Correlation analysis between foot deformity and diabetic foot with radiographic measurement

BackgroundFoot deformity is a risk factor for diabetic foot ulcer. This study was aimed to investigate the relationship between hallux valgus (HV) and diabetic foot through the radiographic measurement.MethodsThe patients with diabetic foot hospitalizing in the Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University from September 2016 to June 2020 were selected. Then the foot plain X-ray radiographs were completed, and the size of HV angle (HVA) was measured. Their clinical data were collected, and the ulcer recurrence rate, amputation rate and mortality rate of the patients were followed up.ResultsA total of 370 patients were included. According to HVA, patients were divided into non-HV group (HVA<15°), and mild (15°≤HVA ≤ 20°), moderate (20°<HVA ≤ 40°) and severe (HVA>40°) HV groups. The age, height, BMI, smoking history and glycosylated hemoglobin level among the non-HVA, mild, moderate, and severe HV group (P<0.05), while smoking history, HbA1c, eGFR and autonomic neuropathy were significantly lower in HV group than those in non-HV group (P<0.05). The ulcer area in patients with moderate HV was larger than that in non-HV patients, and the severity of infection in patients with severe HV was significantly higher than that the other three groups (P<0.05).ConclusionThe occurrence of HV is not only related to age and BMI, but also to the creatinine and eGFR level, autonomic neuropathy, lower limb arteriosclerosis occlusion, coronary heart disease and hypertension. Therefore, more attention should be paid to renal function screening, neuropathy screening and evaluation of lower extremity vascular lesions in patients with diabetes, especially those with moderate or higher HV

A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice

Estimation of Recurrence of Colorectal Adenomas with Dependent Censoring Using Weighted Logistic Regression

In colorectal polyp prevention trials, estimation of the rate of recurrence of adenomas at the end of the trial may be complicated by dependent censoring, that is, time to follow-up colonoscopy and dropout may be dependent on time to recurrence. Assuming that the auxiliary variables capture the dependence between recurrence and censoring times, we propose to fit two working models with the auxiliary variables as covariates to define risk groups and then extend an existing weighted logistic regression method for independent censoring to each risk group to accommodate potential dependent censoring. In a simulation study, we show that the proposed method results in both a gain in efficiency and reduction in bias for estimating the recurrence rate. We illustrate the methodology by analyzing a recurrent adenoma dataset from a colorectal polyp prevention trial

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$