Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive deterioration of memory and cognition. Mild cognitive impairment (MCI) has been implicated as a prodromal phase of AD. Although abnormal functional connectivity (FC) has been demonstrated in AD and MCI, the clinical differentiation of AD, MCI, and normal aging remains difficult, and the distinction between MCI and normal aging is especially problematic. We hypothesized that FC between the hippocampus and other brain structures is altered in AD and MCI, and that measurement of abnormal FC could have diagnostic utility for the classification of different AD stages.Elderly adults aged 60–85 years were assigned to AD, MCI, or normal control (NC) groups based on clinical criteria. Functional magnetic resonance scanning was completed by 119 subjects. Five dimension reduction/classification methods were applied, using hippocampus-derived FC strengths as input features. Classification performance of the five dimensionality reduction methods was compared between AD, MCI, and NC groups.FCs between the hippocampus and left insula, left thalamus, cerebellum, right lingual gyrus, posterior cingulate cortex, and precuneus were significantly reduced in AD and MCI. Support vector machine learning coupled with sparse principal component analysis demonstrated the best discriminative performance, yielding classification accuracies of 82.02% (AD vs. NC), 81.33% (MCI vs. NC), and 81.08% (AD vs. MCI).Hippocampus-seed-based FCs were significantly different between AD, MCI, and NC groups. FC assessment combined with widely used machine learning methods can improve AD differential diagnosis, and may be especially useful to distinguish MCI from normal aging. Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that leads to cognitive and physiologic dysfunction. The predicted 2050 prevalence of AD is estimated at 135.46 million patients . The hisBrain functional connectivity (FC) is a descriptive measure of spatiotemporal correlations between distinct regions of the cerebral cortex  that canMachine learning has been widely used in medical image processing due to its sensitive identification of patterns within large data sets. Numerous algorithms have been proposed to extract features from magnetic resonance and computed tomographic (CT) images to classify stages of AD. However, meaningful intermediate functional and structural information of the brain is unavailable , 2018. NHippocampal function is essential to memory performance; impairments are associated with AD. The utility of hippocampal shape and volume measurements to support the diagnosis of AD has been evaluated in multiple MRI studies . PlateroElderly adults aged 60–85 years were assigned to AD, MCI, or normal control (NC) groups based on clinical criteria (see below), and then underwent fMRI scanning. We first selected the hippocampus as the seed region and constructed FC maps in a voxel-wise manner. We then used five machine learning algorithms to reduce dimensionality using hippocampus-derived FC strengths as input features to classify patients with AD or MCI, and normal controls (NC), and compared the accuracy of the five algorithms. The study protocol was approved by the Medical Ethics Committee of Qilu Hospital of Shandong University.Written informed consent was obtained from all subjects or their families using an informed consent approved by the Medical Ethics Committee of Qilu Hospital of Shandong University. Inclusion criteria included (1) right-handedness; (2) age of 60–85 years; and (3) ability to perform neuropsychological testing and tolerate MR scanning.Exclusion criteria included: (1) metabolic diseases, such as hypothyroidism and vitamin B12/folic acid deficiencies; (2) history of psychiatric disorders such as depression or schizophrenia; (3) history of neurologic diseases associated with cognitive deficits ; (4) history of drug or alcohol abuse; (5) receipt of neurotropic medications; (6) contraindications to MRI; and (7) CT or MRI abnormalities consistent with brain infarction or hemorrhage. Enrollment was limited to Chinese Han subjects to avoid population stratification artifacts.After enrollment, subjects were assigned to either the AD, MCI, or NC group. AD subjects were selected according to the following inclusion criteria: (1) diagnosis of probable AD according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria; and (2) Clinical Dementia Rating (CDR) of 1 or 2. Subjects were assigned to the MCI group as described previously  based upA Siemens verio 3.0 Tesla MR scanner  was used to generate MR images. Tight and comfortable foam padding was used to minimize head movement, and earplugs were provided to mask scanner noise. Resting-state fMRI data were obtained using Gradient-Echo Single-Shot Echo-Planar Imaging sequence (GRE-SS-EPI) with the following imaging parameters: repetition time (TR)/echo time (TE) 2000/30 ms; field of view (FOV) 220 mm × 220 mm; matrix 64 × 64; flip angle (FA) 90°; slice thickness = 3 mm; and slice gap 1 mm; 36 transversal slices; 180 volumes. Subjects were instructed to close their eyes, remain awake and as stationary as possible, and not to concentrate on particular thoughts for the duration of the fMRI scan. Sagittal 3D T1-weighted images were generated by magnetization-prepared rapid acquisition gradient echo sequence .1 based on MATLAB. Considering the magnetic field instability and the frequency of movement artifacts during the initial scanning period, the first 10 volumes were removed and the remaining 170 volumes were evaluated. Slice-timing correction was performed to ensure the consistency of slice acquisition times. The resulting fMRI data sequences were realigned to compensate for head movement. Because small head movements contaminate FC results  results , scans w results . fMRI daAs an important structure for memory function, the hippocampus may be especially relevant to the study of Alzheimer’s disease. Consequently, we selected the hippocampus as a seed region. However, because the neurophysiologies of the left and right the hippocampus may differ, we used both hippocampal aspects  as seeds, and performed voxel-wise calculations of FC. The Pearson correlation coefficient between the time series of seeds and each voxel measures the consistency of their activities, which is defined as functional connection strength. Three-dimensional FC maps were generated for each seed. Fisher-z transformation was used to improve the normality of functional connectivity .p < 0.05.Demographic and clinical characteristics were compared by the Chi-square and non-parametric Kruskal–Wallis tests. The latter was used to compare variables that are not subject to normal distributions. Statistical analyses were performed with Statistical Package for the Social Sciences  and significance threshold was set to t-test in the three groups was used as the mask of ANCOVA. Significant difference of statistical results was set at p < 0.05 (with a combined threshold of p < 0.05 and a minimum cluster size of 71 voxels), which was corrected by the AlphaSim program in RESTplus software. Brain regions with significant differences were extracted as masks. Two-sample post-hoc t tests of intra-mask FC maps were performed by Data Processing and Analysis for Brain Imaging2 between each pair of the three groups . The intergroup significance level was set at p < 0.05 (with a combined threshold of p < 0.05 corrected by the AlphaSim program).Left and right hippocampus seed-based FC maps were subjected to statistical analysis. Analysis of covariance (ANCOVA) was used to differentiate FC values among the three groups. Meanwhile, the union of the significant regions of the one-sample Machine learning methods have multiple applications in AD because they can learn data features quickly and efficiently, and can also fit the distributions of new data. Various machine learning algorithms have been applied to AD diagnosis. However, if a small number of four-dimensional fMRI images are directly classified using machine learning methods, performance will be poor and prone to over-fitting. We took the FC values of the significant difference regions as individual characteristics, supplemented by machine learning methods, to reduce over-fitting and realize the classification of small-size samples. Taking the regions with significant differences as masks, related subject FC values in each mask were extracted and spliced into a feature vector, which is the basis of using machine learning for classification. Feature vectors contain dozens or hundreds of functional connection values; consequently, the sample size of our subjects is relatively small compared to the number of features. This type of data can easily cause the Curse of Dimensionality problem. Dimensionality reduction can save computer memory requirements and prevent model over-fitting by eliminating redundant features and reducing computational complexity. Therefore, it is necessary to select features that are more useful for classification, and to map high-dimensional features to a low-dimensional space effectively. The following methods have good performance in dimensionality reduction: variance-threshold, mutual information, principal component analysis, and sparse principal component analysis.Features whose variance is less than the set value were removed by the variance threshold method. The variance calculation method is shown in equation (1). Small variance indicates that data are concentrated near a value; consequently, their contribution to classification is relatively small. Insufficient threshold indicates that only a small number of features are excluded, while excessive threshold indicates a tendency to lose important information. Consequently, the selection of an appropriate variance threshold is critical.Mutual information is used to evaluate the correlation between independent and dependent variables. MI can measure the correlation between features and labels, to select features highly related to labels. The MI method is usually implemented by selecting a suitable threshold and defined as equation (2).p-dimensional coordinate system to a new coordinate system, with the largest variance taken as the first coordinate axis direction, because the maximum data variance yields the most valuable information. The second new coordinate axis selects the direction which is orthogonal to the first new coordinate axis and has the second largest variance. This process is repeated n times. This process can be realized by singular value decomposition of the feature matrix, as shown in equation (3). Let X be a n×p matrix, where n is the number of subjects and p is the number of features. Through the resulting coordinate system, most of the variance is contained in the first several coordinate axes, while the variance of the later axes is almost zero. Consequently, dimensionality may be reduced by selecting the first few axes and excluding the later axes for analysis.Principal component analysis  is a widT indicates transpose, U indicates the principal components of unit length, and the columns of V are the corresponding loadings of the principal components. Most of the information will be contained in the first few principle components.where, X is the data matrix, and the second and third items are elastic net penalty terms. Whereas, the same λ is used for all k components, different λj1, are allowed to penalize the loadings of different principal components. Optimization can be solved by fixing α and β, and finally sparse principal components can be obtained.The SPCA algorithm reduces dimensionality and the size of explicitly used variables . PCA cann≪p) have been developed. Our data are directly applicable to the latter  and gene expression arrays , and then fused the left and right functional connection values to form a np feature matrix, in which n is the total number of subjects in the two compared groups, and p is the total number of features . After dimensionality reduction, feature matrices were classified by SVM.Support vector machine with a radial basis function kernel was selected for binary classification. SVM optimizes models by introducing relaxation variables and adjusting penalty coefficients. For each pair of the three groups , we extracted the functional connection values  from regions with significant differences in the two-sample post-hoc n iterations  after the statistical analysis. Each iteration uses one subject to test the model, while the remaining subjects are used to train the model. This procedure was repeated until each subject was used as a test set once. The final classification accuracy shown in Leave-one-out cross validation (LOOCV) was used to evaluate the accuracy of the three binary classification tasks. One subject is used as the test set, and the remaining subjects are used as the training set for dimensionality reduction and classification experiments. Specifically in each iteration, the training set is performed dimensionality reduction and classification first, and then the testing set is used to test the effect of the training model. LOOCV performs p < 0.001) and between each pair of groups   were excluded from analyses due to excessive head motion during scanning. The remaining 119 subjects included 44, 30, and 45 in the AD, MCI, and NC groups, respectively. There were no significant intergroup differences in age, gender, or years of education. However, MMSE scores were significantly different between the three groups  .The NC group displayed significant connectivity between the left and right hippocampus and several brain regions that included the medial prefrontal cortex, posterior cingulate cortex (PCC), precuneus, inferior temporal cortex and inferior parietal cortex, and overlapped with regions of the default-mode network . The numt tests revealed that the AD group had decreased FC in the PCC and increased FC in the left insula compared with the NC group. Compared with the MCI group, the AD group showed significantly decreased FC in left thalamus and cerebellum. Compared with the NC group, the MCI group showed increased FC in the right lingual gyrus and left thalamus , 81.08% (AD vs. MCI), and 81.33% (MCI vs. NC). SPCA+SVM exhibited the largest area under the ROC curve and had the best classification performance as shown in We analyzed FC maps of both the left and right hippocampus, which are of critical importance to memory function and early deficits in AD and MCI. Bilateral hippocampal-whole brain FC maps of AD, MCI, and normal elderly controls were subjected to dimensionality reduction and classification. The combination of SPCA and SVM yielded high classification accuracies, and may offer an accurate modality to facilitate the differential diagnosis of MCI and AD.Abnormal FCs were located primarily in the PCC, precuneus, insula, lingual gyrus, thalamus, and cerebellum in the AD and MCI groups. The PCC and precuneus are core regions of the default-mode network initially described by More specifically, the MCI group showed increased FC in the left thalamus and right lingual gyrus compared with the NC group. Previous studies have indicated that the thalamus plays an important role in declarative memory and in modulating communication in all areas of the cerebral cortex . AmyloidNotably, compared with the NC group, the AD group showed enhanced connectivity of the middle and posterior left insula. The insula regulates cerebral circulation and facilitates memory as well as emotional and sensory processes . AD is an≪p), setting λ to infinity as proposed in SPCA accelerates the calculation process. The highly accurate classification results generated by our algorithm suggest that abnormal FC between the hippocampus and whole brain is a clinically relevant and reliable diagnostic biomarker.Computer-aided classification techniques that combine machine learning methods to MRI or PET have been applied for the diagnosis of AD or MCI . The mosOur study had several limitations. First, the sample size in this study was relatively small. A much larger sample should be collected to improve the robustness and generalizability of the classification model. Second, multimodal features such as diffusion tensor imaging, cognitive scale scores, and cerebrospinal fluid tau protein levels should also be investigated, which may lead to higher classification accuracy. Finally, a comparison of our results with large benchmark datasets would be valuable. The implementation of a classification model based on public datasets should be considered for future research.We calculated FCs between the bilateral hippocampus and the whole brain in AD, MCI, and NC. Both the left and right hippocampus have significantly enhanced or attenuated FC to multiple important brain regions, primarily the PCC, left insula, left thalamus, and cerebellum. The abnormal FC values of each subject were extracted as discriminative patterns, which when combined with SPCA for dimensionality reduction and SVM for classification, facilitated the highly accurate differentiation of MCI from NC. Our results suggests that abnormal hippocampal connectivity may serve as a potential neuroimaging biomarker to expedite the early diagnosis of AD and MCI.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Qilu Hospital of Shandong University. The patients/participants provided their written informed consent to participate in this study.QW and DW conceived and designed the study. QZ, YW, and CZ performed testing and data collection and drafted the manuscript. QZ, QX, CZ, YY, ZL, YL, YW, ZS, JW, and ML performed the data analysis and interpretation. All authors contributed to the article and approved the submitted version.ZS was employed by the company Shandong Chenze AI Research Institute Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Male infertility is a common health problem that can be influenced by a host of lifestyle risk factors such as environment, nutrition, smoking, stress, and endocrine disruptors. These effects have been largely demonstrated on sperm parameters . In addition, several studies showed the deregulation of sperm proteins in relation to some of these factors. This review inventories the literature related to the identification of sperm proteins showing abundance variations in response to the four risk factors for male infertility that are the most investigated in this context: obesity, diabetes, tobacco smoking, and exposure to bisphenol-A (BPA). First, we provide an overview of the techniques used to identify deregulated proteins. Then, we summarise the main results obtained in the different studies and provide a compiled list of deregulated proteins in relation to each risk factor. Gene ontology analysis of these deregulated proteins shows that oxidative stress and immune and inflammatory responses are common mechanisms involved in sperm alterations encountered in relation to the risk factors. Spermatozoa are highly specialised and differentiated cells that form from spermatogonial stem cells (SSCs) during spermatogenesis in the seminiferous tubules of the testicles . DisruptWith reproductive history, semen quality analyses are the primary steps in the assessment of male infertility ,7. TheseSeveral studies also focused on the detrimental effects of risk factors for male infertility on the protein composition of sperm. Indeed, in addition to diseases, more and more factors are highlighted for their negative impact on male reproductive potential ,22. AmonAn exhaustive literature search was conducted on Google Scholar and PubMed by using the terms “sperm/spermatozoa/male fertility + risk factor + protein/proteome” to identify articles related to sperm proteins and the above-mentioned risk factors for infertility, published in English. All the studies included were performed on either the whole semen, the seminal plasma, the complete set of cells in the semen, or the purified spermatozoa from human, mouse, or rat . Althoughttp://david.abcc.ncifcrf.gov/ accessed on 27 September 2021) in order to identify overrepresented Gene Ontology (GO) Biological Process terms )Mass spectrometry (MS) is a sensitive and robust tool that can monitor the abondance of hundreds or thousands of proteins at once . Table 1Before processing for MS, extracted proteins can be submitted to a step of labelling to perform ulterior protein quantification. Labelling can be conducted with various methods, reviewed in . In the After this step, proteins are submitted to enzymatic digestion, often by trypsin, to generate peptides that will be analysed by MS. Protein digestion can be performed directly on the proteins (“in-solution digestion”), on the filter in the case of FASP, or after protein separation by one- or two-dimensional polyacrylamide gel electrophoresis (1D- or 2D-PAGE) e.g., ,34,41)  [Tryptic peptides are then analysed with a mass spectrometer, which detects peptides based on their mass-to-charge ratio (m/z)   ,40). Them/z) . They arm/z) .p-value associated with the calculated fold change. In addition to this p-value, exclusion criteria are applied  to increase the confidence in the results [To identify differentially expressed proteins between the study and the control groups, different strategies are used . In the  results . Although MS analyses based on 2D protein separation have been used for many proteomic studies, it is well known that gel-free approaches combined with LC-MS/MS, called “shotgun proteomics”, are more sensitive and allow the identification of a significantly higher number of proteins .Several tests involve the use of antibodies directed against specific proteins to investigate their abundance variations between different tested groups . The mosRecently, our team used Multiple-Reaction Monitoring (MRM) to investigate the abundance of HSP70 isoforms in spermatozoa  data. Indeed, people affected by overweight and obesity have a BMI ≥ 25 kg/m² or ≥ 30 kg/m², respectively . ReductiIn order to identify obesity-associated proteomic changes in spermatozoa, Kriegel et al.  used 2D-Recently, Pini et al.  studied Another study focused on proteomic changes associated with obesity-induced asthenospermia. Using a label-free quantitative LC-MS/MS approach, they compared the proteome of spermatozoa from three obese individuals with severe asthenospermia and three clinically healthy individuals  Table 1Table 1. Seminal plasma has also been investigated to identify changes in protein abundance associated with obesity in humans. Ferigolo et al.  investigSome studies used a rodent model to assess the effect of obesity on sperm proteome. To this end, they compared mice or rats fed with a control diet (CD group) and mice or rats fed with a high-fat diet (HFD group) for several weeks. Peng et al.  compared2. Although no significant correlation could be established between the BMI and the abundance of HSP70 isoforms, our results showed a trend of increased abundance for HSPA1L and HSPA8 according to the BMI, and a trend of decreased abundance for HSPA5  or when the body is unable to effectively sense the INS it produces (type-2 diabetes) . The risIn addition to the impact of obesity, Kriegel et al.  also invLater, a study conducted by An et al.  used iTRThe impact of diabetes on the proteome of spermatozoa was also studied in rats by Carvalho et al. , includiAfter applying our criteria defined in the Methods section, we obtained a compiled list of 82 proteins which appear to be deregulated in relation to diabetes, of which nine (11%) were common to two independent studies . As obseGO analysis identified enriched biological processes related to several types of metabolic processes, and to tissue homeostasis, regulation of apoptosis, cytoskeleton, fertilisation, and platelet degranulation in humans . In rodeA WHO report estimates the number of smokers in the world at 1.3 billion. This corresponds to about 20% of the world’s population and 37% of males aged 15 years or older ,110. In To the best of our knowledge, only one study evaluated the effect of tobacco smoking in humans by using a global proteomic approach . These aAnother proteomic profiling study was performed on mice exposed daily to cigarette smoke. Chen et al.  analysed2+-ATPase (ATP2B4) in spermatozoa from smoking men, associated with increased cadmium concentrations in the seminal plasma. They suggested this effect could be responsible for the decreased motility observed in spermatozoa from smokers [Several studies analysed the expression level or the activity of sperm proteins involved in different cellular responses in smoking men . Cigaret smokers . A decre smokers . As the  smokers . Ghaffar smokers  observed smokers . Finally smokers , was alsAfter compiling all the proteins deregulated in smoking individuals and applying our criteria on the fold changes, we obtained a list of 54 proteins . Three oIn humans, GO analysis revealed the significant enrichment of processes involved in response to drug, immune response, apoptosis, oxidative stress, and cell differentiation . In the The European Commission and the U.S. Environmental Protection Agency (EPA) define endocrine disruptors as chemical compounds that can interfere with the hormonal system and have negative effects on developmental formation, reproduction, and the immune and nervous system of an individual ,129. TheIn order to understand the impact of BPA on the sperm proteome, Rahman et al.  exposed Later, the same team investigated the effect of gestational BPA oral exposure on the proteomic profile of spermatozoa from the first generation (F1) of male mice  Table 1Table 1. To the best of our knowledge, in humans, only one study focused on the impact of BPA on sperm proteins. Barbonetti et al.  showed tBy compiling these studies, we obtained a list of 36 proteins which seem to be deregulated in relation to BPA exposure . FourteeWe performed the GO analysis on the list of mouse proteins only, as only two proteins were highlighted in humans. This analysis showed a significant enrichment of processes related to oxidation–reduction processes, energy production, metabolism, and response to drug .Over the past 30 years, an increasing number of studies have demonstrated the negative impact of environmental and lifestyle factors on male fertility. In particular, smoking, diabetes, psychological stress, drug or alcohol use, endocrine disruptors, and nutrition have been shown to have a deleterious effect on sperm parameters , as well as on DNA integrity, capacitation, and ATP production by mitochondria ,24,148.


Background: Virtual reality (VR) intervention is an innovative and efficient rehabilitative tool for patients affected by stroke, Parkinson's disease, and other neurological disorders. This meta-analysis aims to evaluate the effects of VR intervention on cognition and motor function in older adults with mild cognitive impairment or dementia.Methods: Seven databases were systematically searched for relevant articles published from inception to April 2020. Randomized controlled trials examining VR intervention in adults with mild cognitive impairment or dementia aged >60 years were included. The primary outcome of the study was cognitive function, including overall cognition, global cognition, attention, executive function, memory, and visuospatial ability. The secondary outcome was motor function, consisting of overall motor function, balance, and gait. A subgroup analysis was also performed based on study characteristics to identify the potential factors for heterogeneity.Results: Eleven studies including 359 participants were included for final analysis. Primary analysis showed a significant moderate positive effect size (ES) of VR on overall cognition  = 0.31–0.59; P < 0.001), attention/execution , memory , and global cognition . Secondary analysis showed a significant small positive ES on overall motor function . The ES on balance  was significant and moderate. The ES on visuospatial ability and gait was not significant. In the subgroup analysis, heterogeneity was detected in type of immersion and population diagnosis.Conclusions: VR intervention is a beneficial non-pharmacological approach to improve cognitive and motor function in older adults with mild cognitive impairment or dementia, especially in attention/execution, memory, global cognition, and balance. VR intervention does not show superiority on visuospatial ability and gait performance. Dementia is a collective name for a heterogeneous group of chronic neurodegenerative diseases characterized by progressive deterioration of goal-directed behaviors and cognitive function  is a new technology for implementing rehabilitation of cognitive and motor function  in Kim et al. , the incThe primary objective of this meta-analysis was to assess the effect of VR intervention on cognitive function, including overall cognition, global cognition, attention, executive function, memory, and visuospatial ability. The secondary objective was to identify the effect of VR on motor function, which comprises balance and gait.The results of this meta-analysis are reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines .Intervention: We included studies using VR interventions that met the following definition: “The VR intervention should be the use of interactive simulations created with computer hardware and software to present users with a virtual figure to engage in environments that appear and feel similar to real world objects and events.” We did not limit the type of VR according to level of immersion . Interventions needed to be implemented for ≥5 h using standardized computerized tasks or interactive video games.Control: The comparison group received either an alternative intervention or no intervention. Alternative interventions included any activity designed to be therapeutic at the impairment, activity, or participation level that did not include the use of VR, ranging from motor and/or cognitive training to health education.Outcome: The primary outcome of this study is cognitive function, including overall cognition, global cognition, attention, executive function, memory, and visuospatial ability. The secondary outcome is motor function, consisting of balance and gait. All outcome measures had to be evaluated at baseline and immediately after the intervention period. Long-term follow-up data was not included in data synthesis, as only a small number of studies reported this information.Study: Eligible studies were peer-reviewed articles reporting results from RCTs that examined the effects of VR on one or more cognitive outcomes and motor function in adults aged ≥60 years with MCI or dementia. The following types of articles were excluded: (1) prospective or retrospective cohort studies; (2) case reports; (3) conference abstracts; and (4) not written in English.Two reviewers (SZ and YS) conducted the initial online search independently to avoid selection bias. After excluding duplicate studies, article titles, and abstracts were reviewed. If an abstract was considered relevant or ambiguous, the full text was reviewed and inclusion and exclusion criteria were applied. Disagreements regarding study eligibility were resolved by CG, who approved the final list of included studies.Data regarding study characteristics  of each article were extracted. We also summarized the components of the intervention in the two groups and extracted differences between them based on the description of interventions and the purpose of trials which could contribute to the superiority of VR. In the control group, a blank control group was defined as passive interventions while the same total training time in the experimental group was defined as active interventions. Most data was entered as mean with standard deviation (SD) for the VR and control groups at baseline and immediately after training. When mean and SD were not available, mean changes from baseline with SD or mean differences with 95% confidence interval (CI) were extracted. In addition, we contacted the authors to request raw summary data if there was no data available. For studies that included several tests of the same domain, each domain was averaged to one pooled effect size (ES) using Comprehensive Meta-Analysis (CMA) software version 2.0 . For example, if a study included several attention tests, then these tests were summarized to generate one pooled effect size  and 95% CI of changes in outcome measures between the experimental group (EG) and control group (CG) from pre- to post-test. Estimate variance was scaled up based on an assumed inter-correlation between the tests of 0.7  to assess the quality of included studies using a score  Maher e. The scaPublication bias was assessed by a funnel plot that displayed the relationship between sample size and ES. Small sample studies with a relatively large variance scatter at the bottom and large sample studies appear toward the top clustering around the mean ES. Studies that fall outside the funnel shape have a high risk of bias  and two reported Alzheimer's dementia (n = 42). Thus, the analysis involved a total of 359 participants . Mean patient age was 75.84 years and 71.82% of the patients were female. Three studies reported full immersive VR, seven reported semi-immersive VR, and the remaining one reported low immersive VR. The VR methodology in all studies was task-oriented training. The interactive devices included head-mounted device, stick, Kinect, depth camera, sensor motion tracker, Nintendo Wii, touchscreen, keyboard, Bio Rescue, or gamepad. Number of intervention sessions ranged from 10 to 40 sessions. Training frequency varied from 1 to 5 sessions per week and the duration per session varied from 20 to 90 min. Three interventions in the control group were passive and eight interventions were active. The brief components of interventions in the two groups are listed in (1) Attention/Executive Function: Trail Making Test  Memory: visual span test  Visuospatial ability: constructional praxis  Global Cognition: Montreal Cognitive Assessment  Gait: Gait speed, stride time, gait system  Balance: Tinetti test , attention/execution , memory , and global cognition  .Primary analysis using the random effects model showed a significant moderate positive ES of VR on overall cognition (I2 = 0%) ; Table 4g = 0.28; 95% CI = 0.05–0.51; P = 0.018; I2 = 0%). Its ES on balance  was significant and of moderate efficacy. However, the ES of VR on gait was not significant   ; Table 4g = 0.46; 95% CI = 0.25–0.68; P < 0.0001) than for patients with dementia . Regarding type of immersion, studies using full immersive VR  had a greater efficacy than studies using semi-immersive VR , but did not reach statistical significance on low immersive VR . For the training time, more than 20 h showed moderate effect size , which were almost equal to those <20 h . In terms of the effectiveness of interventions in the control group, active  and passive  interventions both showed moderate effect sizes.The results of a subgroup analysis according to study characteristics are shown in Based on the results from 11 high quality RCTs, this meta-analysis showed that VR intervention exhibited a moderate effect on several measures of cognitive and motor function, including attention/execution, memory, global cognition, and balance. This indicates VR is a promising effective non-pharmacological therapy for older adults with MCI or dementia. However, VR intervention did not show a significant effect on visuospatial ability or gait.To the best of our knowledge, this is the first meta-analysis to focus on the effect of VR intervention on specific cognitive and motor function domains in older adults with MCI or dementia. Recently, Kim et al.  investigd = 0.42; 95% CI = 0.24–0.60), demonstrating a moderate effect of VR on cognitive function , in contrast to our findings. Another recent meta-analysis that evaluated the effect of combined cognitive and physical exercise training on cognitive function in older adults with MCI or dementia did not find a significant effect on the attention/execution cognitive domain  or memory  either , verbal memory , and attention  in older adults with MCI or dementia. They found no significant effect on visuospatial ability , which is consistent with our findings. Another recent review noted that VR was more effective in improving attention, visuospatial deficits, and motor impairment in stroke patients , but did not evaluate the effect on measures of gait, balance, or falls. Balance improvement  was in accordance with the efficacy of VR intervention in community-dwelling older adults found by Neri et al.  population diagnosis ; (2) type of immersion ; (3) training time ; (4) effectiveness of control group . We have found positive effect of VR intervention in the MCI group while no statistical significance in AD. It may be related to the progression of MCI to dementia that MCI as the “predementia stage” is less severe compared to AD (Langa and Levine, The mechanism behind VR technology for patients with MCI or dementia is still unclear and few studies have tried to explore it. As mentioned before, VR intervention has two unique features, immersion and interaction, which are thought to be the mechanisms behind the beneficial effect of VR technology. The feature of immersion can bring the sense of embodiment (Tieri et al., Another unique feature is interaction which plays an important role in the immersion, feeds back the information in real time and has a reward system. By combining different external equipment such as motion tracker, headphone, haptic gloves, we can deliver the information of changes in the real body, resulting from the reactions to virtual environment, to the virtual body and easily set up the association of the virtual body with the virtual environment Slater, . In addiHowever, these two features of VR technology are not independent and they influence each other. Therefore, to avoid one and explore the mechanism of the other is difficult. The above mechanism is our speculation, so future trials should try to set up a reasonable comparable group to explore these features respectively and help clarify the underlying mechanism of VR.Furthermore, among current trials of VR interventions for patients with MCI or dementia, there are still few limitations. First, all of 11 included studies are goal-orientated that could be difficult for patients with severe cognitive impairment, like dementia, to finish such tasks. Our subgroup analysis showing no statistical significance in dementia may be the result of training difficulty. The efficacy of VR may reduce because the majority of the VR devices used are commercialized and few have adjusted their components to fit the needs of these patients. Second, although VR as a non-invasive, non-pharmacological cognitive rehabilitation intervention has gained increasing attention in recent years, health and safety must be taken into consideration, especially when intended for the use in older adults with neurodegenerative diseases. Cyber-sickness, a visually induced motion sickness reaction (Nooij et al., The strength of this meta-analysis is that only RCTs were included. Furthermore, to the best of our knowledge, this is the first meta-analysis to focus on both cognitive and motor functions as well as specific cognitive domains. However, several limitations should be addressed. First, the intervention characteristics varied between studies. The optimal frequency and duration of intervention remain to be explored to maximize intervention effects. Second, as the training sessions in each study are diverse, we only evaluated the immediate results after VR intervention to avoid bias. Therefore, the follow-up effect of VR in MCI or dementia was not analyzed so we could not estimate whether VR can prevent long-term worsening of dementia or conversion of MCI to dementia without persistent training. Third, due to the relatively small number of participants in the included studies, it is statistically inappropriate to analyze the impact of varied intervention components or different subtypes and severities of MCI or dementia.Further research is needed to explore the most effective characteristics of VR intervention, specifically examining type, frequency, and duration of intervention as well as immersion. In addition, large sample studies are needed and long-term effects should be studied to gain insight into possible maintenance effects. Finally, future studies should investigate the effects of VR intervention on changes in neuroimaging findings and molecular markers.Our meta-analysis shows that VR intervention is a beneficial non-pharmacological approach to improve cognitive and motor function in older adults with MCI or dementia, especially in attention/execution, memory, global cognition, and balance. Moreover, VR intervention does not show superiority on visuospatial ability and gait performance. The clinical relevance of our findings remains to be confirmed in future research.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.SZ, YSu, YSh, and YZ chose the topic. SZ, YSu, and YSh performed the analysis. SZ, YSu, and YZ analyzed the data. CG, TW, and NA participated in the whole process. CG and TW made final decisions. All authors contributed to writing of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


From the data during a baseline period when patients did not have any CVD, we extracted 18 features from electrography (ECG) based on signal processing methods, 30 ECG features based on artificial intelligence (AI), ten clinical risk factors for CVD. We trained the model and evaluated it by using CVD outcomes result, monitored in follow-ups. The optimal feature vectors were selected through statistical analysis and support vector machine recursive feature elimination (SVM-RFE) of the extracted feature vectors. Features based on AI, a novel proposal from this study, showed excellent performance out of all selected feature vectors. In addition, new parameters based on AI were possibly meaningful predictors for CVD, when used in addition to the predictors for CVD that are already known. The selected features were used as inputs to the prediction model based on SVM for CVD, determining the development of CVD-free, coronary heart disease (CHD), heart failure (HF), or stroke within ten years. (3) Results: As a result, the respective recall and precision values were 82.9% and 87.5% for CVD-free; 71.9% and 63.8% for CVD; 57.2% and 55.4% for CHD; 52.6% and 40.8% for HF; 52.4% and 44.6% for stroke. The F1-score between CVD and CVD-free was 76.5%, and it was 59.1% in class four. (4) Conclusion: In conclusion, our results confirm the excellence of the prediction model for CVD in patients with SDB and verify the possibility of prediction within ten years of the CVDs that may occur in patients with SDB. Partial or total obstruction of the upper airway during sleep in sleep-disordered breathing (SDB) incurs respiration issues such as apnea or hypopnea as Recurrent sleep apnea or hypopnea acts as an acute stress factor in the cardiovascular system by inducing hypoxemia, reoxygenation, sudden pleural pressure changes, and awakenings of the central nervous system as The National Heart Lung and Blood Institute conducted a multi-center cohort study named the Sleep Heart Health Study (SHHS) to investigate the relationship between SDB and CVDs . ParticiTherefore, in this study propose an algorithm based on artificial intelligence (AI) that can predict the development of CVDs, e.g., CHD, HF, and stroke within ten years, by using the ECG and common risk factors for CVD in patients with SDB. We believe that the potential for the prediction of comorbidities in patients with SDB, as verified in this study, will contribute to the realization of medical services.SHHS is a cohort study conducted to investigate the outcomes of cardiovascular and sleep disorders . It was p > 0.05).Selected data in this study are displayed in For ECG signal, polysomnography data from baseline study were used, and the ECG signal was analyzed during sleep, from sleep onset to sleep offset. After detection of the QRS complex and T wave using the adaptive threshold algorithm and morphological method ,18, QTc To calculate heart rate variability (HRV), we removed ectopic beats in the RR series, and this signal was defined as NN . For HRV analysis after interpolating the NN in equidistance, resampled it at 4Hz. The resample signal was transformed using the fast Fourier transform in 30 s segments, a square of which was the power spectrum density. Frequency bandwidths that were used to calculate the frequency domain characteristics were: very-low frequency (VLF: 0~0.04 Hz), low frequency (LF: 0.04~0.15 Hz), and high frequency (HF: 0.15~0.4 Hz). Finally, we extracted 18 signal processing-based ECG features after calculating the average and standard deviation of each feature during the whole sleep period .Convolutional neural network (CNN) structure consisted of three convolutional layers with activation functions, each of which was followed by a max-pooling layer. A dropout technique was applied to the last three convolutional layers to avoid overfitting, as in the 1D CNN algorithm. The fully connected layer was used for final discrimination using a softmax activation function. Finally, the output was the likelihood that the observed data was produced by CVD or CVD-free event.Finally, we extracted 30 AI-based ECG features after calculating the average and standard deviation, for the entire overnight sleeping period, of the AI-based features extracted from each node in the flattening layer of the CNN model during the whole sleep period .There have been ongoing studies about risk factors for CVD after the 2000s, so we referred to previous clinical literature to extract 10 CVD risk factors to use as inputs into the prediction model .t-test and Chi-square tests were conducted for the training set, and we determined that each feature between classes was significantly different for p-value < 0.05 [To select the optimal features for CVD classification, we confirmed for any significant difference between classes in the 58 features. For statistical analysis, depending on data type, two independent sample e < 0.05 . Additioe < 0.05 . ThroughAfter the process of feature extraction, we use the SVM model to predict the occurrence of CHD, HF, and stroke within ten years. First, when classified as CVD after passing through the SVM_CVD that predicts CVD and CVD-free, the OvO multiple class classifiers are gone through to classify CHD, HF, and stroke. OvO is the method of selecting a combination of two classes out of the K existing target classes and then selecting the class that acquired the most discrimination through K(K-1)/2 binary classifications . In thisThis study proposed a prediction method of CVD that occurs within ten years for patients with SDB. The purpose of our algorithm is to recognize the risks of SDB, assist active treatment of it, and prevent the CVD, which is a comorbidity of SDB.In this study, we extracted a total of 58 feature vectors and selected the optimal feature vector through statistical analysis and SVM-RFE. Among the feature vectors selected with the optimal features, we confirmed the AI-based features that were proposed in this study showing excellent performance. In addition to the conventional CVD predictors, the new AI-based parameters showed a possibility to be used as a meaningful CVD predictor. Selected features were inputted into the SVM model to predict CVD, and four SVM models were designed for classifying CVD-free, CHD, HF, and stroke.We evaluated the prediction performance of CVD and CVD-free in the test set depending on the severity of the SDB symptoms as well as gender. The F1-score was 73.9% in women, and 79.1% in men, so it could be confirmed that the prediction performed better in men than in women. Gottlieb et al.  analyzedTo learn about the difference between our proposed method and the conventional method, we experimented and presented the results in Therefore, we confirmed the excellence of the CVD prediction model for SDB, proposed in this study. We also presented a possibility of CVD prediction that may occur within the next ten years in the patient with SDB. Thus, we believe that the algorithm proposed in this study can be used to, recognize the risks of SDB, assist aggressive treatment, prevent the CVD, comorbidity of SDB.In this study, we extracted the signal processing-based ECG features, AI-based ECG features, and clinical CVD risk factors. We also selected the optimal feature by using statistical analysis and SVM-RFE. Among the optima features, the AI-based ECG features were confirmed to have excellent performance in predicting the incident CVD outcomes. However, this study did not investigate how each feature is related to and CVD clinically. to confirm this, studies are needed that apply explainable AI technique to explain the meaning of each feature.In this study, a method for predicting the occurrence of cardiovascular disease using a single electrocardiogram and major clinical indicators was presented. In addition, meaningful results were obtained by extracting new AI-based features. However, although classification of diseases is important, risk assessment for cardiovascular disease is more meaningful in clinical practice, and additional research is needed for this.In patients with SDB and accompanying CVD, the mechanisms are increased oxidative stress and sympathetic nervous system activation . For the


The present study aimed to design and validate a questionnaire to investigate students' perceptions of learner autonomy in the context of Vietnamese tertiary education. The questionnaire was adapted from various well-established scales in the literature, and then the back-translation method was used to produce a version in Vietnamese. After the questionnaire development process, 1,565 non-English majors at seven different tertiary institutions in Vietnam voluntarily participated in the study and completed the questionnaire. Evidence of reliability and validity was provided for the instrument using SPSS Version 24, SmartPLS 3, and SPSS AMOS. Reliability was evaluated using Cronbach's alpha, composite reliability (CR), rho_A value, and average inter-item correlations. Validity was substantiated using Messick's framework of validity (1995). This entailed five different aspects: content, substantive, structural, external, and consequential. The results indicated that reliability reached adequate values and the aspects of validity were mostly confirmed. The questionnaire, therefore, was suited to exploring how students perceive learner autonomy, but it requires more validation for future use in the other contexts. learner autonomy, questionnaire, validation, perception, English language learning (ELT). This entails a capacity and willingness to act independently and in cooperation with others as a socially responsible person” , p. 1, wr market . LA itser market  and dever market . Specifir market , p. 1, wr market . Neverthr market  and on sr market . Those s2To conceptualize LA, the theoretical underpinning of this study has its roots in the Bergen definition aforementioned. There are three main reasons to choose this definition.Firstly, it is in line with The present study stands on the pole of the quintessential strategy which focuses on analyzing components of a construct , the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), the normed fit index (NFI), the root mean square residual covariance (RMS_theta), and the standardized root mean square residual (SRMR). It is advisable for researchers to calculate and report different fit indices in their studies because of the lack of global agreement on the acceptable values of those indices (2 highlights the inappropriateness of the proposed model to the sample data (2/d.f.); as a result, the researchers arrive at a normed Chi-square value  and confirmatory factor analysis (CFA) were employed to evaluate this aspect of validity. EFA enables researchers to examine the relationships between latent variables and observed ones. There are several factors in EFA that call for research attention. They are the Kaiser–Meyer–Olkin measure of sampling adequacy and Bartlett's test of sphericity, extraction, rotation, variance, and particularly, results in parallel analysis (PA). PA developed initially by  indices . In thisple data . Howeverple data , so Chi-re value . It shoure value  and lessre value . SRMR rere value  or 0.08 re value . RMSEA “l of 95% , p. 15. l of 95% . RMS_thel of 95% , and Henl of 95% . Teo et l of 95% ; howeverl of 95% . In thisThe external aspect refers to convergent and discriminant evidence. The former demonstrates the degree to which items are related to each other, and it is confirmed by average variance extracted (AVE), factor loadings, and CR which is calculated by the total amount of true scale variance divided by the total variance scale score . CR usesAfter we obtained the revised version of the questionnaire, internal consistencies in the instrument and each scale were examined with Cronbach's alpha. Also, we employed CR to analyze reliability. Additionally, reliability was assessed by rho_A, which evaluates the weight of the constructs, not their loadings, and is remarked by “the off-diagonal elements of a latent variable's indicator correlation matrix are reproduced as well as possible in a least squares sense” , p. 300.44.1A sample of 1,565 university students in total were voluntarily recruited from seven universities in Hanoi, Vietnam. Those students were learning English as a minor part in their curriculum and studying the following subjects: information technology (IT) , economics , civil engineering , electrical and electronic engineering , mechanical engineering , law , and various other fields .The sample consisted of 62% students in their second year (n = 971), 23.7% third-year students (n = 371), 11.9% students in their fourth year (n = 186), and 2.4% fifth-year students (n = 37). The respondents reported an average of over 11 years of formal instruction in English . Among the students, 62.2% were male (n = 974), and 37.8% were female (n = 591). As regards students' place of residence within Vietnam, Hanoi is the most popular city, with 28.7% of the students coming from there (n = 449), followed by Nam Dinh Province  and Thai Binh Province . The participants hailed from 34 out of 64 provinces in Vietnam, thus showing a geographic diversity. They have studied English at universities for at least one semester in order to make sure that they were more familiar with and experienced in the tertiary language education environment than their peers in their first year. This enabled them to reflect on themselves and provide in-depth information on their learner autonomy. The participants’ diverse demographic background information might enable the researchers to improve the generalizability of the results to a broader population . The desAs indicated by the literature e.g. , EFA and4.2To develop the questionnaire, we consulted the relevant literature and critically investigated the accredited questionnaires. We borrowed and modified items from frequently used questionnaires that have been established with psychometrically sound properties. This is an important step to generate items, thus aiding in the improvement of the validity and reliability evidence . After tstrongly disagree) to 5 (strongly agree). The items were arranged in random order, instead of a scale-by-scale order, so that the data collected could be more objective and not scale-oriented.The items and their sources can be found in Appendix B. The questionnaire employed a five-point Likert scale ranging from 1 (beliefs about teacher's role (BTR) scale contains eleven items adapted from motivation (M) scale. We added item 65 to refer to one dimension of motivation to learn English. The others were adapted from desire (D) scale consists of nine items, which were all adapted from Metacognitive knowledge (MK) covers metacognitive knowledge about self (MKS), language (MKL), context (MKC), and the learning process (MKP). MKS consists of eight items from metacognitive knowledge scale. There are three metacognitive skills . Metacognitive skill – planning (MSP) consists of seven items from Metacognitive skill – monitoring (MSM) has ten items which were adapted from freedom (F) scale contains ten items which were all adapted from The 4.3After the survey was established, it was revised through discussions with our research team whose discussions, comments, and feedback were very helpful and contributed a great deal to the development of the survey. Back-translation method was used to enable the particpants to understand the questionnaire . In addition, the Kaiser–Meyer–Olkin measure of sampling adequacy showed that the strength of the relationships between variables was “marvelous” to proceed with the analysis (KMO = .933)  , p. 112. = .933) , 2010. B2/d.f. SmartPLS3 brought us with SRMR, RMS_theta, and NFI. Overall, the fit of the five-factor, 40-item model was not entirely satisfactory . Specifically, although Chi-square statistics, p value, SRMR, RMSEA, and RMS_theta suggest a reasonable fit to the students' responses, NFI, TLI, and CFI were slightly lower than the recommended value of 0.9. The standardized five-factor model is illustrated in After iterative EFA using principal axis factoring and double oblimin due to the assumption of correlations among factors , 47 itemThe external aspect of validity for the LAPQ was demonstrated by convergent and discriminant evidence. Convergent validity is confirmed by factor loadings, AVE, and CR. The statistics showed that all the items tested had acceptable factor loadings ranging from 0.407 to 0.774. AVE values of the scales ranged from 0.368 (BTR) to 0.502 (M&D). M&D, which achieved a high level of CR (0.834), had acceptable AVE values, which were more than 0.5, and the AVE for the other scales was below 0.5. However, the CR values for these scales were very high from 0.764 to 0.915. Therefore, it can be stated that convergent validity was established see .Table 4CDiscriminant validity was indicated by three distinguishable criteria: the Fornell–Larcker criterion, cross-loadings, and the HTMT ratio. The data analysis on SPSS 24.0 and SmartPLS 3 showed that, as regards the first criterion, the square roots of the scales’ AVE were higher than those correlations of the scales with each other see .Table 5FWith regard to the second criterion, all the scales had better correlations with themselves than with other scale variables (see Appendix D). In terms of the third criterion, HTMT ratios were all below 0.9 see . Three pThe consequential aspect of validity emphasizes the score interpretation and potential consequences of LAPQ scores. We worked on the descriptive statistics and inferential statistics from the items to investigate how LA was perceived by the participants.5.2The internal consistency  of the 40 items affirmed that the questionnaire had achieved excellent reliability, α = 0.902. The analyses of reliability are summarized in metacognitive skills field possessed the highest reliability level . The MK scale had questionable alphas and rho_A . However, CR achieved a good value of 0.764, so the MK scale's reliability level was adequate. The average inter-item correlations among the final sub-scales ranged from 0.205 to 0.408 . The to 0.408 , and thato 0.408 . Among 46This study examined the reliability and validity aspects of the questionnaire, which aimed at investigating non-English major tertiary students’ perceptions of LA in language learning in a sample of Vietnamese students. The questionnaire development started with a critical review of the relevant literature, followed by a careful selection of items and an addition of appropriate items. The literature review enabled us to develop an operational definition of LA in this study. Despite the existence of a variety of definitions of LA, a specific definition would help researchers to clarify exactly what is developed and measured . AutonomIn terms of validity, five aspects of validity from After the validity analyses, the revised 40-item version of the questionnaire demonstrated an excellent level of overall Cronbach's alpha. All the scales had very good CR values as well as average inter-item correlations, good and acceptable values of internal consistency, and rho_A reliability. The evidence of discriminant and convergent validity as well as the average inter-item correlations contributed to strengthening the theoretical relationships which were positive among the components of LA in a model of structural and functional dynamics , 2018.In conclusion, after the revisions, the new version of the questionnaire with 40 items achieved a solid level of reliability, and the majority of aspects of validity were tested in the current study, suggesting that it can be used to investigate students’ LA in tertiary language education.7The current study was devoted to establishing a survey of LA and notably to scrutinizing the reliability and validity of the LAPQ among non-English major undergraduates. On the whole, the LAPQ can be employed instantly by researchers and teachers or lecturers for two reasons. Firstly, the LAPQ showed sufficient evidence of reliability and validity, notwithstanding the fact that the hypothesized model of five factors was not properly confirmed. Hence, we urge future researchers to iterate as many validations as possible to see if the reliability and validity patterns, especially the model fit, are the same across various populations. Secondly, the LAPQ may yield useful results for some aspects of language learning processes for both educators and students so that they can reflect on themselves and make any improvements that may be necessary.8The present research has several implications for future studies. A specific operational definition offered was twofold. Firstly, we were able to develop the research instrument and validate it throughout the project. Secondly, the definition may serve as a useful reference for further studies on LA because it seemed to cover quite a few aspects of LA. Moreover, as an accurate method in factor analysis, PA which has not been widely used was employed in this study to accommodate retention of components and it is suggested that future studies can use PA more for analyzing factors. The study also contributes to the literature by validating a newly compiled questionnaire to explore LA among university students in a Vietnamese sample. The findings provide the first psychometric or validational evidence for the five-factor, 40-item LAPQ to examine students' perceptions of LA from different aspects in an Asian sample. Future research should build on the current findings to revalidate the scales for a more psychometrically sound questionnaire and to empirically explore the relationships among the sub-scales constituting the operational definition. Studies should be replicated to find out how it works in other contexts. More importantly, the study also provides researchers, educators, and teachers with a practical survey tool to attain a comprehensive overview of students' LA, which is seen to be a key to success at college and in life . Then, t9There exist some caveats in this study which call for further attention. The first is the proposed model of LA in this study, which did not show sufficient goodness-of-fit. This could be due to a failure to estimate the direct relationships between factors and items that led to model misspecification. Another possible explanation was that the scales should be validated in different contexts with various populations .This work was supported by the The data that has been used is confidential.The authors declare no conflict of interest.No additional information is available for this paper.


Few previous studies have focused on early-stage cancer patients. Therefore, this research study aimed to explore BF and its influencing factors for early-stage cancer patients in China. (2) Methods: From April to August 2019, 319 patients with early-stage cancer in the treatment period were selected by the convenience sampling method and evaluated using the Benefit Finding of Cancer Patients Scale-Chinese (BFS-C), Perceived Social Support Scale (PSSS), and Medical Coping Modes Questionnaire (MCMQ). (3) Results: The mean BF score was 47.57 (SD = 12.26). The results of the correlation analysis show that benefit finding was positively correlated with social support, but negatively correlated with acceptance-resignation. In addition, social support was negatively correlated with avoidance and acceptance-resignation. The results of the multiple linear regression indicate that the variables of self-assessment of disease severity, exercise time, coping mode (acceptance-resignation), and social support, affect BF. Finally, social support was shown to exert an intermediary effect on acceptance-resignation and BF. (4) Conclusions: In this study, the score of BF of patients with early-stage cancer was low. Medical staff should be more aware of the health behavior of patients with early-stage cancer, guide them to actively face the disease, and fully mobilize the social support of patients’ friends and family, so as to help patients increase their disease BF. China has the highest rate of cancer in the world. It was reported that there were 19.29 million new cancer cases worldwide in 2020, including 4.57 million new cancer cases in China, accounting for 23.7% of the global new cancer cases , in turnAs one of the most influential research fields in positive psychology, benefit finding (BF) is becoming increasingly valuable among cancer patients. This term refers to a process of positive changes or benefits perceived by patients based on their illness experience, including personal, social, spiritual, and psychological cognitive and behavioral positive responses ,9,10. BFStudies on disease-related factors and BF have shown that the later the pathological stage of cancer is, the lower BF will be . LechnerAlthough research on BF has been gradually carried out in China in recent years, the current research is still in the primary stage. These studies have mostly focused on breast cancer patients, and there have been few studies performed on early-stage cancer patients. At the same time, due to the differences between Chinese and Western culture, foreign research results may not be completely suitable for Chinese patients. Therefore, investigating the characteristics and influencing factors of BF of Chinese early-stage cancer patients will aid clinical medical staff in better determining the psychological adaptation of patients, so that they may take early intervention measures to promote the healthy behavior of patients and improve their quality of life.This cross-sectional study design was conducted to explore BF and its affecting factors on early-stage cancer among Chinese patients.In this study, patients hospitalized in a third-grade class-A cancer hospital in China from April 2019 to August 2019 diagnosed with early-stage cancer and willing to participate in this study were selected as the research objects. The inclusion criteria were as follows: (1) age ≥ 18 years old; (2) primary school education and above; (3) patients with stage Ⅰ to Ⅱa (TNM stage) malignant tumors diagnosed by pathological examination; (4) patients with cancer diagnosed for 6 weeks to 1 year; and (5) patients with cancer after surgery or/and radiotherapy and chemotherapy, without significant progress of the disease at the time.The exclusion criteria were as follows: (1) those who could not undergo investigation due to physiological, psychological, or emotional reasons; (2) people with communication difficulties or cognitive impairments; (3) patients with cancer metastasis or other history of cancer; and (4) people who were participating or had participated in other research studies.According to the principle that the sample size in the multiple regression equation should be 10 times the number of independent variables , in thisThis includes sociodemographic characteristics data  and disease-related data .The scale was adapted by Weaver et al. , then trThe scale was prepared by Blumenthal et al. . It is uThe present study used this scale to assess the coping characteristics of early-stage cancer patients as a specific life event. The questionnaire was prepared by Feifel , and theThe survey was conducted in the respiratory department, digestive department, gynecology department, head and neck tumor, and other wards of the inpatient department of the hospital. First, the clinical oncologist reviewed the medical records of the patients, obtained information regarding the disease diagnosis and pathological reports, and preliminarily selected the research subjects that met the inclusion criteria. Next, the researchers introduced the purpose and significance of the investigation to the patients, informed them of anonymity, confidentiality, and voluntariness, and obtained their consent by having them sign the informed consent form. After the patients had completed the relevant diagnosis and treatment measures on the same day, the researchers separately distributed the questionnaire to them and used unified guidelines to guide them in filling out the forms independently. The questionnaire was distributed and recovered on site.The data analysis was performed using the SPSS 26.0 software. Statistical significance was set at 0.05. Descriptive statistics were conducted to describe the sociodemographic characteristics of patients by means and standard deviations (SDs) and frequencies. Multiple linear regression analysis was used to explore the affecting factors of BF . PearsonThis study was approved by the Research Ethics Committee of Health Science Centre, Shenzhen University. Before recruitment, a data sheet was distributed to all eligible patients to describe the purpose and process of the study. Prior to data collection, each participant gave their informed consent. Participants were informed that they could back out of the study at any time, and all collected data were processed anonymously and confidentially.The mean score of BF of 319 patients was 47.57 (SD = 12.26), and the average score of each item was 2.56 (SD = 0.56). Among the six dimensions, the dimension with the lowest average score of items was “healthy behavior”, which was 2.05 (SD = 0.64). See p = 0.045), self-rated disease severity , and daily exercise time  were statistically significant  and 115 were male (36.1%). Middle-aged people aged 40–59 accounted for 48.6%, those with college degrees or above accounted for 38.5%, married people accounted for 86.5%, and those who had a per capita monthly income of less than CNY 10,000 (about US 1600) accounted for 73.1%. A total of 77.7% of the patients rated their current severity of the disease as moderate or above. The main accompanying symptoms of the disease were anorexia (34.2%), poor sleep (42.6%), and pain (32.0%). All of the variables were normally distributed at the overall level of BF. The results of the univariate analysis reveal that the three variables of education  and negatively correlated with acceptance-resignation . In addition, social support was negatively correlated with avoidance  and acceptance-resignation .The results of the Pearson correlation analysis  show thap < 0.1) were selected as independent variables (see p < 0.05), which could jointly explain 63.3% of the total variation. The results are shown in In order to further clarify the degree of impact of each variable on BF of the cancer patients, according to the univariate analysis result, the variables with statistically significant correlation or difference (β = −0.657 (p = 0.004) and R = 0.356. In addition, when using social support as the dependent variable and acceptance-resignation as the independent variable for regression analysis, then β = −0.392 (p = 0.005) and R = 0.441. Finally, when using BF as the dependent variable and acceptance-resignation and social support as the independent variables for regression analysis, then β = −0.413 (p < 0.001) and R = 0.316. These results show that when social support variables were added to the regression equation, then the absolute value of the regression coefficient of acceptance-resignation in BF decreased (from −0.657 to −0.413). This reveals that social support plays a partial mediating role in the prediction of the acceptance-resignation dimension of BF , and thThis study also found that the average score of the dimension of healthy behavior was the lowest (2.05 ± 0.64), thus indicating that patients with early-stage cancer were not sufficiently active in improving their bad living habits, establishing a healthy diet and regular exercise, and having a healthier lifestyle. It is suggested that medical staff pay more attention to the health behavior of patients with early-stage cancer, guide them to eat and exercise healthily, and promote the development of a healthy lifestyle.This study found that there was a positive correlation between self-perceived disease severity and BF, which is consistent with the results of other research studies . DiseaseThis study found that patients who exercised more than 1 h a day were positively correlated with BF. The reason for this may be that exercise is the main influencing factor in promoting patients’ social adaptation. The psychosocial adaptation level of patients who often exercise is also high . ExercisThe way of response refers to cognitive and behavioral approaches that individuals adopt under frustration and stress , and a pThe results of this study show that there is a positive correlation between social support and BF. The higher the degree of social support is, the higher the level of BF will be. Other studies performed in China have confirmed that social support can improve the social adaptability of cancer patients and urge them to apply positive strategies to deal with problems ,38,39,40In this study, the BF level of early-stage cancer patients was shown to be low, and patients’ self-perception of disease severity, daily exercise time, social support, and coping style were observed to be the main factors affecting BF. Through the above factors, clinical medical workers can estimate the BF level of patients, identify patients with a low BF level as soon as possible, and take corresponding intervention measures. By providing high-quality humanistic care, clinical medical workers can encourage patients to actively face the disease and engage in appropriate physical exercise, guide patients’ families and society as a whole to offer the patients more support and care, and instruct patients to perceive the disease from a positive perspective, so as to improve their quality of life.Due to the limitations of time and funding, this subject survey was only conducted via a cross-sectional survey from a time point after cancer patients fell ill; thus, it is not possible to grasp the dynamic change in BF over the course of the disease. In addition, due to the limitations of cross-sectional research, hybrid research methods must be adopted in the future to explore the relevant mechanisms and motion trajectories of BF in early-stage cancer patients, so as to better guide and serve clinical practice.


Salmonella enterica serovar Chester infections increased temporarily between 2014 and 2016. Concurrently with this temporal increase in the Kanto region, S. Chester isolates belonging to one clonal group were causing repetitive outbreaks in Europe. A recent study reported that the European outbreaks were associated with travelers who had been exposed to contaminated food in Morocco, possibly seafood. Because Japan imports a large amount of seafood from Morocco, we aimed to establish whether the temporal increase in S. Chester infections in the Kanto region was associated with imported Moroccan seafood. Short sequence reads from the whole-genome sequencing of 47 S. Chester isolates from people in the Kanto region (2014–2016), and the additional genome sequences from 58 isolates from the European outbreaks, were analyzed. The reads were compared with the complete genome sequence from a S. Chester reference strain, and 347 single nucleotide polymorphisms (SNPs) were identified. These SNPs were used in this study. Cluster and Bayesian cluster analyses showed that the Japanese and European isolates fell into two different clusters. Therefore, PTΦ and dN/dS ratio indicated that the Japanese strain has evolved under positive selection pressure. Generally, a population of bacterial clones in a reservoir faces negative selection pressure. Therefore, the Japanese strain must have existed outside of any reservoir during its emergence. In conclusion, S. Chester isolates originating from one clone probably emerged in the Kanto region via the consumption of contaminated foods other than imported Moroccan seafood. The emerging strain may have not established a reservoir for survival in the food supply chain resulting in its disappearance after 2017.In Japan’s Kanto region, the number of  Salmonella enterica serovar Chester infections increased temporarily during 2014–2016 in the Kanto region of Japan. We reported that an outbreak in 2016 from an unknown infection source was caused by S. enterica subsp. enterica serovar Chester in a part of the aforementioned Japanese region  showed that all 10 of the isolates had almost identical band patterns  is a significant cause of foodborne illness worldwide  has been imported from Morocco  O121 isolates with similar PFGE patterns were isolated in various Japanese regions. Analysis of single nucleotide polymorphism (SNP) extracted from WGS data showed that only some of EHEC O121 isolates were derived from a single clone, while the WGS data showed that the remaining isolates were not from this clone  data compared with PFGE data. PFGE has generally been used to determine whether bacterial isolates originate from the same outbreak. However, PFGE can only analyze nucleotide differences between isolates at a relatively small number of restriction enzyme sites . Therefois clone . The supis clone  and S. AS. Agona .S. Chester infections in the Kanto region of Japan was associated with imported seafood consumption from Morocco. First, we performed molecular epidemiological and population genetic analyses using WGS data to elucidate the relationship between S. Chester isolates from the Kanto region and those from Europe. Second, we investigated whether a correlation exists between the genetic and geographical distances of the Japanese isolates using a molecular epidemiological analysis to confirm that the Kanto region’s temporal increase occurred via contaminated food. Third, our WGS data was used to assess whether selective pressure featured in the outbreaks involving S. Chester. We then discuss why the S. Chester isolates emerging in the Kanto region during 2014–2016 were not isolated after 2017.Therefore, the aim of this study was to clarify whether the temporal increase in Salmonella Chester isolates isolated in the Kanto region (47 isolates) during 2014–2016 were used in this study  that were collected from humans, chickens and food in the multinational European outbreaks were obtained in a previous study (PRJNA248792). The short sequence reads from the European isolates were kindly provided by the Institute Pasteur (France) to the National Institute of Infectious Diseases (NIID), Japan.The short sequence reads from 58 European us study . These rS. Chester isolates from the Kanto region of Japan were obtained by the NIID. DNA was extracted from the Japanese isolates and purified using the Isoplant II DNA extraction kit . The genomic DNA libraries were prepared using the Nextera XT DNA sample prep kit (Illumina). The pooled libraries were subjected to multiplexed paired-end sequencing (300 mer × 2) using MiSeq . The adapter sequences were trimmed from the short reads, and low quality bases with Phred scores less than 15 were eliminated using the Skewer program  as a reference  based on the analysis of seven housekeeping genes  (S. Chester ATCC 11997 strain (Accession number CP019178.1) were 1,427,763–1,428,848 for aroC, 1,406–2,506 for dnaN, 4,565,282–4,566,022 for hemD, 1,788,720–1,790,024 for hisD, 1,788,720–1,790,024 for purE, and 3,104,010–3,106,100 for sucA, and 3,872,960–3,873,009 for thrA. The European isolates were classified as belonging to ST1954 based on the MLST analysis in our previous study (To perform multilocus sequence typing (MLST) on the Japanese isolates, the assembled sequence contigs from the A5-MiSeq pipeline  were ana4 . The MLSnd thrA) . The chrus study .Cluster analysis was conducted using MEGA 7 software . The 347K) to show how the isolates could be grouped, delta K was calculated using STRUCTURE Harvester Ver. 0.6.93 software  to the variance expected for a state of linkage equilibrium (Ve), scaled by the number of loci used in the analysis (l), from:Linkage disequilibrium in the Japanese and European isolates was evaluated using the standardized index of association (IAS) . LIAN VeVD and Ve was determined by the Monte Carlo simulations with 103 resamplings.The significance between A root-to-tip analysis was performed using Path-O-Gen software to evaluate whether changes occurred in the diversity of the Japanese and European isolates as time proceeds . In the PN) and synonymous (PS) substitutions in the amino acids were compared to evaluate the selection pressure on each of the Japanese and European isolates as follows  analysis was performed using PopART software Version 1.7  to evalumutS, mutL, mutH, and uvrD) within the mismatch repair system (S. Chester strain ATCC 11997 (Accession number CP019178.1) were 956,162–958,729 for mutS, 4,121,661–4,123,517 for mutL, 3,712,806–3,713,201 for mutT, and 4,551,149–4,553,311 for uvrD. When at least one SNP was located in these genes, we examined whether the SNP caused either non-synonymous or synonymous substitutions.The presence of SNPs in four genes  were exactly the same among the Japanese and European isolates. This MLST result is consistent with no SNPs being detected in these seven genes among the Japanese and European isolates, as determined by mapping the short sequence reads from these isolates against the reference genome sequence  and cluster B  was 0.327 , one of which only contains the Japanese isolates (cluster A), while the other contains only the European isolates (cluster B) . We founas 0.327 . The paiK value was obtained when a population was partitioned in two sub-populations  = −3479.8. In the third run, when the isolates were ordered according to their posterior probability values (Q)  , they folues (Q) . The Q vPTΦ value. The value was 0.700 (P = 0.001), indicating that the two groups belonged to different populations. Furthermore, the The division of the Japanese and European isolates into these two groups was supported by the han zero , indicatS. Chester isolates (excepting isolate 29149) increased as time advanced after the isolates originated from a single clone. In contrast, no such tendency was found when the European isolates were analyzed  (The pulation . No SNPsnd uvrD) .S. Chester infections in the Kanto region of Japan was related to the S. Chester isolates spreading continuously in Morocco. The cluster and Bayesian cluster analyses showed that the Japanese and European isolates fell into two different clusters; thus, it is possible that the Japanese and European isolates are genetically distinct populations. Therefore, population genetic analysis was performed using PTΦ and S. Chester isolates belonging to the same clonal group had spread continuously in Morocco and that the spreading isolates  have caused outbreaks in Europe via Moroccan seafood. Therefore, it is likely that the temporary increase of S. Chester infections in Japan was caused by an infectious source other than the seafood imported from Morocco.Our results refute the hypothesis that the temporary increase in S. Chester isolates originating from one clone emerged in the Kanto region via contaminated food items, which differed from the infection source of the European outbreaks. In general, when bacterial strains emerge via person-to-person contact, genetic distances in the strains are positively correlated with the geographical distances among the places where the strains are isolated  acting on the isolates may explain why the Japanese S. Chester strain was not isolated after 2017. In general, the population of bacterial clones in a reservoir of persistent infection is subject to negative selection pressure, and the pressure excludes some clones with mutations that differ from the population  can be found in the article/S. Chester from human stools, and identified the isolates as S. Chester. TS, YM, NJ-D, SL, KM, and MK performed WGS on the S. Chester isolates. TS, NJ-D, SL, and MK processed and analyzed the WGS data. NA, TS, EY, MK, and SH performed molecular epidemiological and population genetic analyses using the SNPs extracted from the WGS data. KI, KN, MS and HK assisted with project development and data analysis. NA, EY, KM, and SH wrote the draft manuscript. All authors have commented on and agreed upon the final manuscript.NA, EY, KM, and SH conceptualized and designed this study. NA, EY, YA, NK, YM, and SF isolated The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.