Postpartum depression and partner support during the period of lactation:Correlation research and its influencing factors

BACKGROUND Postpartum depression(PPD)not only affects the psychological and physiological aspects of maternal health but can also affect neonatal growth and development.Partners who are in close contact with parturient women play a key role in communication and emotional support.This study explores the PPD support relationship with partners and its influencing factors,which is believed to establish psychological well-being and improve maternal partner support.AIM To explore the correlation between PPD and partner support during breastfeeding and its influencing factors.METHODS Convenience sampling was used to select lactating women(200 women)who underwent postpartum examinations at the Huzhou Maternity and Child Health Care Hospital from July 2022 to December 2022.A cross-sectional survey was conducted on the basic information(general information questionnaire),depression level[edinburgh postnatal depression scale(EPDS)],and partner support score[dyadic coping inventory(DCI)]of the selected subjects.Pearson’s correlation analysis was used to analyze the correlation between PPD and DCI in lactating women.Factors affecting PPD levels during lactation were analyzed using multiple linear regression.RESULTS The total average score of EPDS in 200 lactating women was(9.52±1.53),and the total average score of DCI was(115.78±14.90).Dividing the EPDS,the dimension scores were:emotional loss(1.91±0.52),anxiety(3.84±1.05),and depression(3.76±0.96).Each dimension of the DCI was subdivided into:Pressure communication(26.79±6.71),mutual support(39.76±9.63),negative support(24.97±6.68),agent support(6.87±1.92),and joint support(17.39±4.19).Pearson’s correlation analysis demonstrated that the total mean score and individual dimension scores of EPDS during breastfeeding were inversely correlated with the total score of partner support,stress communication,mutual support,and cosupport(P<0.05).The total mean score of the EPDS and its dimensions were positively correlated with negative support(P<0.05).Multiple linear regression analysis showed that the main factors affecting PPD during breastfeeding were marital harmony,newborn health,stress communication,mutual support,negative support,cosupport,and the total score of partner support(P<0.05).CONCLUSION PPD during breastfeeding was associated with marital harmony,newborn health,stress communication,mutual support,negative support,joint support,and the total DCI score.

Preliminary research on the relationship between long-range correlations and predictability

By establishing the Markov model for a long-range correlated time series （LRCS） and analysing its evolutionary characteristics, this paper defines a physical effective correlation length （ECL） T, which reflects the predictability of the LRCS. It also finds that the ECL has a better power law relation with the long-range correlated exponent γ of the LRCS： T = Kexp（-γ/0.3） ＋ Y, （0 〈 γ〈 1） the predictability of the LRCS decays exponentially with the increase of γ It is then applied to a daily maximum temperature series （DMTS） recorded at 740 stations in China between the years 1960-2005 and calculates the ECL of the DMTS. The results show the remarkable regional distributive feature that the ECL is about 10-14 days in west, northwest and northern China, and about 5-10 days in east, southeast and southern China. Namely, the predictability of the DMTS is higher in central-west China than in east and southeast China. In addition, the ECL is reduced by 1-8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS; however, it is only slightly altered when the decadal linear trend is removed from the original DMTS. Therefore, it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures. Seasonal oscillation is also significant for guiding general weather predictions, as well as seasonal weather predictions.

Canonical Correlation Analysis and climate research

Correlation analysis as used by meteorologists and oceanographers is a tool for the analysisof the spacial or temporal variability of physical fields. In his notes, Dr. Hasselmann pro-posed to combine correlation analysis and linear regression analysis in climate prediction re-search. The main idea is to decompose the physical field into its principal oscillation patterns.

The correlation and relationship of obesity and cancer: a possible research perspective

Recently, obesity is a well-recognized risk factor of type 2 diabetes and cardiovascular disease. There is more and more sufficient evidence that excess body weight is an avoidable cause of excess cancers including gastrointestinal, endometrial, esophageal adenocarcinoma, colorectal, postmenopausal breast, prostate, and renal cancers. The mechanism that obesity association with cancer is remains not well understood. There be some most studied hypothesized mechanisms such as, high levels of insulin and free levels of insulin-like growth factors （IGFs）, sex hormones, adipocytokines, intlammatory cytokines, c-Myc （or Myc） oncogenic transcription factor, obesity-induced hypoxia and Warburg effect, and so on. In the future, the potential mechanisms and conclusions in obesity associated with increased risk for developing cancer, and the underlying cellular and molecular mechanisms will be studied.

Influence of Sows Farrowing Performance on Growth of 21-Day-Old Weaned Piglets and Their Correlation Research

[ Objective ] To study the influence of sows farrowing performance on the growth of 21-day-old weaned piglets and their correlation. [ Methods ] A total of 1 350 Duroc x Landrace Yorkshire piglets produced by 2-6 fetal Landrace x Yorkshire sows from 118 litters within a month were selected to be the test objects. All the newborn piglets were lactated by their parent sows. The test groups were divided according to different numbers （6 - 17） of live birth. The variance analysis were conducted on birth weight, litter weight at birth, number of 21-day-old weaning piglets, weaning weight, weaninl2g weight of litter, weaning survival rate and relative individual weight gain among different weeks. [ Results] Sows farrowing performance had significant influence on the growth performance of 21- day-old piglets. However, the influences among different traits varied, some were even on the contrary. With the increase of the number of live birth, the birth weight, weaning weight of litter and weaning survival rate showed a decline trend. And when the number of live birth was equal or more than 14, the weaning sur- vival rate dropped to below 90% （P 〈 0.01 ） ; when the number of live birth was 13, the litter weight at birth and weaning weight of litter reached the peak. What- ever the number of live birth increased or decreased, the weaning weight showed a regular decline trend. [ Conclusions] When the number of live birth changed from 11 to 13, the maximum values of the economic indicator （weaning weight of litter） and the technical specifications （weaning survival rate） were achieved.

Public cloud computing for seismological research:Calculating large-scale noise cross-correlations using ALIYUN

The amount of seismological data is rapidly increasing with accumulating observational time and increasing number of stations, requiring modern technique to provide adequate computing power. In present study, we proposed a framework to calculate large-scale noise crosscorrelation functions(NCFs) using public cloud service from ALIYUN. The entire computation is factorized into small pieces which are performed parallelly on specified number of virtual servers provided by the cloud. Using data from most seismic stations in China, five NCF databases are built. The results show that, comparing to the time cost using a single server, the entire time can be reduced over two orders of magnitude depending number of evoked virtual servers. This could reduce computation time from months to less than 12 hours. Based on obtained massive NCFs, the global body waves are retrieved through array interferometry and agree well with those from earthquakes. This leads to a solution to process massive seismic dataset within an affordable time and is applicable to other large-scale computing in seismological researches.

Correlation research of serum APoM contents with disease activity for SLE patients

Objective:To study the correlation of serum APoM (Apolipoprotein M) contents with disease activity for SLE (systemic lupus erythematosus) patients.Method: A total of 80 SLE patients treated from January 2013 to March 2017 in our Rheumatology and Immunology Department were selected. The subjects were divided into activity group (44 cases) and relief group (36 cases) according to the SLEDAI scores. There were 31 cases with renal injury and 49 cases without renal injury. The index of CRP, ESR, ApoM and blood lipid level for each group was compared. The correlation of ApoM contents with CRP and ESR was analyzed.Result:The index of CRP and ESR for activity group was higher than relief group;the ApoM contents for activity group were lower than relief group;the index of CRP and ESR for renal injury group was higher than non-renal injury group;the ApoM contents for renal injury group was lower than non-renal injury group;the index of TG and LDL-C for activity group were higher than relief group;the index of TC and HDL-C for activity group were lower than relief group;the index of TG and LDL-C for renal injury group was higher than non-renal injury group;the index of TC and HDL-C for renal injury group was lower than non-renal injury group;the ApoM contents were negatively correlated with the index of ESR and CRP.Conclusion: At the activity and relief stage,, the ApoM contents for renal injury patients and non-renal injury patients are significantly different;the ApoM contents can be seen as the observation index of SEL activity. It may be correlated with the mediation of inflammatory response.

Correlations of the expression of Cx43,SCFFBXW7,p-cyclin E1(Ser73),p-cyclin E1(Thr77)and p-cyclin E1(Thr395)in colon cancer tissues

BACKGROUND Previous cellular studies have demonstrated that elevated expression of Cx43 promotes the degradation of cyclin E1 and inhibits cell proliferation through ubiquitination.Conversely,reduced expression results in a loss of this capacity to facilitate cyclin E degradation.The ubiquitination and degradation of cyclin E1 may be associated with phosphorylation at specific sites on the protein,with Cx43 potentially enhancing this process by facilitating the phosphorylation of these critical residues.AIM To investigate the correlation between expression of Cx43,SKP1/Cullin1/F-box(SCF)FBXW7,p-cyclin E1(ser73,thr77,thr395)and clinicopathological indexes in colon cancer.METHODS Expression levels of Cx43,SCFFBXW7,p-cyclin E1(ser73,thr77,thr395)in 38 clinical colon cancer samples were detected by immunohistochemistry and were analyzed by statistical methods to discuss their correlations.RESULTS Positive rate of Cx43,SCFFBXW7,p-cyclin E1(Ser73),p-cyclin E1(Thr77)and p-cyclin E1(Thr395)in detected samples were 76.32%,76.32%,65.79%,5.26%and 55.26%respectively.Positive expressions of these proteins were not related to the tissue type,degree of tissue differentiation or lymph node metastasis.Cx43 and SCFFBXW7(r=0.749),p-cyclin E1(Ser73)(r=0.667)and p-cyclin E1(Thr395)(r=0.457),SCFFBXW7 and p-cyclin E1(Ser73)(r=0.703)and p-cyclin E1(Thr395)(0.415)were correlated in colon cancer(P<0.05),and expressions of the above proteins were positively correlated in colon cancer.CONCLUSION Cx43 may facilitate the phosphorylation of cyclin E1 at the Ser73 and Thr195 sites through its interaction with SCFFBXW7,thereby influencing the ubiquitination and degradation of cyclin E1.

Research on the Correlation between Meteorological Factors and Flue-cured Tobacco Chemical Components in Different Altitudes of Lincang Region

In this study, the relationship between major meteorological factors and tobacco chemical components in different altitudes of Yongde County, Lincang Region of Yunnan Province was studied using correlation analysis and path analysis methods.The results showed that there was a difference on meteorological factors in different altitudes, causing different impacts on chemical components of flue-cured tobacco; contents of nicotine and total nitrogen decreased with the rising of altitude, conversely, total sugar and reducing sugar contents increased with the rising trend of altitude, and the differences on the contents of potassium （K） and chlorine（CI）, and the ratio of K to CI in different altitudes were not significant, at the altitude of 1 250-1 450 m, each chemical component indicator was better. The correlation between main meteorological factors and conventional chemical components of tobacco leaves was as follows： contents of reducing sugar and total sugar increased with the increase of rainfall and relative humidity, and decreased with the increase of sunshine duration; nicotine content increased with the increase of accumulated temperature and sunshine duration; total nitrogen （N） content increased with the increase of daily mean temperature and accumulated temperature; sugar-alkali ratio increased with the increase of rainfall. Path analysis showed that the direct effect of sunshine duration on reducing sugar and total sugar of tobacco leaves was faint, but the indirect effect was obvious; meanwhile, the direct and indirect effects of it on nicotine and total N were the greatest.

Classical Correlations vs Quantum Correlations—Similarities, Differences, Opportunities

Classical Correlations were founded in 1900 by Karl Pearson and have since been applied as a statistical tool in virtually all sciences. Quantum correlations go back to Albert Einstein et al. in 1935 and Erwin Schrödinger’s responses shortly after. In this paper, we contrast classical with quantum correlations. We find that classical correlations are weaker than quantum correlations in the CHSH framework. With respect to correlation matrices, the trace of classical correlation matrices is dissimilar to quantum density matrices. However, the off-diagonal terms have equivalent interpretations. We contrast classical dynamic (i.e., time evolving) stochastic correlation with dynamic quantum density matrices and find that the off-diagonal elements, while different in nature, have similar interpretations. So far, due to the laws of quantum physics, no classical correlations are applied to the quantum spectrum. However, conversely, quantum correlations are applied in classical environments such as quantum computing, cryptography, metrology, teleportation, medical imaging, laser technology, the quantum Internet and more.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

Research on a Diagnosis Method Based on a Correlation Integral

For the first time, the diagnosis idea based on a correlation integral isproposed, which regard's the correlation integral as a feature set. The correlation dimension iscontained in the double-log curve of the correlation integral to scale, so extracting featuresdirectly from the correlation integral can avoid the bottleneck problem of determining the range ofnon-scale length. Several features extracted from the correlation integral are better than thesingle feature of the correlation dimension when describing the signal. It is obvious that thismethod utilizes more information of the signal than does the correlation dimension. The diagnosisexamples verify that this method is more accurate and more effective.

Patients Involved in Funding Research.A Look Back at the Unique Experience of the ECLAIR Working Group of Canceropole CLARA

This article aims to share an innovative experience of organizing and funding research involving those most directly affected:patients.The“ECLAIR”working group of the Canceropole Lyon Auvergne-Rhone-Alpes(CLARA)was created at the end of 2020 with the goal of contributing to the development of a call for projects on the patient experience in oncology,which was launched in January 2021.Initially composed of 8 members,including 7 patients,coordinated by a project manager from CLARA,the ECLAIR working group actively participated in drafting the specifications of the call for projects,developing the eligibility criteria for applications,revising the evaluation and selection criteria for projects,and monitoring the selected projects.This experience was repeated twice.With significant freedom of action,the working group made two decisions that strongly demonstrate the commitment to supporting research partnerships and the active involvement of those affected:firstly,by making partnership a mandatory requirement for the eligibility of applications,and secondly,by conducting the selection of projects themselves,after an independent scientific evaluation phase.Seeking to shed light on the“black box”of partnership,the article also presents the concrete modalities of interaction among the working group members,the adjustments made between different editions of the call for projects,and the relationships maintained with CLARA.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

Embracing the complexity of lived experiences in psychiatry research:Reflexivity,cultural sensitivity,and emergent design

This article examines the critical integration of reflexivity,cultural sensitivity,and emergent design in qualitative psychiatry research focused on lived experiences.While quantitative methods offer essential clinical insights,qualitative approaches provide a deeper understanding of the emotional,psychological,and social dimensions of mental health.Reflexivity enables researchers to remain aware of how their personal biases and professional backgrounds shape data interpretation.Cultural sensitivity ensures that mental health conditions are understood within their broader cultural contexts,helping avoid misrepresentation and promoting authentic participant expression.Emergent design offers flexibility in adapting the research process to evolving themes,particularly in the dynamic and multifaceted realm of psychiatric conditions.Together,these principles promote ethically sound,participant-centered research that captures the full complexity of lived experiences.The article also highlighted the practical implications of these principles for enhancing both academic knowledge and clinical practice in psychiatry.

Research on the effect of Reynolds correlation in natural convection film condensation

Film condensation is a vital phenomenon in the nuclear engineering applications,such as the gas-steam pressurizer design,and heat removing on containment in the case of postulated accident.Reynolds number in film condensation can be calculated from either the mass relation or the energy relation,but few researches have distinguished the difference between them at present.This paper studies the effect of Reynolds correlation in the natural convection film condensation on the outer tube.The general forms of the heat transfer coefficient correlation of film condensation are developed in different flow regimes.By simultaneously solving a set of the heat transfer coefficient correlations with Re_(mass) and Re_(energy),the general expressions for Re_(mass) and Re_(energy) and the relation between the corresponding heat transfer coefficients are obtained.In the laminar and wavefree flow regime,Re_(mass) and Re_(energy) are equivalent,while in the laminar and wavy flow regime,Re_(mass) is much smaller than Re_(energy),and the deviation of the corresponding average heat transfer coefficients is about 30% at the maximum.In the turbulent flow regime,the relation of Re_(mass) and Re_(energy)is greatly influenced by Prandtl number.The relative deviation of their average heat transfer coefficients is the nonlinear function of Reynolds number and Prandtl number.Compared with experimental results,the heat transfer coefficient calculated from Re_(energy) is more accurate.

Research on Correlation between TCM Syndrome Distribution Characteristics and Prognosis of Hypertensive Intracerebral Hemorrhage Operation

Objective: This study is to analyze correlation between traditional Chinese medicine (TCM) syndrome distribution characteristics and prognosis of hypertensive intracerebral hemorrhage (HICH) operation. Methods: In this study, a total of 150 patients who had received HICH operation from April, 2017 to December, 2020 in our hospital and conformed to inclusion standards were selected. According to classification of TCM syndromes, amount of bleeding of patients was recorded through multiple radiological technologies, baseline information was collected, and prognosis was investigated. The final event was long-term follow-up visit of all-cause mortalities. Moreover, correlation between prognosis and TCM syndromes was analyzed. Results: It found through investigations that there are no statistically significant differences in composition ratio of TCM syndromes among patients with different genders and different age ranges (P > 0.05). Among so many TCM syndromes, bleeding amount of patients with declining vitality and distraction is the highest, while bleeding amount of patients with stirring wind due to yin deficiency is the lowest. With respect NIHSS scores, the patients with declining vitality and distraction show the highest NIHSS scores at admission and 10 d of the course of the disease, followed by patients with upward disturbance of wind-fire. There are statistically significant differences among these two groups (P < 0.05). In this study, follow-up visits are performed to all 150 patients and the average follow-up visit time ranges within 2 - 15 months. A total of 13 deaths are reported. Number of all-cause deaths has statistically significant differences among different syndromes (P < 0.05). Conclusions: Evolutionary characteristics of TCM syndromes of HICH are manifested by development from evidence-based symptoms to deficiency syndromes. Different syndromes are correlated with prognosis of patients. On the one hand, this can be used as an objective index for TCM syndrome classification. On the other hand, this is conducive to judge prognosis recovery of patients and to apply the corresponding symptomatic treatment.

Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.