An Analysis of the Latent Class and Influencing Factors of Problematic Mobile Social Media Usage Among Chinese College Students.

Purpose: To explore the potential classification of Problematic Mobile Social Media Usage (PMSMU) in Chinese college students, analyze whether there is group heterogeneity in PMSMU, and discuss the differences in the latent profile of PMSMU in fear of missing out, online positive feedback, and boredom proneness. Methods: A total of 2591 Chinese college students were investigated using the Problematic Mobile Social Media Usage Questionnaire, Fear of Missing Out (FOMO) Scale, Online Positive Feedback Scale and Short-form Boredom Proneness Scale, and heterogeneity was tested by latent profile analysis. Results: The PMSMU of college students can be divided into three latent profiles: no-problem use group (26.44%), mild problem use group (56.66%), and severe problem use group (16.91%). Male students, as compared to female students, showed a significantly lower likelihood of being classified as mild problematic users (OR=0.50, p<0.001) and severe problematic users (OR=0.29, p<0.001). Additionally, students with higher levels of FOMO, a stronger craving for online positive feedback, and increased boredom proneness are more likely to belong to the severe problematic use group (OR=2.91, p<0.001; OR=1.42, p<0.01; OR=8.72, p<0.001). Conclusion: The results of this study highlight the factors influencing the heterogeneity of individual PMSMU. Specifically, female college students and those with a higher fear of missing out, greater susceptibility to boredom, and a stronger craving for positive online feedback are more likely to exhibit severe PMSMU. These findings provide valuable empirical evidence for developing preventive strategies to address PMSMU among college students.

Difference in quantitative MRI measurements of cartilage between Wiberg type III patella and stable patella based on a 3.0-T synthetic MRI sequence.

Purpose: The purpose of this study was to investigate the difference between the quantitative MRI values of Wiberg type III and stable patellar cartilage, and to improve the accuracy of MRI quantification in early patellar cartilage damage. Methods: The knee joints of 94 healthy volunteers were scanned by a GE Signa Pioneer 3.0-T synthetic MRI machine. According to the Wiberg classification, the patella was divided into types I-III. Types I-II made up the stable patella group, and type III made up the unstable patella group. Two radiologists independently measured patellar cartilage thickness and quantitative synthetic MRI values (T1, T2, PD) in both groups. Interobserver agreement for quantitative variables was assessed using the Bland-Altman method. A third radiologist assessed differences in measurements. Results: The medial T2 and T1 value of Wiberg III patella did not show a normal distribution (all P > 0.05). Compared with the stable group, the Wiberg type III group had thinner cartilage of the medial surface of the patella (P < 0.05), lower cartilage T2 and PD values (P < 0.05), but a similar cartilage T1 value (P > 0.05). There was no significant difference in the cartilage thickness, T1, T2, or PD value of the lateral patella between the Wiberg type III and the stable group (P > 0.05). Conclusion: There were certain differences in the cartilage thickness of the medial surface of the patella and the quantitative value of synthetic MRI in Wiberg type III patellas. Quantitative studies of patellar cartilage MRI measurements need to consider the influence of patellar morphology.

Association between vitamin D deficiency and diabetic foot ulcer wound in diabetic subjects: A meta-analysis.

A meta-analysis was performed to evaluate the association between vitamin D deficiency and diabetic foot ulcer wounds in diabetic subjects. A systematic literature search up to March 2022 incorporated 7586 subjects with diabetes mellitus at the beginning of the study; 1565 were using diabetic subjects with foot ulcer wounds, and 6021 were non-ulcerated diabetic subjects. Statistical tools like the dichotomous and contentious method were used within a random or fixed-influence model to establish the odds ratio (OR) and mean difference (MD) with 95% confidence intervals (CIs) to evaluate the influence of vitamin D deficiency in managing diabetic foot ulcer wound. Diabetic subjects with foot ulcer wounds had significantly lower vitamin D levels (MD, -6.48; 95% CI, -10.84 to -2.11, P < .004), higher prevalence of vitamin D deficiency (<50 nmoL/L) (OR, 1.82; 95% CI, 1.32-2.52, P < .001), and higher prevalence of severe vitamin D deficiency (OR, 2.53; 95% CI, 1.65-3.89, P < .001) compared with non-ulcerated diabetic subjects. Diabetic subjects with foot ulcer wounds had significantly lower vitamin D levels, higher prevalence of vitamin D deficiency, and higher prevalence of severe vitamin D deficiency compared with non-ulcerated diabetic subjects. Further studies are required to validate these findings.

Formation of crystalline multimetallic layered double hydroxide precipitates during uptake of Co, Ni, and Zn onto γ-alumina: Evidence from EXAFS, XRD, and TEM.

While the phenomenon of surface adsorption of heavy metals occurring at the mineral-water interface is well understood, the mechanisms of surface precipitation in controlling the fate of heavy metals in soils and water have not been clearly addressed. In this research, we used a combination of extended X-ray absorption fine structure (EXAFS) spectroscopy, high-resolution transmission electron microscopy (HRTEM), and X-ray diffraction (XRD) to determine the uptake mechanisms of Co, Ni, and Zn on γ-Al2O3 at pH 7.5. EXAFS analysis revealed the formation of multimetallic layered double hydroxides (LDHs), and the Me-Me distances (Me = Co, Ni, and Zn) of the multimetallic LDH were inversely correlated with the molar ratio of the sorbed Ni and the sorbed total metals. The HRTEM analysis showed that flake or needle-like shapes of the LDH precipitate formed at the nanoscale. Additionally, XRD suggested that these multimetallic LDHs were crystalline, and the crystallinity was dependent on the heavy metal type. This provides, for the first time, experimental evidence for the formation of CoNiZn-Al LDH precipitates at mineral-water interfaces. These results have pronounced environmental implications in heavy metal remediation, reactive transport modeling, and environmental risk assessment.

Light responsive Fe-Tcpp@ICG for hydrogen peroxide detection and inhibition of tumor cell growth.

In this work, we synthesized Fe-Tcpp@ICG(ICG, Indocyanine green) with light stimuli-response through 5,10,15,20-tetrakis (4-carboxyphenyl) porphyrin (Fe-Tcpp) loaded ICG by electrostatic adsorption. The morphology and properties of Fe-Tcpp and Fe-Tcpp@ICG were characterised by ultraviolet-visible absorption spectrometer, X-ray diffraction, thermogravimetric analyzer and transmission electron microscope, respectively. A non-enzymatic photoelectrochemical sensor based on Fe-Tcpp@ICG was constructed to quantitatively detect hydrogen peroxide in tumor microenvironment. Under the optimal conditions, the linear range of detecting hydrogen peroxide was 0.01-50 mmol/L with detection limit of 0.2 µmol/L (S/N = 3). This sensor proposed a simple, fast, sensitive and label-free method for the detection of hydrogen peroxide. Moreover, the results also showed that the Fe-Tcpp@ICG can catalyze the decomposition of hydrogen peroxide to generate singlet oxygen, which can kill tumor cells. These indicated that this material was expected to be used for detecting hydrogen peroxide and inhibition of tumor cell growth.

Photoelectric effect driving PANI/PB multicolor visualized detection of CEA based on Ag2S NPs@ZnO NTs.

In this work, a multicolor visual immunoassay platform was developed. The photoelectric effects of Ag2S NPs@ZnO NTs made the color changes of PANI/PB, which enabled visual inspection of CEA. Under the visible light excitation, Ag2S NPs@ZnO NTs generates electron-holes. Where, photoelectrons will pass electrical circuit to PB and photoinduced holes will oxidize PANI, which making the PANI/PB composite changes from emerald green-blue-purple-black colors. When CEA was incubated, the migration rate of photogenerated carriers is slowed down owing to the steric hindrance, resulting in different color changes of PANI/PB. In addition, the average green channel of PANI/PB read by photoshop has a certain correlated linear relationship with the concentration of CEA. Meanwhile, we can observe the color transformation of PANI/PB with our own eyes. By integrating advantages of photoelectrochemistry and colorimetry, the linear range of CEA detection was 0.1-20 ng/mL, and the detection limit was 0.05 ng/mL (S/N = 3). More importantly, this multicolor sensing method is very convenient, simple and low-cost. The photocarriers-modulated colorimetric strategy also provides a novel idea for visual portable platform design in clinical diagnosis.

[Quantitative Detection of Iron in Soybean Oils with Laser Induced Breakdown Spectroscopy and Simple Regression Methods].

LIBS (laser-induced breakdown spectroscopy) was used to detect Fe element content in soybean oil quantitatively. In this experiment, a series of soybean oil samples with different concentrations of Fe were used; LIBS spectra were collected with a two-channel high precision spectrometer. According to the LIBS spectrum of samples, two characteristic wavelength of Fe (404.58 and 406.36 nm) were determined, and different simple regression methods (exponential regression, linear regression and quadratic regression) were used to establish the quantitative analysis models of Fe content using each characteristic spectral line. The results indicate that the average relative error of Fe I 404.58 and Fe I 406.36 in simple exponential regression, linear regression and quadratic regression models were 29.49%, 8.93%, 8.70% and 28.95%, 8.63%, 8.44%, respectively. The results of Fe I 406.36 regression models is better than that of Fe I 404.58, and the quadratic regression model is optimal among the three regression models. According to these results, LIBS technology has certain feasibility for detecting Fe in soybean oil; the quadratic linear regression model can improve the prediction accuracy of Fe element effectively.

[Model Optimization of Ternary System Adulteration Detection in Camellia Oil Based on Visible/Near Infrared Spectroscopy].

Visible/near infrared spectroscopy combined with chemometrics methods was used to detect ternary system adulteration in camellia oil quantificationally. In order to get adulterated samples, rapeseed oil and peanut oil were added to pure camellia oil in different proportion. Visible/near infrared spectroscopy data of pure and adulterated camellia oil samples were acquired in the wavelength range of 350~1800nm, and samples were randomly divided into calibration set and prediction set. The adulteration models were optimized by comparing different wavelength ranges, pretreatment methods and calibration methods The results show that the optimal modeling wavelength ranges and pretreatment methods for the prediction models of rapeseed oil, peanut oil and total adulteration amount are 750~1 770, 900~1 770, 870~1 770 nm and Multiple scattering correction (MSC), Standard normal variate (SNV) and second order differentia, and the best modeling method is Least square support vector machine (LSSVM). The correlation coefficient (R(P)) in prediction set and the root mean square error predictions(RMSEPs) of optimal adulteration models for rapeseed oil, peanut oil and total adulteration are 0.963, 0.982, 0.993 and 2.1%, 1.5%, 1.8%, respectively. Thus it can be seen that visible /near infrared spectroscopy combined with chemometrics methods can be used for quantitative ternary system adulteration detection in camellia oil.

[Qualitative Detection of Procymidone in Edible Vegetable Oils by Near Infrared Spectroscopy and Variable Selection Methods].

In this research, near infrared (NIR) spectroscopy was used to detect procymidone in edible vegetable oils qualitatively. Edible vegetable oil samples with different procymidone contents were classified to two groups according to boundary line of maximum residue limit of procymidone in national standard. QualitySpec spectrometer was used to acquire spectra of two group samples, then uninformative variable elimination (UVE) and subwindow permutation analysis (SPA) were used to select informative wavelength variables. At last, several methods such as linear discriminant analysis (LDA), partial least squares-linear discriminant analysis (PLS-LDA) and discriminant partial least squares (DPLS) were used to develop classification models. The results indicate that NIR spectroscopy is feasible to classify the two group samples. UVE method can select informative wavelength variables effectively, and improve the performance of classification model. The best model is developed by UVE-DPLS method, the classification correct rate, sensitivity and specificity of prediction samples in this model are 98.7%, 95.0% and 100.0%, respectively.

Detection of Chromium Content in Soybean Oil by Laser Induced Breakdown Spectroscopy and UVE Method.

In order to monitor chromium (Cr) content in soybean oil, laser induced breakdown spectroscopy (LIBS) was used to detect Cr content in this research. Pine wood chips was used to enrich heavy metal of Cr, and the spectra of pine wood chips were acquired in the wavelength range of 206.28~481.77 nm by a two-channel high-precision spectrometer. Then, uninformative variable elimination (UVE) method was used to select sensitive wavelength variables for heavy metal of Cr, and calibration model of Cr in soybean oil was developed with partial least squares (PLS) regression, the performance of the calibration model was compared to univariate and full PLS calibration models. The results indicate that the performance of UVE-PLS calibration model is better than that of univariate and full PLS calibration models, the correlation coefficient, root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) are 0.990, 0.045 mg·g-1, 0.050 mg·g-1 and 0.054 mg·g-1, respectively. After UVE variable selection, the number of wavelength variables in UVE-PLS calibration model is about 2% of wavelength variables in full PLS calibration model. This means UVE is an effective variable selection method which can select correlative variables for heavy metal of Cr.