Identification of hub genes correlated with diabetic retinopathy via bioinformatics methods

OBJECTIVE

The aim of this study was to identify the hub genes and uncover the molecular mechanisms of diabetic retinopathy (DR).

MATERIALS AND METHODS

We used the Gene Expression Omnibus (GEO) dataset GSE60436 in our study. After screening for differentially expressed genes (DEGs), we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis. Subsequently, a protein-protein interaction (PPI) network was constructed using the Search Tool for Retrieval of Interacting Genes (STRING) database and visualized using the Cytoscape software. Finally, we identified 10 hub genes by cytoHubba plugin.

RESULTS

A total of 592 DEGs were identified, including 203 up-regulated genes and 389 downregulated genes. The DEGs were mainly enriched in visual perception, photoreceptor outer segment membrane, retinal binding, and PI3K-Akt signaling pathway. By constructing a protein-protein interaction (PPI) network, 10 central genes were finally identified, including CNGA1, PDE6G, RHO, ABCA4, PDE6A, PDE6B, NRL, RPE65, GUCA1B and AIPL1.

CONCLUSIONS

CNGA1, PDE6G, RHO, ABCA4, PDE6A, PDE6B, NRL, RPE65, GUCA1B, and AIPL1 may be potential biomarkers and therapeutic targets for DR.

[Association of cadmium internal exposure with chronic kidney disease in Chinese adults]

Objective: To analyze the association of the cadmium internal exposure with chronic kidney disease (CKD) in Chinese adults aged 18 and older. Methods: A total of 9 821 adults aged 18-79 from the China National Human Biomonitoring (CNHBM) from 2017 to 2018 were included. Blood and urine cadmium exposure levels were measured by inductively coupled plasma mass spectrometry (ICP-MS), and urine cadmium levels were adjusted with urine creatinine; CKD were defined by estimated glomerular filtration (eGFR) using the chronic kidney disease epidemiology collaboration (CKD-EPI). Weights were considered due to complex sampling process for in statistical analysis. Logistic regression is used to analyze the association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine exposure levels with CKD, and restricted cube spline (RCS) was used to assess the exposure-response curve of blood cadmium, urine cadmium and urine cadmium adjusted with creatinine with CKD. Results: The weighted age was 44.75 and males accounted for 61.1%. The prevalence rate of CKD was 12.7%. The geometric mean values of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine were 0.96 μg/L, 0.61 μg/L, and 0.58 μg/g. After adjusting for confounding factors, the weighted logistic regression showed that the lowest quintile (Q1) was compared with the odds ratio (OR) of the highest quintile (Q5) of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine and the 95% confidence interval (CI) was 1.80 (1.02-3.20), 1.77 (0.94-3.31) and 1.94 (1.11-3.37) respectively. In the restricted cubic spline regression model, non-linear association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine with CKD were observed after adjusting for related confounding factors (P<0.001, 0.018, 0.031 respectively). The risk of CKD increased with the increment of cadmium exposure without risk threshold, and the exposure response curve was steeper at low cadmium exposure. Conclusions: Among Chinese adults aged 18 and older, cadmium exposure is positively associated with the risk of chronic kidney disease.

Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network

BACKGROUND

Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carcinoma (BCC) using smartphone-captured images.

OBJECTIVES

To develop deep neural network frameworks for accurate BCC recognition and segmentation based on smartphone-captured microscopic ocular images (MOIs).

METHODS

We collected a total of 8046 MOIs, 6610 of which had binary classification labels and the other 1436 had pixelwise annotations. Meanwhile, 128 WSIs were collected for comparison. Two deep learning frameworks were created. The 'cascade' framework had a classification model for identifying hard cases (images with low prediction confidence) and a segmentation model for further in-depth analysis of the hard cases. The 'segmentation' framework directly segmented and classified all images. Sensitivity, specificity and area under the curve (AUC) were used to evaluate the overall performance of BCC recognition.

RESULTS

The MOI- and WSI-based models achieved comparable AUCs around 0·95. The 'cascade' framework achieved 0·93 sensitivity and 0·91 specificity. The 'segmentation' framework was more accurate but required more computational resources, achieving 0·97 sensitivity, 0·94 specificity and 0·987 AUC. The runtime of the 'segmentation' framework was 15·3 ± 3·9 s per image, whereas the 'cascade' framework took 4·1 ± 1·4 s. Additionally, the 'segmentation' framework achieved 0·863 mean intersection over union.

CONCLUSIONS

Based on the accessible MOIs via smartphone photography, we developed two deep learning frameworks for recognizing BCC pathology with high sensitivity and specificity. This work opens a new avenue for automatic BCC diagnosis in different clinical scenarios. What's already known about this topic? The diagnosis of basal cell carcinoma (BCC) is labour intensive due to the large number of images to be examined, especially when consecutive slide reading is needed in Mohs surgery. Deep learning approaches have demonstrated promising results on pathological image-related diagnostic tasks. Previous studies have focused on whole-slide images (WSIs) and leveraged classification on image patches for detecting and localizing breast cancer metastases. What does this study add? Instead of WSIs, microscopic ocular images (MOIs) photographed from microscope eyepieces using smartphone cameras were used to develop neural network models for recognizing BCC automatically. The MOI- and WSI-based models achieved comparable areas under the curve around 0·95. Two deep learning frameworks for recognizing BCC pathology were developed with high sensitivity and specificity. Recognizing BCC through a smartphone could be considered a future clinical choice.

IL-10 promoter SNPs and susceptibility to leprosy in ethnic groups from southwest China

The purpose of this study was to determine whether interleukin-10 (IL-10) promoter polymorphisms are associated with leprosy or their subtypes in ethnic groups from southwest China. Genotyping using TaqMan® SNP Genotyping Master Mix and ABI 7500 real-time PCR system was performed for IL-10 T3575A, G2849A, C2763A, A1082G, C819T, and C592A in 189 healthy controls (40 ± 18 years) and 193 patients (46 ± 18 years) with leprosy [multibacillary, N = 131; paucibacillary (PB), N = 62]. The allelic frequencies of -2763C (97.9 vs 94.0%, P = 0.0074) and -1082A (92.8 vs 88.6%, P = 0.0452) in leprosy patients were significantly higher than in control subjects. The genetic frequency of -2763CC and -1082AA was not only significantly higher among leprosy patients than among control subjects [odds ratio (OR) = 3.33, 95% confidence interval (95%CI) = 1.39-7.99, P = 0.0071 and OR = 1.76, 95%CI = 1.02-3.03, P = 0.0420, respectively] but also significantly higher among PB patients than among control subjects (OR = 2.46, 95%CI = 1.22-4.96, P = 0.0115 and OR = 5.58, 95%CI = 2.06-15.12, P = 0.0007, respectively). The frequency of IL-10 haplotype 3575A/2849G/2763A/1082G/819C/592C was significantly higher among leprosy patients (OR = 5.57, 95%CI = 1.13-27.52, P = 0.0351) and PB patients (OR = 10.5, 95%CI = 1.36- 81.05, P = 0.0241) than among control subjects. IL-10 promoter -2763C/CC,-1082A/AA and haplotype 3575A/2849G/2763A/1082 G/819C/592C are associated with susceptibility to leprosy and the PB subtype in southwest China.

MR imaging volumetry of subcortical structures and cerebellar hemispheres in temporal lobe epilepsy

BACKGROUND AND PURPOSE

There is mounting evidence of extratemporal volume changes associated with medically refractory temporal lobe epilepsy (TLE). This MR imaging study aimed to characterize volume changes in subcortical structures and cerebellar hemispheres with respect to lateralization of the seizure focus, onset and duration of epilepsy, and frequency of generalized tonic-clonic seizures (GTCS).

METHODS

Amygdalar, hippocampal, thalamic, caudate head, and cerebellar volume measurements were obtained in the preoperative MR images of 40 patients with TLE (20 right, 20 left), who underwent temporal lobe resection with good outcome, and in 20 right-handed control participants. All 3D MR images were spatially aligned and normalized before measurements were obtained. Standardized volumes and right-to-left volume ratios (VRs) were compared between control participants and right and left TLE groups. Multiple regression analyses were performed to study the effects of epilepsy onset and duration and GTCS frequency on ipsilateral-to-contralateral VRs with respect to the resected seizure focus.

RESULTS

Thalamic volumes were smaller bilaterally in patients with TLE. Hippocampal volumes were smaller ipsilateral to the seizure focus, but there was no significant volume loss involving the amygdala, caudate, or cerebellum. Hippocampal and amygdalar right-to-left VRs differed significantly between right and left TLE groups and controls, whereas thalamic right-to-left VRs differed only between the TLE groups. Thalamic ipsilateral-to-contralateral VRs were correlated positively with epilepsy onset and negatively with epilepsy duration. Caudate ipsilateral-to-contralateral VRs were positively, whereas amygdalar and cerebellar VRs were negatively, correlated with GTCS frequency.

CONCLUSIONS

Unilateral amygdalar and bilateral thalamic volume loss, in the absence of caudate head atrophy, is likely to reflect seizure-induced injury due to TLE. Correlations of VRs affecting the amygdala, caudate, and cerebellum with GTCS frequency may also reflect injury or a prediposition for secondary generalization. Potential effects of complex partial seizures, febrile seizures, or antiepileptic medications on subcortical structures need to be evaluated in future studies.

Study of urinary 8-hydroxydeoxyguanosine as a biomarker of oxidative DNA damage in diabetic nephropathy patients

Increased oxidative stress induced by hyperglycemia may contribute to the pathogenesis of diabetic complications. Urinary 8-hydroxydeoxyguanosine (8-OHdG) has been reported to serve as a sensitive biomarker of oxidative DNA damage and also of oxidative stress. This article studied oxidative DNA damage in patients with diabetic nephropathy and in healthy control subjects by urinary 8-OHdG evaluations. Contents of 8-OHdG in urine were analyzed by capillary electrophoresis with end-column amperometric detection (CE-AD) after a single-step solid-phase extraction (SPE). Levels of urinary 8-OHdG in diabetic nephropathy patients with macroalbuminuria was significant higher than in control subjects (5.72 +/- 6.89 micromol/mol creatinine versus 2.33 +/- 2.83 micromol/mol creatinine, P = 0.018). A significant difference of 24 h urinary 8-OHdG excretions exists between the patients with macroalbuminuria and the patients with normoalbuminuria (19.2 +/- 16.8 microg/24 h versus 8.1 +/- 1.7 microg/24 h, P = 0.015). There was a positive correlation between urinary excretion of 8-OHdG and glycosylated hemoglobin (HbA1c) (r = 0.287, P = 0.022). A weak correlation exists between the levels of 8-OHdG and triglyceride (r = 0.230, P = 0.074). However, the urinary 8-OHdG contents are not correlated with blood pressure and total cholesterol. The increased excretion of urinary 8-OHdG is seen as indicating an increased systemic level of oxidative DNA damage in diabetic nephropathy patients.

Location-probability profiles for the mouth region of human primary motor-sensory cortex: model and validation

The mouth representation of the human, primary motor cortex (M1) is not reliably identified by surface anatomy but may be reliably localized by means of spatial coordinates. For this report, three quantitative metanalyses were performed which jointly described the mean location, location variability and location-probability profiles of the human M1-mouth representation. First, a literature metanalysis of intersubject functional-area variability was performed using eleven, per-subject studies, each of which reported a coordinate-referenced measure of intersubject variability for one or more brain areas. From these data, a weighted-mean value for intersubject variability was computed, which proved to be small (5.6 mm, standard deviation), consistent across coordinate axes (x, y, z), and consistent across brain areas. Second, a literature metanalysis of the location of M1-mouth was performed using seven, coordinate-referenced, group-mean studies (71 subjects in all), each of which reported a grand-average location for M1-mouth. From this, a weighted-mean location and weighted values for total variability (interlaboratory plus interindividual) were determined. Using these two literature metanalyses as input data, location-probability profiles were computed for the cardinal axes (x, y, and z) of the reference space, using the functional volumes modeling (FVM) statistical model. Third, an original-data metanalysis was performed on in-house PET data from 30 normal subjects performing overt-speech tasks. M1-mouth's mean location, location variability, and location-probability profiles were consistent with those conjointly modeled by FVM from the two literature metanalyses. Collectively, these observations provide a detailed, consensus probabilistic description of the location of the human M1-mouth representation in standardized coordinates.

Brain correlates of stuttering and syllable production. A PET performance-correlation analysis

To distinguish the neural systems of normal speech from those of stuttering, PET images of brain blood flow were probed (correlated voxel-wise) with per-trial speech-behaviour scores obtained during PET imaging. Two cohorts were studied: 10 right-handed men who stuttered and 10 right-handed, age- and sex-matched non-stuttering controls. Ninety PET blood flow images were obtained in each cohort (nine per subject as three trials of each of three conditions) from which r-value statistical parametric images (SPI¿r¿) were computed. Brain correlates of stutter rate and syllable rate showed striking differences in both laterality and sign (i.e. positive or negative correlations). Stutter-rate correlates, both positive and negative, were strongly lateralized to the right cerebral and left cerebellar hemispheres. Syllable correlates in both cohorts were bilateral, with a bias towards the left cerebral and right cerebellar hemispheres, in keeping with the left-cerebral dominance for language and motor skills typical of right-handed subjects. For both stutters and syllables, the brain regions that were correlated positively were those of speech production: the mouth representation in the primary motor cortex; the supplementary motor area; the inferior lateral premotor cortex (Broca's area); the anterior insula; and the cerebellum. The principal difference between syllable-rate and stutter-rate positive correlates was hemispheric laterality. A notable exception to this rule was that cerebellar positive correlates for syllable rate were far more extensive in the stuttering cohort than in the control cohort, which suggests a specific role for the cerebellum in enabling fluent utterances in persons who stutter. Stutters were negatively correlated with right-cerebral regions (superior and middle temporal gyrus) associated with auditory perception and processing, regions which were positively correlated with syllables in both the stuttering and control cohorts. These findings support long-held theories that the brain correlates of stuttering are the speech-motor regions of the non-dominant (right) cerebral hemisphere, and extend this theory to include the non-dominant (left) cerebellar hemisphere. The present findings also indicate a specific role of the cerebellum in the fluent utterances of persons who stutter. Support is also offered for theories that implicate auditory processing problems in stuttering.

[Advances in nonaqueous capillary electrophoresis]

Advances in nonaqueous capillary electrophoresis are reviewed with 71 references in this paper. Capillary electrophoresis is generally performed in aqueous buffer. In fact, it can provide some advantages to use organic solvent as a separation medium instead of water. The choice of organic solvent and electrolyte, the detection mode and solute-additive interactions are summarized. Furthermore, the separation of inorganic ions, neural compounds, organic acids, pharmaceuticals, metabolites and chiral substance is also described.

Functional volumes modeling: scaling for group size in averaged images

Functional volumes modeling (FVM) is a statistical construct for metanalytic modeling of the locations of brain functional areas as spatial probability distributions. FV models have a variety of applications, in particular, to serve as spatially explicit predictions of the Talairach-space locations of functional activations, thereby allowing voxel-based analyses to be hypothesis testing rather than hypothesis generating. As image averaging is often applied in the analysis of functional images, an important feature of FVM is that a model can be scaled to accommodate any degree of intersubject image averaging in the data set to which the model is applied. In this report, the group-size scaling properties of FVM were tested. This was done by: (1) scaling a previously constructed FV model of the mouth representation of primary motor cortex (M1-mouth) to accommodate various degrees of averaging (number of subjects per image = n = 1, 2, 5, 10), and (2) comparing FVM-predicted spatial probability contours to location-distributions observed in averaged images of varying n composed from randomly sampling a 30-subject validation data set.

Functional volumes modeling: scaling for group size in averaged images

Functional volumes modeling (FVM) is a statistical construct for metanalytic modeling of the locations of brain functional areas as spatial probability distributions. FV models have a variety of applications, in particular, to serve as spatially explicit predictions of the Talairach-space locations of functional activations, thereby allowing voxel-based analyses to be hypothesis testing rather than hypothesis generating. As image averaging is often applied in the analysis of functional images, an important feature of FVM is that a model can be scaled to accommodate any degree of intersubject image averaging in the data set to which the model is applied. In this report, the group-size scaling properties of FVM were tested. This was done by: (1) scaling a previously constructed FV model of the mouth representation of primary motor cortex (M1-mouth) to accommodate various degrees of averaging (number of subjects per image = n = 1, 2, 5, 10), and (2) comparing FVM-predicted spatial probability contours to location-distributions observed in averaged images of varying n composed from randomly sampling a 30-subject validation data set.

Further study of the effectiveness of single doses of clarithromycin and minocycline against Mycobacterium leprae in mice

The anti-Mycobacterium leprae activities of single doses of rifampin (RMP), clarithromycin (CLARI), or minocycline (MINO) alone, and various combinations of CLARI + MINO were determined in immunocompetent mice by the kinetic method. A single dose of RMP 10 mg/kg, CLARI 100 mg/kg or 200 mg/kg, MINO 25 mg/kg or 50 mg/kg alone, or various combinations of CLARI & MINO were active. RMP was more active than the other treatments; the activity of CLARI 100 mg/kg was greater than that of 50 mg/kg, but did not differ significantly from that of 200 mg/kg; MINO 50 mg/kg was more active than 25 mg/kg; and none of the combinations of CLARI + MINO was more active than any of the stronger components administered alone. Therefore, both CLARI and MINO may be applied, either alone or in combination, as components of monthly administered, fully supervised, multidrug regimens for the treatment of multibacillary leprosy. Taking into account the effectiveness of the drugs and the comparative pharmacokinetic data, we propose that the optimal dosage in human trials is CLARI 1000 mg per month or MINO 200 mg per month.