Effect of metacognitive therapy on depression in patients with chronic disease: a protocol for a systematic review and meta-analysis.

BACKGROUND: Chronic diseases have a high prevalence worldwide, and patients with chronic diseases often suffer from depression, leading to a poor prognosis and a low quality of life. Metacognitive therapy is a transdiagnostic psychotherapy intervention focused on thinking patterns, with the advantages of reliable implementation effect, short intervention period and low cost. It can help patients change negative metacognition, alleviate depression symptoms, and has a higher implementation value compared with other cognitive interventions. Therefore, metacognitive therapy may be an effective way to improve the mental health of patients with chronic diseases. METHODS AND ANALYSIS: CNKI, Wanfang Database, VIP Database for Chinese Technical Periodicals, Sinomed, PubMed, SCOPUS, Embase, The Cochrane Library, Web of Science and PsycINFO will be used to select the eligible studies. As a supplement, websites (eg, the Chinese Clinical Registry, ClinicalTrials.gov) will be searched and grey literature will be included. The heterogeneity and methodological quality of the eligible studies will be independently screened and extracted by two experienced reviewers. All the data synthesis and analysis (drawing forest plots, subgroup analysis and sensitive analysis) will be conducted using RevMan 5.4.1. ETHICS AND DISSEMINATION: This article is a literature review that does not include patients' identifiable information. Therefore, ethical approval is not required in this protocol. The findings of this systematic review and meta-analysis will be published in a peer-reviewed journal as well as presentations at relevant conferences. PROSPERO REGISTRATION NUMBER: CRD42023411105.

The relationship between social frailty and loneliness in community-dwelling older adults: a cross-sectional study.

BACKGROUND: Social frailty (SF) is associated with multiple adverse health outcomes, yet there has been an inadequate focus on social frailty. The convoy model portrays the social networks through the perspective of the life course, thus providing a framework to explain the occurrence of social frailty. This study aimd to figure out the prevalence of social frailty and loneliness among community-dwelling older adults and to explore their correlations based on convoy model. METHODS: This was a cross-sectional study, and 295 older adults from 10 communities of Zhengzhou in Henan Province participated in the study. Social frailty and loneliness were assessed separately with the Social Frailty Scale and University of California at Los Angeles-Loneliness Scale. The scores of social frailty of the older adults in different characteristic communities were compared by independent sample t-test and single factor analysis of variance. The influencing factors of social frailty were analysed by multiple stepwise linear regression and the structural equation model. The correlation between social frailty and loneliness was analysed by Pearson correlation analysis. RESULTS: The total scores of social frailty and loneliness of the older adults in the community were (2.09 ± 1.53) and (43.19 ± 8.91), respectively. There was a moderate positive correlation between social frailty and loneliness (r = 0.621, P < 0.01). The results of multiple stepwise linear regression analysis showed that age, living styles, balance of payments, and loneliness were the main influencing factors of the social frailty of older adults in the community (F = 27.180, P < 0.001). The structural equation model of social frailty fitted well (χ2 = 47.292, df = 26, χ2/df = 1.819, P = 0.007; RMSEA = 0.053, 95%CI (0.028, 0.076), P = 0.359; GFI = 0.971; AGFI = 0.939; NFI = 0.904; IFI = 0.955; TLI = 0.918; CFI = 0.953; SRMR = 0.0466). CONCLUSIONS: The convoy model had certain applicability in explanation of the relationship between loneliness and social frailty among older adults in community. The incidence of social frailty among the older adults in the community was high, and loneliness was at a medium level. It is necessary to strengthen the intervention of social frailty and loneliness of the older adults in the community, improve the quality of life of the older adults, and promote the development of healthy aging.

Evaluating the Generalization Ability of Super-Resolution Networks.

Performance and generalization ability are two important aspects to evaluate the deep learning models. However, research on the generalization ability of Super-Resolution (SR) networks is currently absent. Assessing the generalization ability of deep models not only helps us to understand their intrinsic mechanisms, but also allows us to quantitatively measure their applicability boundaries, which is important for unrestricted real-world applications. To this end, we make the first attempt to propose a Generalization Assessment Index for SR networks, namely SRGA. SRGA exploits the statistical characteristics of the internal features of deep networks to measure the generalization ability. Specially, it is a non-parametric and non-learning metric. To better validate our method, we collect a patch-based image evaluation set (PIES) that includes both synthetic and real-world images, covering a wide range of degradations. With SRGA and PIES dataset, we benchmark existing SR models on the generalization ability. This work provides insights and tools for future research on model generalization in low-level vision.

Blind Image Super-Resolution: A Survey and Beyond.

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with unknown degradation, has attracted increasing attention due to its significance in promoting real-world applications. Many novel and effective solutions have been proposed recently, especially with powerful deep learning techniques. Despite years of efforts, it still remains as a challenging research problem. This paper serves as a systematic review on recent progress in blind image SR, and proposes a taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used to solve the SR model. This taxonomy helps summarize and distinguish among existing methods. We hope to provide insights into current research states, as well as revealing novel research directions worth exploring. In addition, we make a summary on commonly used datasets and previous competitions related to blind image SR. Last but not least, a comparison among different methods is provided with detailed analysis on their merits and demerits using both synthetic and real testing images.

Texture-based Error Analysis for Image Super-Resolution.

Evaluation practices for image super-resolution (SR) use a single-value metric, the PSNR or SSIM, to determine model performance. This provides little insight into the source of errors and model behavior. Therefore, it is beneficial to move beyond the conventional approach and reconceptualize evaluation with interpretability as our main priority. We focus on a thorough error analysis from a variety of perspectives. Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally. We then use this to determine (a) the semantic alignment of SR datasets, (b) how SR models perform on each label, (c) to what extent high-resolution (HR) and SR patches semantically correspond, and more. Through these different angles, we are able to highlight potential pitfalls and blindspots. Our overall investigation highlights numerous unexpected insights. We hope this work serves as an initial step for debugging blackbox SR networks.

AI-enabled image fraud in scientific publications.

Destroying image integrity in scientific papers may result in serious consequences. Inappropriate duplication and fabrication of images are two common misconducts in this aspect. The rapid development of artificial-intelligence technology has brought to us promising image-generation models that can produce realistic fake images. Here, we show that such advanced generative models threaten the publishing system in academia as they may be used to generate fake scientific images that cannot be effectively identified. We demonstrate the disturbing potential of these generative models in synthesizing fake images, plagiarizing existing images, and deliberately modifying images. It is very difficult to identify images generated by these models by visual inspection, image-forensic tools, and detection tools due to the unique paradigm of the generative models for processing images. This perspective reveals vast risks and arouses the vigilance of the scientific community on fake scientific images generated by artificial intelligence (AI) models.

Electricity-consumption data reveals the economic impact and industry recovery during the pandemic.

Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.

A Frailty Assessment Tool to Predict In-Hospital Mortality in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease.

Background: The exacerbation of chronic obstructive pulmonary disease (AECOPD) is a chronic, frequent, and life-threatening lung disease. In 2014, a frailty index (FI) based on deficits in commonly used laboratory tests (FI-Lab) was suggested to identify older adults at increased risk of death. Objective: We aim to study the prognostic value of the FI-Lab in older Chinese patients who were admitted because of AECOPD. Methods: We screened 1932 older patients hospitalized with AECOPD from September 2016 to June 2019 at Zhenjiang First People's Hospital, China. A multivariate logistic regression analysis was used to identify prognostic factors for in-hospital mortality. Results: A total of 77 survivors and 77 non-survivors were finally included in the study. Both the mean DECAF (including dyspnea, eosinopenia, consolidation, acidemia, and atrial fibrillation) score and the mean FI-Lab value of non-survivors were statistically higher than those of survivors (4.45 ± 0.80 versus 3.03 ± 0.90, P=0.000; 0.51 ± 0.13 versus 0.29 ± 0.10, P=0.000, respectively). Logistic regression analysis suggested that DECAF Rank and FI-Lab Rank were strongly related factors of death in AECOPD patients. The areas under the receiver-operating characteristic (ROC) curves were 0.906 for FI-Lab and 0.870 for DECAF (P=0.2991). Conclusion: FI-Lab is a simple, efficient, and objective tool to stratify the risk of in-hospital mortality of AECOPD.

Light chain modulates heavy chain conformation to change protection profile of monoclonal antibodies against influenza A viruses.

The isolation of human monoclonal antibodies with broadly neutralizing breadth can provide a promising countermeasure for influenza A viruses infection. Most broadly neutralizing antibodies against influenza A viruses bind to the conserved stem region or the receptor-binding cavity of hemagglutinin and the interaction is dominated by the heavy chain. The light chain, however, contributes few or no direct contacts to the antigen. Here we report an H3-clade neutralizing human monoclonal antibody, AF4H1K1, which recognizes the hemagglutinin glycoproteins of all group 2 influenza A viruses. This human monoclonal antibody has been obtained through the screening by pairing different heavy and light chains from an H7N9-infected patient based on the next-generation sequencing technology. Further structural studies revealed that light chains modulate the neutralizing spectrum by affecting the local conformation of heavy chains, instead of direct interaction with the antigen. These findings provide important clues to understand the molecular basis of light chains in antigen recognition and to explore the strategies in particular of the use of light chain modification to develop broadly protective monoclonal antibodies against influenza A viruses and other emerging viruses.

Efficient production and characterization of homopolymeric poly(3-hydroxyvalerate) produced by Bacillus strain PJC48.

Aliphatic polyester, poly(3-hydroxyvalerate) (PHV), is commonly produced as a granular component in bacterial cells of various species. Based on 16S rDNA gene sequence analysis, strain PJC48 was identified as a Bacillus species. The current study is aimed to screen for a high-yield strain that can produce PHV efficiently and to increase PHV product yield by optimizing the fermentative process. We identified a high-producer strain based on Nile red staining. Characterization of the PHV produced by PJC48 by nuclear magnetic resonance spectroscopy revealed that it consisted of (R)-3-hydroxyvalerate monomers. The suggested model was validated by response surface methodology. Optimization of the PHV yield resulted in an increase of 32.75% compared to control, with a maximum production of 1.64 g/L after 48 H.

Draft Genome Sequence of a Polydroxyalkanoate-Synthesizing Bacterium, Bacillus sp. Strain PJC48, Isolated from Activated Sludge.

The genome sequence of a Bacillus strain is capable of synthesizing polyhydroxyalkanoates, and Bacillus sp. is considered a platform strain for the production of many biodegradable materials. Here, we present the sequence of the PJC48 strain genome, which is composed of three chromatin structures, an extracellular structure, and a cytoskeleton.

ß-Defensin-2 is overexpressed in human vocal cord polyps.

The objective of the study is to investigate the expression of human ß-defensin-1 (hBD-1) and human ß-defensin-2 (hBD-2) in vocal cord polyps using tissue microarray. Tissue specimens from vocal cord polyps (N = 51), vocal cord nodules (N = 26), and healthy vocal cords (N = 8) were retrieved from the biobank of the Department of Pathology of Tianjin Tianhe Hospital between 2003 and 2006 and immunostained on tissue microarrays for the quantitative analysis of hBD-1 and hBD-2 expression. hBD-1 expression did not differ significantly between healthy vocal cords, vocal cord nodules, and vocal cord polyps (p = 0.904). In contrast, hBD-2 expression was significantly higher in vocal cord polyps compared to vocal cord nodules and healthy vocal cords (p < 0.001). The expression of hBD-2, but not hBD-1, is elevated in vocal cord polyp epithelium. This suggests that hBD-1 has a more constitutive role in host defense in the vocal cords, whereas hBD-2 expression may be a result of local inflammation or the presence of invading pathogens.

[Changes of plasma glucagon level in individuals with different glucose metabolism].

OBJECTIVE: To determine the levels of plasma glucagon and associated factors in individuals with different glucose metabolism. METHODS: One hundred and ten outpatient patients received an oral glucose tolerance test (OGTT) voluntarily. The patients were divided into three groups according to their OGTT results: normal glucose golerance (NGT) group (n=33), impaired glucose regulation (IGR) group (n=35), newly diagnosed type 2 diabetes (DM2) group (n=42). Plasma glucagon (GLC) and insulin (INS) at 0 min (0 h), 30 min (0.5 h), 60 min (1 h) and 120 min (2 h) were measured by radio immunity and electrochemiluminescence assay, respectively. We calculated the area of glucagon under the curve (AUCglc), the value of early phase glucagon secretion (deltaGLC = GLC(0.5 h) - GLC(0 h)) and the ratio of insulin to glucagon (INS/GLC). RESULTS: (1) There was no significant difference in the level of GLC(0 h) among the three groups (P > 0.05). (2) Patients in the IGR group had higher levels of GLC(0.5 h) and GLC(1 h), greater values of AUC(glc), and deltaGLC, and lower values of INS(0.5 h)/GLC(0.5 h) than those in the NGT group (P < 0.05). (3) Patients in the DM2 group had higher levels of GLC(0.5 h) and GLC(1 h), greater values of AUC(glc) and deltaGLC, and lower values of INS(0.5 h)/GLC(0.5 h) and INS(1 h)/GLC(1 h) than those in the NGT group (P < 0.05). (4) Patients in the DM2 group had higher levels of GLC(1 h), greater values of AUC(glc), and lower values of INS(0.5 h)/GLC(0.5 h), INS(1 h)/GLC(1 h), and INS(2 h)/GLC(2 h), than those in the IGR group (P < 0.05). (5) The multiple linear regression analysis showed that the value of AUC(glc). was positively correlated with HOMA-IR (adjust R2 = 0.219, P = 0.001). CONCLUSION: There are no significant differences in fasting plasma glucagon levels among the individuals with different glucose metabolism. Hypersecretion of glucagon and reduced glucagon suppressing effect of insulin occur in IGR patients, which is exacerbated when DM2 are developed. HOMA-IR is an important factor associated with glucagon hypersecretion.

Automatic kidney segmentation in CT images based on multi-atlas image registration.

Kidney segmentation is an important step for computer-aided diagnosis or treatment in urology. In this paper, we present an automatic method based on multi-atlas image registration for kidney segmentation. The method mainly relies on a two-step framework to obtain coarse-to-fine segmentation results. In the first step, down-sampled patient image is registered with a set of low-resolution atlas images. A coarse kidney segmentation result is generated to locate the left and right kidneys. In the second step, the left and right kidneys are cropped from original images and aligned with another set of high-resolution atlas images to obtain the final results respectively. Segmentation results from 14 CT angiographic (CTA) images show that our proposed method can segment the kidneys with a high accuracy. The average Dice similarity coefficient and surface-to-surface distance between segmentation results and reference standard are 0.952 and 0.913mm. Furthermore, the kidney segmentation in CT urography (CTU) and CTA images of 12 patients were performed to show the feasibility of our method in CTU images.

[Meta analysis of the efficacy and safety of combined treatment with ARB and ACEI on diabetic kidney disease].

OBJECTIVE: To evaluate the efficacy and safety of combined treatment with angiotensin II receptor blocker (ARB) and angiotensin converting enzyme inhibitor (ACEI) on diabetic kidney disease. METHODS: Randomized controlled trials (RCTs) were identified from CoChrane library, PubMed, EMbase, CNKI and VIP. Eleven RCTs involving 602 patients were included and analyzed with Rev Man 5.1 software. RESULTS: Compared with ACEI alone, combined treatment with ARB and ACEI was more effective on decreasing 24 h albuminuria, systolic pressure, average 24 h systolic pressure, diastolic pressure, and average 24 h diastolic pressure but with a high level of serum potassium. Compared with ARB alone, combined treatment with ARB and ACEI was more effective on decreasing systolic pressure and diastolic pressure. Compared with ACEI or ARB alone, we didn't get a definite conclusion that whether combined treatment with ARB and ACEI was more effective on decreasing 24 h proteinuria. CONCLUSION: Based on this Meta analysis, combined treatment with ARB and ACEI is safer and has positive effect on diabetic kidney disease. However, small sample size and low methodological quality appeared in most of the trials included in this systematic review. Therefore, available evidence is insufficient to recommend a routine clinical application of combined treatment with ARB and ACEI on diabetic kidney disease.

[Construction of adenoviral vectors expressing miR-30a and miR-30e].

OBJECTIVE: To construct adenoviral vectors expressing mature miRNA-30a and miRNA-30e. METHODS: The target mmu-miR-30a and mmu-miR-30e genes amplified from mouse genome were digested and linked to the shuttle plasmid pSES-HUS, which was then transformed into competent AdEaseier cells for recombination. The confirmed recombinant plasmids were transfected into Hek-293 cells for production of the adenoviruses pAd-mmu-miR-30a and pAd-mmu-miR-30e. The obtained adenoviruses were used to infect Mefs cells, and the cellular expressions of mmu-miR-30a and mmu-miR-30e were detected using fluorescence quantitative PCR. RESULTS: mmu-miR-30a (357 bp) and mmu-miR-30e (324 bp) containing the restriction sites were amplified and linked to the shuttle plasmid pSES-HUS, which was successfully recombined with AdEasy1. After packaging in Hek-293 cells, the adenoviral vectors were obtained, which caused an increase of mmu-miR-30a expression by 26.46∓7.46 folds and mmu-miR-30e expression by 2.76∓0.25 folds in transfected Mefs cells. CONCLUSION: We have successfully constructed the adenoviral vectors expressing the mature miRNAs.

[New perspectives in the treatment of diabetes by interfering with glucagon signaling pathway].

Glucagon, a hormone secreted from the alpha cell of the endocrine pancreas, is a major counterpart to insulin. After released into blood, glucagon will combine with its receptor in targeting tissues and form a compound, which then activates its signaling pathway, produces cAMP, promotes gluconeogenesis and glycogenolysis and inventually increases blood glucose. Researches recently display that it will be an important addition to treatment method by inhibiting synthesis and secretion of glucagon, neutralizing circulating glucagon, using glucagon receptor antagonists and prohibiting gene expression of glucagon receptor.