Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Prognostic Value of Elevated Plasma Galectin-3 for Renal Adverse Events in Dialysis Patients: A Systematic Review and Meta-Analysis.

Context: In prognostic research, Galectin-3 (Gal-3) has gained recognition in renal fibrosis and nephrosis for its characteristics of promoting inflammation and fibrosis. High levels of Gal-3 may function as a predictor of adverse outcomes for patients with end-stage renal disease (ESRD). Objective: The review intended to systematically examine the significance of Gal-3 in the forecast of adverse outcomes for dialysis patients, using a method of evidence-based medicine. Design: The research team performed a systematic narrative review and meta-analysis by searching the Excerpta Medica Database (EMBASE) and the PubMed, Cochrane, and Web of Science databases for pertinent studies published before June 1, 2022. The search contained both meshes and free terms, such as Galectin 3, Gal-3, renal dialysis, hemodialysis, peritoneal dialysis, HD, and PD. Setting: The review took place at First People's Hospital of Linping District in Hangzhou, China. Outcome Measures: The research team used the Newcastle-Ottawa Scale (NOS) for assessment of the quality of the included research. The team created two reports to assess the value of Gal-3 in prediction of risk: (1) one for studies using continuous variables and (2) one for studies using categorical variables, dividing patients into high- and low-level Gal-3 groups with a cut-off value of Gal-3, being Gal-3 < 10.5 ng/mL for the lower tertile, and Gal-3 ≥ 13.4 ng/mL for the higher tertile. The team performed the meta-analysis using Stata 15.0, analyzed publication bias using Egger's test and directly showed it in a funnel plot. Results: The search found 1061 publications, with eight studies with 5194 participants being included in the current review. For the continuous variables, Gal-3 was associated with all-cause risk of death-Hazard ratio (HR) 1.06, 95%CI 1.01-1.12, and P = .024-and cardiovascular (CV) events-HR 1.13, 95%CI 1.07-1.203, and P = .000, but no significant correlation existed between Gal-3 and risk of CV mortality-HR 1.07, 95%CI 0.99-1.16, and P = .091. For the categorical variables, a high level of Gal-3 was correlated with a high risk of dying, from all causes-HR 2.05, 95%CI 1.50-2.80, and P = .000. Conclusions: Clinicians can use Gal-3 as a standalone forecaster of all-cause mortality and CV events for hemodialysis patients because correlates with these outcomes. Further research is necessary to determine its predictive value for CV mortality. Investigators need to perform further research with a large sample size on the predictive value of Gal-3 for dialysis patients, particularly PD patients, from a variety of ethnic backgrounds to improve the precise treatment for high-risk patients.

Serum IL-17A concentration and a IL17RA single nucleotide polymorphism contribute to the risk of autoimmune type 1 diabetes.

AIMS: Interleukin (IL)-17 is associated with autoimmunity. This study aimed to affirm the role of IL-17A, IL-17F and single nucleotide polymorphisms (SNPs) in genes related to them and their receptors in autoimmune type 1 diabetes (T1D) for Chinese population. METHODS: In this study, 130 patients with autoimmune T1D and 140 non-T1D controls were included for analysis. Clinical and biochemical data were collected, and serum levels of IL-17A, IL-17F, IL-6, and high-sensitivity C reactive protein were measured using ELISA. The SNPs rs2275913, rs8193036, rs3819025, rs763780, rs879577, rs4819554, and rs708567 were genotyped using the SNaPshot assay. RESULTS: IL-17A levels were higher in patients with autoimmune T1D than in controls (median [IQR] 28.83[37.38] vs. 16.68[8.10], p < 0.001) and high IL-17A was a risk factor for autoimmune T1D (odds ratio (OR), 1.013; 95% CI, 1.003-1.023; p = 0.013) after adjusting for confounding factors. Linear regression analysis revealed that log10 IL-17A levels were independently associated with fasting C-peptide, IL-6, body mass index, and IL-17F. However, no independent association was found between IL-17F and autoimmune T1D. The GG genotype of SNP rs4819554 in the interleukin 17 receptor A (IL17RA) gene was associated with a decreased risk of autoimmune T1D (OR, 0.458; 95% CI, 0.246-0.852; p = 0.014) after adjusting for other confounders. The IL17RA rs4819554 GG genotype was negatively correlated with serum glutamic acid decarboxylase antibody appearance (p < 0.05). CONCLUSIONS: Increased serum IL-17A, but not IL-17F, is a risk factor for autoimmune T1D. The GG genotype of IL17RA rs4819554 might decrease the risk for autoimmune T1D.

High level of complement factor Ba within first prenatal test of gestation increases the risk of subsequent gestational diabetes: a propensity score-matched study.

OBJECTIVE: This study was to assess the alteration of circulating complement factor Ba (CFBa) within 11 to 17 weeks of gestation and its association with subsequent gestational diabetes mellitus (GDM) and its delivery outcome. METHODS: Biochemical parameters and blood samples were collected from 399 pregnant women within 11 to 17 weeks of gestation. At 24 to 28 weeks of gestation, all participants underwent 75-g oral glucose tolerance test and were assigned to GDM group (n = 80) and normal control group (n = 319). Perinatal data were collected after delivery. A propensity score-matched (PSM) analysis was performed to reduce the impact of confounding factors on glucose metabolism during pregnancy between the two groups. RESULTS: Two groups of 74 well-matched patients who maintained balance in terms of baseline characteristics. The levels of CFBa in pregnant women who later developed GDM were significantly higher than those in healthy pregnant women [0.4(0.1-0.8) vs. 0.2(0.2-0.3), p = 0.024]. Logistic regression analysis results confirmed that the level of CFBa was an independent impact factor for the occurrence of GDM (OR = 1.57, 95% CI: 1.118-2.210, p = 0.009). Further grouping according to the median level of CFBa, it was found that the incidence of GDM in category two (>0.23 ng/ml, n = 74) was markedly higher than that in the first category (≤0.23 ng/ml, n = 74) (p = 0.021). CONCLUSIONS: High level of the CFBa within 11 to 17 weeks of gestation increases the risk of subsequent GDM, and maybe a biomarker for predicting GDM.

The role of complement factor H in gestational diabetes mellitus and pregnancy.

BACKGROUND: Complement factor H (CFH) has been found to be associated with insulin resistance. This study assessed the correlation between CFH and other clinical parameters, and determined whether CFH played a role in gestational diabetes mellitus (GDM) and adverse pregnancy outcomes. METHODS: A total of 397 pregnant women were included for analysis in this nested case-control study. Clinical parameters and serum were collected within the 11-17th gestational age at the first prenatal visit. At 24-28 weeks of gestation, a 75 g oral glucose tolerance test was performed and subjects were divided into a GDM (n = 80) and a non-GDM control group (n = 317). The delivery data were also followed. The serum CFH level was assayed by ELISA. RESULTS: CFH was higher in GDM than in non-GDM controls (280.02 [58.60] vs. 264.20 [68.77]; P = 0.014). CFH level was moderately associated with pre-pregnancy body mass index (BMI), BMI and total triglycerides (TG), and slightly associated with gestational age, low density lipoprotein cholesterol (LDL-C), total cholesterol (TC) in GDM and non-GDM (all P <  0.05). Moreover, CFH level was moderately correlated with alkaline phosphatase (ALP) and slightly correlated with age, uric acid (UA) and total bilirubin (TB) in non-GDM (all P <  0.05). After adjustment for clinical confounding factors, BMI, TG, gestational age, ALP, TB, age and UA were independent risk factors for log10 CFH levels (all P <  0.05) in all subjects. In addition, overweight or obese pregnant women, women with hypertriglyceridemia and women in the second trimester had significantly higher CFH levels than normal weight and underweight group (P <  0.001), the non-hypertriglyceridemia group (P <  0.001) and women in the first trimester group (P < 0.05) in all pregnant women respectively. Following binary logistic regression, CFH was not independently associated with GDM and related pregnant outcomes. CONCLUSIONS: The CFH in 11-17th weeks of gestation might be affected by many factors, including BMI, TG, gestational age, ALP, TB, age and UA. CFH was not an independent risk factor for GDM and avderse pregnancy outcomes.

Serum Glycated Albumin Levels Are Affected by Alcohol in Men of the Jinuo Ethnic Group in China.

AIM: To investigate the effects of alcohol on serum glycated albumin (GA) levels in Chinese men. METHODS: A total of 2314 male subjects from the Jinuo ethnic group in China were enrolled. Of these, 986 subjects drank alcohol frequently and 404 subjects did not. Lifestyle information was gathered by using a questionnaire, and measurements of blood pressure, body mass index, blood glucose level, liver function, and kidney function were collected. GA was measured by using an enzymatic method. Frequent drinking was defined as a history of drinking ethanol > 80 g/d within the past two weeks. Nondrinking was defined as no alcohol consumption in the past three months. Subjects with an alcohol intake between 0 and 80 g/d in the past two weeks were included in the drinking-occasionally group. Analysis of variance (ANOVA), correlation analysis, and linear regression were used to evaluate the effects of drinking on serum GA levels. Decision tree regression (DTR) algorithm was used to evaluate the effect of features (variables) on GA levels. RESULTS: We found that male subjects who drank frequently had significantly lower serum GA levels than subjects who did not drink (13.0 ± 1.7 vs. 14.1 ± 3.7, p < 0.05). Spearman's correlation analysis calculated a coefficient of -0.152 between drinking and GA (p < 0.005). Linear regression established that drinking was an independent predictor for GA levels with a standardized regression coefficient of -0.144 (p < 0.05). Decision tree regression showed that the effect of drinking on GA levels (0.0283) is five times higher than that of smoking (0.0057). CONCLUSIONS: Frequent alcohol consumption could result in decreased GA levels in men of the Jinuo ethnic group in China.

The efficacy and safety of cold atmospheric plasma as a novel therapy for diabetic wound in vitro and in vivo.

Cold atmospheric plasma (CAP) is a group of various chemical active species, such as ozone and nitric oxide, generated by working gas. CAP was demonstrated to have an effect on tissue regeneration and wound healing. We conducted this study to evaluate the efficacy and safety of CAP as a novel therapy for diabetic wounds in vitro and in vivo. The plasma consists of ionised helium gas that is produced by a high-voltage and high-frequency power supply. Eight-week-old male db/db mice and C57BL mice were treated with helium gas (control group), 90s' CAP (low-dose group), and 180s' CAP (high-dose group). Mice were treated and observed for 2 weeks. Skin samples from around the wound and blood samples were collected. Our in vitro analysis included scratch wound-healing assays by using human HaCaT immortalised human epidermal cells. After 14 days of treatment, CAP could obviously promote diabetic wound healing. Wound closure rates were significantly higher in the low-dose group and high-dose groups compared with the control group. Meanwhile, compared with the control group, the protein expression of IL-6, tumour necrosis factor-α, inducible nitric oxide synthase, and superoxide dismutase in two CAP groups significantly decreased, while the protein expression of vascular endothelial growth factor and transforming growth factor-ß in two CAP groups significantly increased (all P < .05); these data show good agreement with the change in mRNA level (all P < .05). In vitro, scratch wound-healing assays showed that plasma treatment could effectively ensure healing within 3 minutes of exposure (all P < .05). In addition, no difference was found in histological observations of normal skin and the level of serum alanine transaminase, aspartate aminotransferase, blood urea nitrogen, creatinine, and white blood cells among the CAP groups and control group. CAP treatment for 3 minutes every day improves wound healing in diabetic mice by suppressing inflammation, reducing oxidative stress, and enhancing angiogenesis, involving several proteins signalling, and it is safe for the liver and kidney.

Learning misclassification costs for imbalanced classification on gene expression data.

BACKGROUND: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights. RESULTS: In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches. CONCLUSIONS: Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

Accelerating smooth molecular surface calculation.

This study proposes a novel approach, namely, skin flow complex algorithm (SFCA), to decompose the molecular skin surface into topological disks. The main contributions of SFCA include providing a simple decomposition and fast calculation of the molecular skin surface. Unlike most existing works which partition the molecular skin surface into sphere and hyperboloid patches, SFCA partitions the molecular skin surface into triangular quadratic patches and rectangular quadratic patches. Each quadratic patch is proven to be a topological disk and rendered by a rational Bézier patch. The skin surface is constructed by assembling all rational Bézier patches. Experimental results show that the SFCA is more efficient than most existing algorithms, and produces a triangulation of molecular skin surface which is decomposable, deformable, smooth, watertight and feature-preserved.

Meta-Analysis of the Effects of Exercise Training on Markers of Metabolic Syndrome in Solid Organ Transplant Recipients.

BACKGROUND: The markers of metabolic syndrome can prompt the development of metabolic syndrome and are associated with worse graft function among transplant recipients. Studies have shown that exercise might be effective in reducing incidence rate of metabolic syndrome components in nontransplant patients. However, there is no sufficient evidence to determine whether exercise training is safe or effective for markers of metabolic syndrome among solid organ transplant recipients. OBJECTIVE: To evaluate the safety and efficacy of exercise training programs for risk markers of metabolic syndrome and to further evaluate its long-term effects in solid organ transplant recipients. METHODS: We systematically reviewed all randomized trials comparing the outcomes of exercise training in organ transplant recipients. The Cochrane Library, Joanna Briggs Institute EBP Database, PubMed, Embase, Web of Science Core Collection, ProQuest Health & Medical Complete, and SinoMed databases were searched to June 2017. The meta-analysis was conducted using RevMan 5.3. RESULTS: In total, 13 eligible trails involving 464 patients were included. Recipients who engaged in an exercise program after transplantation showed significant reductions in fasting blood glucose, diastolic blood pressure, triglycerides, and body mass index and a significant increase in high-density lipoprotein. But found no significant changes in new-onset diabetes, systolic blood pressure, total cholesterol, or low-density lipoprotein. CONCLUSION: Exercise training may be a promising intervention for markers of metabolic syndrome in transplant recipients. Further research is required to determine essential aspects of exercise according to organ transplantation type for effects on markers of metabolic syndrome.

Different effect of testosterone and oestrogen on urinary excretion of metformin via regulating OCTs and MATEs expression in the kidney of mice.

The aim of this study was to investigate the effect of testosterone and oestrogen on regulating organic cation transporters (Octs) and multidrug and toxin extrusions (Mates) expression in the kidney of mice and urinary excretion of metformin. 8 week-old male db/db mice were treated with estradiol (5 mg/kg), testosterone (50 mg/kg) or olive oil with same volume. Metformin (150 mg/kg) was injected in daily for successive 7 days. Plasma, urine and tissue concentrations of metformin were determined by liquid chromatography-tandem mass spectrometry (LCMS) assay. Western blotting and Real-time PCR analysis were successively used to evaluate the renal protein and mRNA expression of Octs and MATEs. After treatment, the protein expression of Mate1 and Oct2 in testosterone group was significantly increased than those in control group (both P < 0.05). The protein expression of Mate1 and Oct2 in estradiol group was significantly reduced by 29.4% and 43.3%, respectively, compared to those in control group (all P < 0.05). These data showed a good agreement with the change in mRNA level (all P < 0.05). The plasma metformin concentration (ng/ml) in mice treated with estradiol was significantly higher than control and testosterone group (677.56 ± 72.49 versus 293.92 ± 83.27 and 261.46 ± 79.45; P < 0.01). Moreover, testosterone increased the metformin urine excretion of mice while estradiol decreasing (both P < 0.01). Spearman correlation analysis showed that gonadal hormone was closely associated with Mate1 and Oct2 expression and metformin urine excretion in db/db mice (all P < 0.05). Testosterone and oestrogen exerted reverse effect on metformin urinary excretion via regulating Octs and Mates expression in the kidney of mice.

Frozen section of uterine curetting in excluding the possibility of ectopic pregnancy--a clinicopathologic study of 715 cases.

INTRODUCTION: To investigate the utility of frozen section of uterine curetting in excluding the possibility of ectopic pregnancy (EP). MATERIALS AND METHODS: A retrospective analysis of 715 curetting records in the present hospital from July 1999 to May 2009 was obtained. All specimens were processed routinely with frozen section and paraffin section. RESULTS: Of 715 cases, frozen section analyses were discordant in 33 cases (4.6%), including 32 cases under-diagnosed, and one case over-diagnosed, compared with the final diagnoses. Frozen section had a sensitivity of 92.6%, specificity of 99.6%, and frozen section accuracy rate of 95.4%. CONCLUSIONS: Frozen section is a useful and rapid method to differentiate EP from intrauterine pregnancy.

Changes in PM2.5-related diabetes risk under the implementation of the clean air act in Shanghai.

OBJECTIVES: We examined the associations between PM2.5 exposure and Type 2 diabetes mellitus risk under the implementation of the Clean Air Act (CAA) among high-risk population for diabetes in Shanghai. METHODS: A total of 10,499 subjects from the Shanghai High-Risk Diabetic Screen (SHiDS) project between 2002 and 2018, linked with remotely sensed PM2.5 concentrations, were enrolled in this study. Ordinary least squares and logistic regression were applied to explore associations between PM2.5 and diabetes risk in various exposure periods. RESULTS: In year 2002-2013 (before CAA), the diabetes risk increased 7.5 % (95 % CI: 1.018-1.137), 8.0 % (95 % CI: 1.022-1.142) and 7.9 % (95 % CI: 1.021-1.141) under each 10 µg/m3 increase of long-term (1, 2 and 3 years) PM2.5 exposure, respectively. Elevated PM2.5 exposure were also associated with a significant increase in glycemic parameters before CAA implementation. However, in the year 2014-2018 (after CAA), the associations between PM2.5 exposure and diabetes risk were not significant after controlling for potential confounders. CONCLUSION: Our findings suggest that long-term and high-level exposure to PM2.5 was associated with increased prevalence of diabetes. Moreover, the implementation of CAA might ameliorate PM2.5-related diabetes risk.

Development and Validation of a Self-Aligning Knee Exoskeleton With Hip Rotation Capability.

The self-aligning capability of an exoskeleton is important to ensure wearing comfort, and the delicate motion ability of the exoskeleton is essential for motion assistance. Designing a self-aligning exoskeleton that offers improved wearing comfort and enhanced motion-assistance functions remains a challenge. This paper proposes a novel spatial self-aligning mechanism for a knee exoskeleton to enable simultaneous assistance in the flexion and extension (FE) of the knee joint and the internal and external rotation (IER) of the hip joint. Additionally, considering the misalignment of the human-robot joint axes, a kinematic model of the knee exoskeleton is established and analyzed to demonstrate the kinematic compatibility of the exoskeleton. Furthermore, a global torque manipulability (GTM) index is proposed to evaluate the effects of dimensional parameters on the exoskeleton's performance, and then the knee exoskeleton is optimized according to the GTM index. Finally, experiments are conducted to validate the performance of the proposed exoskeleton. The experimental results show that during knee FE and hip IER, the proposed exoskeleton exhibits lower interaction forces and torques than existing exoskeletons.

Symptoms experienced after transcatheter arterial chemoembolization in patients with primary liver cancer: A network analysis.

Objective: This study aimed to establish a symptom network for patients with primary liver cancer posttranscatheter arterial chemoembolization (TACE), identifying core and bridge symptoms. The goal is to provide a foundation for precise and comprehensive nursing interventions. Methods: A total of 1207 post-TACE patients were included using a consecutive sampling method. Data collection involved a general information questionnaire, the Anderson Symptom Assessment Scale, and a primary liver cancer-specific symptom module. The symptom network was constructed using the R language. Results: In the overall network, distress exhibited the highest strength (rs = 1.31) and betweenness (rb = 62). Fatigue had the greatest closeness (rc = 0.0043), while nausea and vomiting (r = 0.76 ± 0.02) had the highest marginal weights. Nausea had the highest bridge strength (rbs = 5.263). In the first-time TACE-treated symptom network, sadness (rbs = 5.673) showed the highest bridge strength, whereas in the non-first-time symptom network, fever (rbs = 3.061) had the highest bridge strength. Conclusions: Distress serves as a core symptom, and nausea acts as a bridge symptom after TACE treatment in liver cancer patients. Interventions targeting bridge symptoms should be tailored based on the number of treatments, enhancing the quality of symptom management.

Serum electrolyte levels in relation to macrovascular complications in Chinese patients with diabetes mellitus.

BACKGROUND: The prevalence of diabetes in China is increasing rapidly. However, scarce data are available on serum electrolyte levels in Chinese adults with diabetes, especially in those with cardiovascular complications. This study measured serum electrolyte levels and examined their relationship with macrovascular complications in Chinese adults with diabetes. METHODS: The three gender- and age-matched groups were enrolled into this analysis, which were 1,170 subjects with normal glucose regulation (NGR), 389 with impaired glucose regulation (IGR) and 343 with diabetes. Fasting plasma glucose (FPG), 2-hour post-load plasma glucose (2hPG), glycosylated hemoglobin A1c (HbA1c) and serum electrolyte levels were measured. Data collection included ankle brachial index results. RESULTS: Serum sodium and magnesium levels in the diabetes group were significantly decreased compared to the NGR group (sodium: 141.0 ± 2.4 vs. 142.1 ± 2.0 mmol/l; magnesium: 0.88 ± 0.08 vs. 0.91 ± 0.07 mmol/l, all P < 0.01), while the serum calcium level was significantly increased (2.36 ± 0.11 vs. 2.33 ± 0.09 mmol/l, P < 0.01). Multiple linear regression showed that serum sodium and magnesium levels in the diabetes group were negatively correlated with FPG, 2hPG and HbA1c (sodium: Std ß = -0.35, -0.19, -0.25; magnesium: Std ß = -0.29, -0.17, -0.34, all P < 0.01), while the serum calcium level was positively correlated with HbA1c (Std ß = 0.17, P < 0.05). In diabetic subjects, serum sodium, magnesium and potassium levels were decreased in the subjects with the elevation of estimated glomerular filtration rates (P < 0.05). ANCOVA analysis suggested that serum magnesium level in subjects with diabetic macrovascular complications was significantly decreased compared with diabetic subjects without macrovascular complications after the effect of some possible confounding being removed (P < 0.05). CONCLUSIONS: Serum sodium and magnesium levels were decreased in Chinese subjects with diabetes, while the observed increase in calcium level correlated with increasing glucose level. Diabetic patients with macrovascular complications had lower serum magnesium level than those with no macrovascular complications.

Vibration perception threshold for sight-threatening retinopathy screening in type 2 diabetic outpatients.

OBJECTIVE: We investigated the relationship between vibration perception threshold and diabetic retinopathy and verified the screening value of vibration perception threshold for severe diabetic retinopathy. METHODS: A total of 955 patients with type 2 diabetes were recruited and divided into three groups according to their fundus oculi photography results: no diabetic retinopathy (n = 654, 68.48%), non-sight-threatening diabetic retinopathy (n = 189, 19.79%) and sight-threatening diabetic retinopathy (n = 112, 11.73%). Their clinical and biochemical characteristics, vibration perception threshold and the diabetic retinopathy grades were detected and compared. RESULTS: There were significant differences in diabetes duration and blood glucose levels among three groups (all p < 0.05). The values of vibration perception threshold increased with the rising severity of retinopathy, and the vibration perception threshold level of sight-threatening diabetic retinopathy group was significantly higher than both non-sight-threatening diabetic retinopathy and no diabetic retinopathy groups (both p < 0.01). The prevalence of sight-threatening diabetic retinopathy in vibration perception threshold >25 V group was significantly higher than those in 16-24 V group (p < 0.01). The severity of diabetic retinopathy was positively associated with diabetes duration, blood glucose indexes and vibration perception threshold (all p < 0.01). Multiple stepwise regression analysis proved that glycosylated haemoglobin (ß = 0.385, p = 0.000), diabetes duration (ß = 0.275, p = 0.000) and vibration perception threshold (ß = 0.180, p = 0.015) were independent risk factors for diabetic retinopathy. Receiver operating characteristic analysis further revealed that vibration perception threshold higher than 18 V was the optimal cut point for reflecting high risk of sight-threatening diabetic retinopathy (odds ratio = 4.20, 95% confidence interval = 2.67-6.59). CONCLUSION: There was a close association between vibration perception threshold and the severity of diabetic retinopathy. vibration perception threshold was a potential screening method for diabetic retinopathy, and its optimal cut-off for prompting high risk of sight-threatening retinopathy was 18 V.

Ten-year changes in the prevalence of overweight, obesity and central obesity among the Chinese adults in urban Shanghai, 1998-2007 - comparison of two cross-sectional surveys.

BACKGROUND: In China, obesity is expected to increase rapidly in both urban and rural areas. However, there have been no comprehensive reports on secular trends in obesity prevalence among Chinese adults in urban Shanghai, which is the largest city in southern China. METHODS: In 1998-2001 and again in 2007-2008, two independent population-based cross-sectional surveys were conducted in Shanghai to investigate the prevalence of metabolic disorders. These surveys obtained height, waist circumference (WC), and weight measurements for Chinese adults aged between 20 and 74 years who lived in urban communities. From the 1998-2001 survey, 4,894 participants (2,081 men and 2,813 women, mean age: 48.9 years) were recruited, and 4,395 participants (1,599 men and 2,796 women, mean age: 49.8 years) were recruited from the 2007-2008 survey. Using the World Health Organization criteria, overweight was defined as 25 kg/m² ≤ BMI < 30 kg/m² and obesity as BMI ≥ 30 kg/m². Central obesity was defined as WC ≥ 90 cm in men or ≥85 cm in women. The differences in prevalence of obesity, central obesity and overweight between the two surveys were tested using multivariable logistic regression analyses. RESULTS: Compared to the 1998-2001 survey, in the 2007-2008 survey the BMI distribution for men and the WC distribution for both genders is shifted significantly to the right along the x-axis (all p < 0.001). Over the ten years, the prevalence of combined overweight and obesity increased 24% (from 31.5% to 39.1%, p < 0.001) in men, but decreased 8% (from 27.3% to 25.0%; p < 0.01) in women. The prevalence of central obesity increased 40% in men (from 19.5% to 27.3%; p < 0.01), but the increase was not significant in women (15.0% to 17.1%; p = 0.051). In the total population, only central obesity showed a significant change between the populations in the two surveys, increasing 29% (from 17.3% to 22.4%; p < 0.001). CONCLUSIONS: Over this 10 year period, central obesity increased significantly in the Shanghai adult population. However, the prevalence of combined overweight and obesity was significantly increased in men but not in women.

Identification of potential crosstalk genes and mechanisms between periodontitis and diabetic nephropathy through bioinformatic analysis.

Periodontitis and diabetic nephropathy are significant public health concerns globally and are closely related with each other. This study aimed to identify potential crosstalk genes, pathways, and mechanisms associated with the interaction between periodontitis and diabetic nephropathy. Expression profiles of periodontitis and diabetic nephropathy were retrieved from the Gene expression omnibus gene expression omnibus database, and differentially expressed genes (DEGs) were screened, followed by identification of co-expressed differential genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. A protein-protein interaction network was constructed via STRING website, and key crosstalk genes were selected using Cytoscape. Subsequent gene ontology and KEGG analyses were conducted for the key genes, and a validation dataset was obtained from the gene expression omnibus database for differential gene validation. The TRRUST website was employed to identify transcription factors (TFs) associated with the key crosstalk genes between periodontitis and diabetic nephropathy, followed by differential analysis of TFs. A total of 17 crosstalk genes were obtained. Among them, SAMSN1, BCL2A1, interleukin-19, IL1B, RGS1, CXCL3, CCR1, CXCR4, CXCL1, and PTGS2 were identified as key crosstalk genes between periodontitis and diabetic nephropathy. Additionally, 16 key TFs were discovered. This bioinformatic analysis revealed potential crosstalk genes between periodontitis and diabetic nephropathy. The identified key genes participate in signaling pathways, including cytokine signaling and chemokine signaling transduction, which might collectively influence these 2 diseases. These genes may serve as potential biomarkers guiding future research in this field.

A cross-modal deep metric learning model for disease diagnosis based on chest x-ray images.

The emergence of unknown diseases is often with few or no samples available. Zero-shot learning and few-shot learning have promising applications in medical image analysis. In this paper, we propose a Cross-Modal Deep Metric Learning Generalized Zero-Shot Learning (CM-DML-GZSL) model. The proposed network consists of a visual feature extractor, a fixed semantic feature extractor, and a deep regression module. The network belongs to a two-stream network for multiple modalities. In a multi-label setting, each sample contains a small number of positive labels and a large number of negative labels on average. This positive-negative imbalance dominates the optimization procedure and may prevent the establishment of an effective correspondence between visual features and semantic vectors during training, resulting in a low degree of accuracy. A novel weighted focused Euclidean distance metric loss is introduced in this regard. This loss not only can dynamically increase the weight of hard samples and decrease the weight of simple samples, but it can also promote the connection between samples and semantic vectors corresponding to their positive labels, which helps mitigate bias in predicting unseen classes in the generalized zero-shot learning setting. The weighted focused Euclidean distance metric loss function can dynamically adjust sample weights, enabling zero-shot multi-label learning for chest X-ray diagnosis, as experimental results on large publicly available datasets demonstrate.