Objective
To explore the relevance of serum homocysteine (Hcy) level to erythrocyte and platelet parameters in patients with unstable angina pectoris (UAP).
Methods
Sixty patients with UAP were collected in Tongling Municipal Hospital from August 1st, 2012 to December 31st, 2015. Serum Hcy was measured by enzymatic cycling method. Erythrocyte parameters, such as red blood cell count (RBC), hemoglobin, mean corpuscular volume (MCV), coefficient of variation of red blood cell volume distribution width (RDW-CV), and platelet parameters, such as platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), platelet large cell ratio (P-LCR), were measured with blood cell counter. All patients were classified into UAP with hyperhomocystinemia (HHcy) group and UAP with normal Hcy group according to the level of Hcy. The data in two groups were analyzed and the relevance of serum Hcy level to erythrocyte and platelet parameters was evaluated.
Results
The differences in the levels of RBC, hemoglobin, MCV, PLT, PDW, MPV, P-LCR between the two groups were not statistically significant (P>0.05); while the levels of RDW-CV and the proportion of RDW-CV above the upper reference limit of patients in the UAP with HHcy group (13.81%±1.13%, 39.4%) were higher than those in the UAP with normal Hcy group (13.06%±0.97%, 4.8%), and the differences between the two groups were statistically significant (P<0.05). Correlation analysis showed that serum Hcy level of patients with UAP was significantly correlated with RDW-CV (r=0.380, P<0.01) and was not significantly correlated with other erythrocyte and platelet parameters (P>0.05).
Conclusion
The high level of Hcy affects red blood cell volume heterogeneity in patients with UAP, which may be one of the mechanisms of HHcy participating in the occurrence and development of UAP.
ObjectiveTo propose automatic measurement of global and local tessellation density on color fundus images based on a deep convolutional neural network (DCNN) method. MethodsAn applied study. An artificial intelligence (AI) database was constructed, which contained 1 005 color fundus images captured from 1 024 eyes of 514 myopic patients in the Northern Hospital of Qingdao Eye Hospital from May to July, 2021. The images were preprocessed by using RGB color channel re-calibration method (CCR algorithm), CLAHE algorithm based on Lab color space, Retinex algorithm for multiple iterative illumination estimation, and multi-scale Retinex algorithm. The effects on the segmentation of tessellation by adopting the abovemetioned image enhancement methods and utilizing the Dice, Edge Overlap Rate and clDice loss were compared and observed. The tessellation segmentation model for extracting the tessellated region in the full fundus image as well as the tissue detection model for locating the optic disc and macular fovea were built up. Then, the fundus tessellation density (FTD), macular tessellation density (MTD) and peripapillary tessellation density (PTD) were calculated automatically. ResultsWhen applying CCR algorithm for image preprocessing and the training losses combination strategy, the Dice coefficient, accuracy, sensitivity, specificity and Jordan index for fundus tessellation segmentation were 0.723 4, 94.25%, 74.03%, 96.00% and 70.03%, respectively. Compared with the manual annotations, the mean absolute errors and root mean square errors of FTD, MTD, PTD automatically measured by the model were 0.014 3, 0.020 7, 0.026 7 and 0.017 8, 0.032 3, 0.036 5, respectively. ConclusionThe DCNN-based segmentation and detection method can automatically measure the tessellation density in the global and local regions of the fundus of myopia patients, which can more accurately assist clinical monitoring and evaluation of the impact of fundus tessellation changes on the development of myopia.
ObjectiveTo analyze the clinical characteristics and geographical distribution of Keshan disease in Chongqing city for prevention and disease control.
MethodsWe collected the clinical data of patients with Keshan disease from 2008 to 2012 in Liangping, Shizhu, Fengdu and Dianjiang counties as well as Wanzhou district of Chongqing city including the medical history, physical examination, results of laboratory tests to analyze the clinical characteristics and geographical distribution.
ResultsFifty-eight patients were included from Liangping (n=21), Shizhu (n=25), Fengdu (n=11) and Dianjiang (n=1). The number of patients with potential and chronic Keshan disease was 16 and 42, respectively. The average age of patients was 54.91±15.53 years. The proportion above age 60 was 32.76% and below age 10 was 3.45%. The patients had main clinical signs as heart enlargement (36.76%), low-weak first heart sound (22.41%), systolic murmur (10.34%), arrhythmia (8.62%), etc. Abnormal ECG detection rate was 98.28%, with common types followed by sinus rhythm (37.93%), complete right bundle branch block (25.86%), ST-T changes (24.14%), left ventricular hypertrophy (15.52%), atrial fibrillation (13.79%), occasional ventricular premature (10.34%), T changes (10.34%), sinus bradycardia (8.62%), and incomplete right bundle branch block (6.90%). X-ray results showed that heart enlargement accounted for 82.76%. The ratios of mild, moderate and significant expansion of the heart were 46.55%, 27.59%, and 8.62%, respectively.
ConclusionIn recent years, most patients with Keshan disease in Chongqing are chronic type at older age. The main clinical symptom is heart enlargement with high abnormal ECG detection rate.
Objective To observe the association between the red cell distribution width (RDW)/albumin (ALB) ratio (RAR) and the progression of diabetic retinopathy (DR). MethodsA cohort study. From June 2020 to February 2022, 835 diabetic patients who participated in the Phase II Beichen Eye Study, conducted at the Tianjin Medical University Eye Hospital were included. All participants underwent a two-year follow-up. Data were collected from patients at both baseline and the two-year follow-up, including mydriatic color fundus photography and laboratory tests for RDW and ALB. The RAR was calculated based on these measurements. DR was diagnosed and graded according to the DR International Clinical Severity Scale. Based on the progression of DR, patients were categorized into a non-progression group (689 cases, 83%) and a progression group (146 cases, 17%). Univariate models, as well as models Ⅰ, Ⅱ, and Ⅲ, were constructed after adjusting for various variables. The associations between RAR and its tertiles with the progression of DR were analyzed utilizing multivariate logistic regression analysis. Additionally, subgroup and interaction analyses were conducted to further investigate the relationship. ResultsThere was no significant difference in RDW and ALB levels between patients in the non-progression group and those in the progression group (t=-1.399, 1.954; P > 0.05). However, a significant difference was observed in RAR (t=-2.147, P=0.033). Results from the multivariate logistic regression analysis indicated that, in the fully adjusted model Ⅲ, RAR was an independent risk factor for the progression of DR. Specifically, each unit increase in RAR was associated with a 2.33-fold higher risk of DR progression [odds ratio (OR)= 2.33, 95% confidence interval (CI) 1.20-4.54, P = 0.013]. Compared to the univariate model, the predictive power of the fully adjusted model III for DR progression was 71.3% (area under the curve= 0.713, P < 0.001). Interaction analysis revealed a statistically significant difference in the effect of insulin use on the association between RAR and DR progression (insulin users: OR= 5.83, 95% CI 2.15-15.78, P=0.013). ConclusionsIncreased RAR is associated with a heightened risk of DR progression, and insulin use may influence the relationship between the two.
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.
ObjectiveStudy how to quantify the bias of each study and how to estimate them.
MethodIn the random-effect model, it is commonly assumed that the effect size of each study in meta-analysis follows a skew normal distribution which has different shape parameter. Through introducing a shape parameter to quantify the bias and making use of Markov estimation as well as maximum likelihood estimation to estimate the overall effect size, bias of each study, heterogeneity variance.
ResultIn simulation study, the result was closer to the real value when the effect size followed a skew normal distribution with different shape parameter and the impact of heterogeneity of random effects meta-analysis model based on the skew normal distribution with different shape parameter was smaller than it in a random effects metaanalysis model. Moreover, in this specific example, the length of the 95%CI of the overall effect size was shorter compared with the model based on the normal distribution.
ConclusionIncorporate the bias of each study into the random effects meta-analysis model and by quantifying the bias of each study we can eliminate the influence of heterogeneity caused by bias on the pooled estimate, which further make the pooled estimate closer to its true value.
ObjectiveTo retrospectively analyze antibiotic resistance and clinical characteristics of Klebsiella pneumoniae strains for guiding the rational use of antibiotics in the area of the Bai nationality.MethodsThe antibiotic resistance and clinical characteristics of Klebsiella pneumoniae strains were retrospective analyzed, which were isolated from specimens of inpatients in First People’s Hospital of Dali between May 2016 and May 2017.ResultsAmong the 1 342 samples of various kinds of samples, 262 strains of Klebsiella pneumoniae were isolated, with the detection rate of 19.52% (262/1342). Clinical isolated strains were mainly from the new pediatric, intensive care unit, respiratory medicine, pediatrics, and mostly from sputum specimens (78.24%, 205/262). By screening of 22 kinds of antimicrobial agents, all strains had ampicillin resistance (100.00%), while none of these strains had ertapenem resistance. Extended-spectrum β-lactamases (ESBLs) positive strains’ resistance rate was higher than ESBLs negative strains (χ2=261.992, P<0.01). There were 76 drug resistant profiles, most of which were multidrug-resistant bacteria except 116 (44.27%) strains were resistant to ampicillin antibiotics only. And the number of strains in other resistant types ranged from 1 to 16. Only one of 262 strains had amikacin resistance, two of them were resistant to imipenem and meroenan.ConclusionsThere are many multidrug-resistant bacteria in Klebsiella pneumoniae in the population of Bai nationality, and there are no extensively drug resistant bacteria and pandrug-resistant bacteria strains. The strains of carbapene-resistant antibiotics should be worthy of clinical attention.
ObjectiveTo compare the clinical characteristics of patients with nosocomial and community infections with extended-spectrum beta-lactamase-containing Klebsiella pneumoniae (ESBL-KP) and non-ESBL-KP so as to improve clinical diagnosis and treatment outcomes.MethodsThis retrospective study determined the clinical features of patients with nosocomial and community infections with KP who were admitted to our hospital from January 1st, 2017 to June 30th, 2018. The chi-square test or Fisher's exact probability method were used to compare different groups.ResultsWe identified 334 strains of KP, and 83 (24.9%) of them strains were EBSL-KP. The percentages of ESBL-KP infections among those with nosocomial and community infections were similar (31.25% vs. 22.27%, χ2=2.955, P=0.086). Significantly more females than males had ESBL-KP infections (32.32 vs. 21.70%, χ2=4.208, P=0.040). The percentages of ESBL-KP infections were similar among <18 years-old group, 18 to 45 years-old group, 45 to 60 years-old group, and ≥60 years-old group. The three major locations of KP infections were the lower respiratory tract, urinary tract, and bloodstream (bacteremia). Among nosocomial KP infections, there were no significant differences in the percentages of ESBL-KP infections at different sites, nor in the hospital departments where patients were treated; among community KP infections, there were significant differences in the percentages of ESBLs-KP infections at different sites, and in the hospital departments where patients were treated. For community KP infections, the two most common infection sites were the urinary tract (37.74%) and the skin and soft tissue (30.77%), and most patients were treated in the urology department (40.00%) and respiratory medicine department (38.10%). ESBL-KP isolates had greater resistance than non-EBSL-KP isolates to 16 tested antibiotics (P<0.05). There were no statistically significant differences in the percentages of nosocomial infections and community infections among those with ESBL-KP and among those with non-ESBL-KP (P>0.05).ConclusionsOur population have high rates of nosocomial and community KP infections and of infections with ESBL-KP. It is necessary to strengthen the management and clinical use of antibiotics and to provide real-time surveillance of KP infections, especially for patients with ESBL-KP infections. Increased vigilance is required for KP infections of females and community KP infections to improve control of nosocomial infections and reduce the prevalence of cross-infections.
Functional imaging method of biological electrical characteristics based on magneto-acoustic effect gives valuable information of tissue in early tumor diagnosis, therein time and frequency characteristics analysis of magneto-acoustic signal is important in image reconstruction. This paper proposes wave summing method based on Green function solution for acoustic source of magneto-acoustic effect. Simulations and analysis under quasi 1D transmission condition are carried out to time and frequency characteristics of magneto-acoustic signal of models with different thickness. Simulation results of magneto-acoustic signal were verified through experiments. Results of the simulation with different thickness showed that time-frequency characteristics of magneto-acoustic signal reflected thickness of sample. Thin sample, which is less than one wavelength of pulse, and thick sample, which is larger than one wavelength, showed different summed waveform and frequency characteristics, due to difference of summing thickness. Experimental results verified theoretical analysis and simulation results. This research has laid a foundation for acoustic source and conductivity reconstruction to the medium with different thickness in magneto-acoustic imaging.
【摘要】 目的 探討中型和重型顱腦損傷后患者血小板（platelet，Plt）參數的變化特點及臨床意義。 方法 選取2009年3月-2010年3月腦外傷后24 h內入院的顱腦損傷患者75例作為觀察組，于傷后1、3、7、14 d采血測定Plt數量、血小板平均體積（mean platelet volume，MPV）、血小板體積分布寬度（platelet distribution width，PDW），并同時進行格拉斯哥昏迷評分（Glasgow coma scale，GCS）。同時選取60例健康體檢者，測定Plt、MPV和PDW作為對照組。 結果 觀察組傷后1、3、7 d Plt計數分別為（106.21±36.31）、（102.76±35.23）、（108.37±31.32）×109/L，較對照組[（210.41±68.56）×109/L]明顯降低（Plt;0.05）；觀察組傷后1、3、7 d MPV分別為（12.34±1.34）、（11.21±1.52）、（10.78±1.36） fL，PDW分別為（15.78±1.26）、（17.67±1.16）、（16.72±1.21） fL，均較對照組[MPV：（8.24±1.76） fL，PDW：（12.86±1.42） fL]明顯升高（Plt;0.05）；傷后14 d Plt、MPV和PDW均較對照組差異無統計學意義（Pgt;0.05）。GCS≤8分組傷后1 d Plt計數為（96.85±36.52）×109/L，明顯低于GCSgt;8分組[（123.85±35.78）×109/L]，而GCS≤8分組MPV為（12.14±1.32） fL，PDW為（18.63±1.21） fL，均明顯高于GCSgt;8分組[MPV：（9.78±1.34） fL，PDW：（16.72±1.34） fL]，差異均有統計學意義（Plt;0.05）。傷后第1天Plt與隨訪6個月GOS評分呈正相關（r=0.625，Plt;0.05）。 結論 中型和重型顱腦損傷后Plt計數明顯降低，MPV和PDW值明顯升高，且與傷情及預后有關。Plt及其參數的檢測有助于對傷情、預后的判斷。【Abstract】 Objective To investigate the platelet parameters changes and its clinical significance in medium and severe head injury patients. Methods From March 2009 to March 2010, 75 brain injury patients hospitalized within 24 h after injury were included in this study. The platelet number (Plt), mean platelet volume (MPV), platelet volume distribution width (PDW) and Glasgow coma scale were measured on the first, third, seventh and fourteenth day after injury respectively. We also measured the Plt, MPV and PDW of 60 healthy volunteers to make comparisons. Results The Plt counts were (106.21±36.31), (102.76±35.23), and (108.37±31.32)×109/L in the head injury patients on the first, third, and 7th day respectively, which were significantly lower than those in the control group [(210.41±68.56)×109/L] (Plt;0.05); the MPV and PDW values measured on the first day [MPV: (12.34±1.34) fL, PDW: (15.78±1.26) fL] and the third day [MPV: (11.21±1.52) fL, PDW: (17.67±1.16)fL] were both significantly lower than those of the control group (Plt;0.05); There was no evidence of a difference in Plt, MPV and PDW between the two groups fourteen day after injury (P＞0.05); The Plt count was (96.85±36.52)×109/L in GCS≤8 group on the first day, which was significantly lower than that of GCSgt;8 group [(123.85±35.78) fL, Plt;0.05]; However, the MPV and PDW values in GCS≤8 group [(MPV: (12.14±1.32) fL, PDW: (18.63±1.21) fL] were both significantly higher than those of GCSgt;8 group [MPV: (9.78±1.34) fL, PDW: (16.72±1.34) fL, Plt;0.05]; The Plt count was correlated with GOS score positively (r=0.625,Plt;0.05). Conclusions Medium and severe head injury patients are significantly associated with a lower Plt count and increased MPV and PDW values. The Plt parameters changes are correlated with the prognosis of patients. Therefore, the measurement of Plt parameters may contribute to the valuation of severity and prognosis, and provide new ideas for treatment of head injury patients.
