedicineDepartment of Microbiology/ImmunologyUIC Cancer CenterUniversity of Illinois ChicagoJesse Brown VA Medical Center Chicago(537)ChicagoIL 60612USA Division of Gastroenterology and Hepatology Department of MedicineUniversity of Illinois ChicagoChicagoIL 60612USA

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples.Metabolomics is emerging as a powerful tool generally for pre-cision medicine.Particularly,integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease.However,metabo-lomics data are very complicated.Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis.In this review article,we comprehensively review various methods that are used to preprocess and pretreat metabolo-mics data,including MS-based data and NMR-based data preprocessing,dealing with zero and/or missing values and detecting outliers,data normalization,data centering and scaling,data transformation.We discuss the advantages and limitations of each method.The choice for a suitable preprocessing method is determined by the biological hypothesis,the characteristics of the data set,and the selected statistical data analysis method.We then provide the perspective of their applications in the microbiome and metabolome research.

关键词： Data centering and scaling Data normalization Data transformation Missing values MS-Baseddata preprocessing NMRData preprocessing Outliers Preprocessing/pretreatment

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X-ray-activated Bi^(3+)/Pr^(3+)co-doped LiYGeO_4 phosphor with UV and NIR dual-emissive persistent luminescence

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Journal of Rare Earths 2024年第5期42卷 955-961,I0005页

作者： xianggui Yin Hongyun Zhong Lin Liu Junpeng Shi xia sun yun Zhang State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou350002China xiamen Key Laboratory of Rare Earth Photoelectric Functional Materials Xiamen Institute of Rare Earth MaterialsHaixi InstituteChinese Academy of SciencesXiamen361021China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108China

X-ray-activated luminescence materials have broad application prospects in photodynamic therapy of deep tissue.Among them,X-ray-activated persistent luminescence materials(PLMs)exhibiting multiple emission peaks have drawn extensive attention for the ir capacity to achieve a combination of bioimaging and therapeutic functions.Here,we developed a novel PLM,LiYGeO_4:Bi^(3+),Pr^(3+),that simultaneously exhibits UV and NIR dual persistent luminescence(PersL)emissions after irradiation by X-ray.The material can be re peatedly excited by X-ray and emits similar lumine scence intensity every time,which shows good PersL stability.In addition,LiYGeO_4:Bi^(3+),Pr^(3+)exhibits photostimulated PersL properties by stimulation with a red light-emitting diode(LED)or NIR laser after long-term decay.This work provides a new choice of X-ray-excited PLMs with UV and NIR dual emission and the novel phosphor shows promise as a potential candidate for the integration of treatment and diagnosis of deep tumors.

关键词： Co-doping X-ray-activated Dual emissions Persistent luminescence Rare earths

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X-ray excited Mn^(2+)-doped persistent luminescence materials with biological window emission for in vivo bioimaging

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Journal of Rare Earths 2024年第1期42卷 28-35,I0001页

作者： Hanrun Zheng Lin Liu Yi’ang Li Rui Rong Liang Song Junpeng Shi Jing Teng xia sun yun Zhang State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou350002China University of the Chinese Academy of Sciences Beijing100049China xiamen Hospital of Traditional Chinese Medicine Xiamen361015China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108China

In recent years,persistent luminescence materials(PLMs)excited by X-rays and emitting in biological windows have received extensive attention in the field of high-sensitivity bioimaging.Transition metal Mn^(2+)is an ideal emission center,but few studies focus on Mn^(2+)-doped PLMs with X-ray excitation and biological window emission.Here,we report a Mn^(2+)-doped PLM,LiYGeO_(4):Mn^(2+)(LYGM),with excellent biological window persistent luminescence emission.After excitation by UV,LYGM produces a durable biological window of persistent luminescence emission at 660 nm for up to 20 h.More importantly.LYGM can be repeatedly excited by X-rays,resulting in long-term biological window persistent luminescence emission.In addition,we obtain LYGM around 200 nm in diameter by ball milling and centrifugation and improve its biocompatibility by surface modification to apply it to in vivo imaging in mice.After LYGM are injected into mice through the tail vein,in situ excitation of X-rays can be achieved.After the persistent luminescence decays,LYGM can be re-excited for repeated imaging.Therefore,LYGM shows potential prospects for in vivo deep tissue and long-term bioimaging.

关键词： Persistent luminescence X-ray Biological window In vivo imaging Rare earths

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Microbiome and intestinal pathophysiology in post-acute sequelae of COVID-19

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Genes & Diseases 2024年第3期11卷 220-231页

作者： Jilei Zhang Yongguo Zhang Yinglin xia Jun sun Division of Gastroenterology and Hepatology Department of MedicineUniversity of Ilinois ChicagoIL60612USA Uic Cancer Center Department of Microbiology and ImmunologyUniversity of Ilinois ChicagoChicagoIL60612USA Jesse Brown VA Medical Center ChicagoIL60612USA

Long CoVID,also known for post-acute sequelae of CovID-19,describes the people who have the signs and symptoms that continue or develop after the acute coviD-19 phase.Long CovID patients suffer from an inflammation or host responses towards the virus approx-imately 4 weeks after initial infection with the SARS CoV-2 virus and continue for an unchar-acterized duration.Anyone infected with CovID-19 before could experience long-CcovID conditions,including the patients who were infected with SARS CoV-2 virus confirmed by tests and those who never knew they had an infection early.People with long CoviD may experience health problems from different types and combinations of symptoms over time,such as fa-tigue,dyspnea,cognitive impairments,and gastrointestinal(Gl)symptoms(e.g.,nausea,vom-iting,diarrhea,decreased or loss of appetite,abdominal pain,and dysgeusia).The critical role of the microbiome in these Gl symptoms and long CovID were reported in clinical patients and experimental models.Here,we provide an overall view of the critical role of the Gl tract and microbiome in the development of long COVID,including the clinical Gl symptoms in patients,dysbiosis,viral-microbiome interactions,barrier function,and inflammatory bowel disease patients with long CovID.We highlight the potential mechanisms and possible treatment based on Gl health and microbiome.Finally,we discuss challenges and future direction in the long CoVID clinic and research.

关键词： Gastrointestinal symptoms Gl tract Gut microbiome Inflammatorybowel diseases Intestinal barrier Long CoVID Metabolites Tight junctions

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Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 261-271页

作者： Jinping sun Qifang Chen Mingchao xia School of Electrical Engineering Beijing Jiaotong University(BJTU)Beijing 100044China

Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

关键词： Distribution systems line parameter detection and identification probability density function sampling algorithm the time skew of PMU and SCADA measurements

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Drug repurposing for cancer therapy

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Signal Transduction and Targeted Therapy 2024年第5期9卷 1981-2013页

作者： Ying xia Ming sun Hai Huang Wei-Lin Jin Center for Clinical Laboratories The Affiliated Hospital of Guizhou Medical UniversityGuiyang 550004PR China The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine Guiyang 550001PR China School of Clinical Laboratory Science Guizhou Medical UniversityGuiyang 550004PR China Division of Gastroenterology and Hepatology Department of Medicine andDepartment of OncologySidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMD 21287USA Institute of Cancer Neuroscience Medical Frontier Innovation Research CenterThe First Hospital of Lanzhou UniversityThe First Clinical Medical College of Lanzhou UniversityLanzhou 730000PR China

Cancer,a complex and multifactorial disease,presents a significant challenge to global health.Despite significant advances in surgical,radiotherapeutic and immunological approaches,which have improved cancer treatment outcomes,drug therapy continues to serve as a key therapeutic strategy.However,the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects,and thus there remains a critical need to develop novel cancer therapeutics.One promising strategy that has received widespread attention in recent years is drug repurposing:the identification of new applications for existing,clinically approved drugs.Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective,proven to be safe,and can significantly expedite the drug development process due to their already established safety profiles.In light of this,the present review offers a comprehensive overview of the various methods employed in drug repurposing,specifically focusing on the repurposing of drugs to treat cancer.We describe the antitumor properties of candidate drugs,and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment.In addition,we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery.We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen.To conclude,we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

关键词： drugs integrating spite

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Path signature-based XAI-enabled network time series classification

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Science China(Information Sciences) 2024年第7期67卷 91-106页

作者： Le sun Yueyuan WANG Yongjun REN Feng xia Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology School of Computing Technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification methods.These include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

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Low expression of fatty acid oxidation related gene ACADM indicates poor prognosis of renal clear cell carcinoma and is related to tumor immune infltration

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Oncology Research 2024年第3期32卷 545-561页

作者： JIECHUAN QIU TIANMIN YANG YANNING sun KAI sun YINGKUN XU QINGHUA xia Department of Urology Shandong Provincial Hospital A fliated to Shandong First Medical UniversityJinan250021China Depatment of Urology Shandong Provincial HospitalCheeloo College of MedicineShandong UniversityJinan250021China Depatment of Breast and Thyroid Surgery The First Afiliated Hospital of Chongqing Medical UniversityChongqing400042China

This research aims to identify the key fatty acid beta-oxidation(FAO)genes that are altered in kidney renal clear cell carcinoma(KIRC)and to analyze the role of these genes in KIRC The Gene Expression Omnibus(GEO)and FAO datasets were used to identify these key genes.Wilcoxon rank sum test was used to assess the levels of acyl-CoA dehydrogenase medium chain(ACADM)between KIRC and non cancer samples.The logistic regression and Wilcoxon rank sum test were used to explore the association between ACADM and clinical features.The diagnostic performance of ACADM for KIRC was asessed using a diagnostic receiver operating ch aracteristic(ROC)curve.The co-expressed genes of ACADM were identifed in LinkedOmics database,and their function and pathway enrichment were analyzed.The correlation between ACADM expression level and immune infitration was analyzed by Gene Set Variation Analysis(GSVA)method Additionally,the proliferation,migration,and invasion abilities of KIRC cells were assessed after overexpressing ACADM.Following differential analysis and intersection,we identifed six hub genes,induding ACADM.We found that the expression level of ACADM was decreased in KIRC tissues and had a better diagnostic efect(AUC=0.916).Survival analysis suggested that patients with decreased ACADM expression had a worse prognosis.According to correlation analysis,a variety of dinical features were associated with the expression level of ACADML By analyzing the infiltration level of immune cells,we found that ACADM may be related to the enrichment of immune cells.Finally,ACADM overexpression inhibited proliferation,migration,and invasion of KIRC cells.In conclusion,our findings suggest that reduced ACADM expression in KIRC patients is indicative of poor prognosis.These results imply that ACADM may be a diagnostic and prognostic marker for individuals with KIRC,offering a reference for dinicians in diagnosis and treatment.

关键词： Kidney renal clear cell carcinoma Acyl CoA dehydrogenase medium chain Immune infiltration Fatty acid oxidation Prognosis

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A projected Newton algorithm based on chemically allowed interval for chemical equilibrium computations

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Frontiers of Chemical Science and Engineering 2024年第3期18卷 25-37页

作者： Hongbin Lu Shaohui Tao xiaoyan sun Li xia Shuguang xiang Institute of Process Systems Engineering College of Chemical EngineeringQingdao University of Science and TechnologyQingdao266042China

The chemical equilibrium equations utilized in reactive transport modeling are complex and nonlinear,and are typically solved using the Newton-Raphson method.Although this algorithm is known for its quadratic convergence near the solution,it is less effective far from the solution,especially for ill-conditioned problems.In such cases,the algorithm may fail to converge or require excessive iterations.To address these limitations,a projected Newton method is introduced to incorporate the concept of projection.This method constrains the Newton step by utilizing a chemically allowed interval that generates feasible descending iterations.Moreover,we utilize the positive continuous fraction method as a preconditioning technique,providing reliable initial values for solving the algorithms.The numerical results are compared with those derived using the regular Newton-Raphson method,the Newton-Raphson method based on chemically allowed interval updating rules,and the bounded variable least squares method in six different test cases.The numerical results highlight the robustness and efficacy of the proposed algorithm.

关键词： chemical equilibrium reactive transport modeling numerical methods projected Newton method

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