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Understanding the epidemic of chronic kidney disease of uncertain etiology may be critical for health policies and public health responses. Recent studies have shown that microplastics (MPs) contaminate our food chain and accumulate in the gut, liver, kidney, muscle, and so on. Humans manufacture many plastics-related products. Previous studies have indicated that particles of these products have several effects on the gut and liver. Polystyrene (PS)-MPs (PS-MPs) induce several responses, such as oxidative stress, and affect living organisms. The aim of this study was to investigate the effects of PS-MPs in kidney cells and . PS-MPs were evaluated in human kidney proximal tubular epithelial cells (HK-2 cells) and male C57BL/6 mice. Mitochondrial reactive oxygen species (ROS), endoplasmic reticulum (ER) stress, inflammation, and autophagy were analyzed in kidney cells. , we evaluated biomarkers of kidney function, kidney ultrastructure, muscle mass, and grip strength, and urine protein levels, as well as the accumulation of PS-MPs in the kidney tissue. Uptake of PS-MPs at different concentrations by HK-2 cells resulted in higher levels of mitochondrial ROS and the mitochondrial protein Bad. Cells exposed to PS-MPs had higher ER stress and markers of inflammation. MitoTEMPO, which is a mitochondrial ROS antioxidant, mitigated the higher levels of mitochondrial ROS, Bad, ER stress, and specific autophagy-related proteins seen with PS-MP exposure. Furthermore, cells exposed to PS-MPs had higher protein levels of LC3 and Beclin 1. PS-MPs also had changes in phosphorylation of mitogen-activated protein kinase (MAPK) and protein kinase B (AKT)/mitogen-activated protein kinase (mTOR) signaling pathways. In an study, PS-MPs accumulated and the treated mice had more histopathological lesions in the kidneys and higher levels of ER stress, inflammatory markers, and autophagy-related proteins in the kidneys after PS-MPs treatment by oral gavage. The results suggest that PS-MPs caused mitochondrial dysfunction, ER stress, inflammation, and autophagy in kidney cells and accumulated in HK-2 cells and in the kidneys of mice. These results suggest that long-term PS-MPs exposure may be a risk factor for kidney health. https://doi.org/10.1289/EHP7612.

Particulate matter 2.5 (PM2.5) is a risk factor for lung cancer. In this study, we investigated the molecular mechanisms of PM2.5 exposure on lung cancer progression. We found that short‐term exposure to PM2.5 for 24 h activated the EGFR pathway in lung cancer cells (EGFR wild‐type and mutant), while long‐term exposure of lung cancer cells to PM2.5 for 90 days persistently promoted EGFR activation, cell proliferation, anchorage‐independent growth, and tumor growth in a xenograft mouse model in EGFR‐driven H1975 cancer cells. We showed that PM2.5 activated AhR to translocate into the nucleus and promoted EGFR activation. AhR further interacted with the promoter of TMPRSS2, thereby upregulating TMPRSS2 and IL18 expression to promote cancer progression. Depletion of TMPRSS2 in lung cancer cells suppressed anchorage‐independent growth and xenograft tumor growth in mice. The expression levels of TMPRSS2 were found to correlate with nuclear AhR expression and with cancer stage in lung cancer patient tissue. Long‐term exposure to PM2.5 could promote tumor progression in lung cancer through activation of EGFR and AhR to enhance the TMPRSS2‐IL18 pathway. Synopsis PM2.5 promotes lung cancer progression through activation of the AhR‐TMPRSS2‐IL18. Exposure to PM2.5 activates EGFR pathway and promotes lung cancer progression. Long‐term exposure to PM2.5 increases lung cancer cell proliferation, anchorage‐independent growth, and xenograft tumor growth in mice. PM2.5 activates AhR to translocate into the nucleus and upregulates the expression of TMPRSS2. Depletion of TMPRSS2 in lung cancer cells suppresses anchorage‐independent growth and xenograft tumor growth in mice. TMPRSS2 upregulates IL I8 expression and promotes lung cancer progression. Graphical Abstract PM2.5 promotes lung cancer progression through activation of the AhR‐TMPRSS2‐IL18.

Autophagy is a major degradation pathway that utilizes lysosome hydrolases to degrade cellular constituents and is often induced under cellular stress conditions to restore cell homeostasis. Another prime degradation pathway in the cells is ubiquitin-proteasome system (UPS), in which proteins tagged by certain types of polyubiquitin chains are selectively recognized and removed by proteasome. Although the two degradation pathways are operated independently with different sets of players, recent studies have revealed reciprocal cross talks between UPS and autophagy at multiple layers. In this review, we summarize the roles of protein ubiquitination and deubiquitination in controlling the initiation, execution, and termination of bulk autophagy as well as the role of ubiquitination in signaling certain types of selective autophagy. We also highlight how dysregulation of ubiquitin-mediated autophagy pathways is associated with a number of human diseases and the potential of targeting these pathways for disease intervention.

Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria. We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist's structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists' clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy. Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1) six symptom criteria, (2) four functional impairment criteria and (3) exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%), while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use. The diagnostic criteria of smartphone addiction demonstrated the core symptoms \"impaired control\" paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment.

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique’s response and the target analyte’s standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument’s response is dependent. The inverse equation is the opposite: the instrument’s response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument’s response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced.

Many types of thermometers have been developed to measure body temperature. Infrared thermometers (IRT) are fast, convenient and ease to use. Two types of infrared thermometers are uses to measure body temperature: tympanic and forehead. With the spread of COVID-19 coronavirus, forehead temperature measurement is used widely to screen people for the illness. The performance of this type of device and the criteria for screening are worth studying. This study evaluated the performance of two types of tympanic infrared thermometers and an industrial infrared thermometer. The results showed that these infrared thermometers provide good precision. A fixed offset between tympanic and forehead temperature were found. The measurement values for wrist temperature show significant offsets with the tympanic temperature and cannot be used to screen fevers. The standard operating procedure (SOP) for the measurement of body temperature using an infrared thermometer was proposed. The suggestion threshold for the forehead temperature is 36 °C for screening of fever. The body temperature of a person who is possibly ill is then measured using a tympanic infrared thermometer for the purpose of a double check.

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s, was used as criteria to determine the fitting performance. The Prediction Sum of Squares (PRESS) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation’s fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable. Quantitative phase imaging suffers from a lack of specificity in label-free imaging. Here, the authors introduce Phase Imaging with Computational Specificity (PICS), a method that combines phase imaging with machine learning techniques to provide specificity in unlabeled live cells with automatic training.

Protein therapy holds great promise for treating a variety of diseases. To act on intracellular targets, therapeutic proteins must cross the plasma membrane. This has previously been achieved by covalent attachment to a variety of cell-penetrating peptides (CPPs). However, there is limited information on the relative performance of CPPs in delivering proteins to cells, specifically the cytosol and other intracellular locations. Here we use green fluorescent protein (GFP) as a model cargo to compare delivery capacity of five CPP sequences (Penetratin, R8, TAT, Transportan, Xentry) and cyclic derivatives in different human cell lines (HeLa, HEK, 10T1/2, HepG2) representing different tissues. Confocal microscopy analysis indicates that most fusion proteins when incubated with cells at 10 µM localise to endosomes. Quantification of cellular uptake by flow cytometry reveals that uptake depends on both cell type (10T1/2 > HepG2 > HeLa > HEK), and CPP sequence (Transportan > R8 > Penetratin≈TAT > Xentry). CPP sequence cyclisation or addition of a HA-sequence increased cellular uptake, but fluorescence was still contained in vesicles with no evidence of endosomal escape. Our results provide a guide to select CPP for endosomal/lysosomal delivery and a basis for developing more efficient CPPs in the future.

The aim of this study was to develop a self-administered scale based on the special features of smartphone. The reliability and validity of the Smartphone Addiction Inventory (SPAI) was demonstrated. A total of 283 participants were recruited from Dec. 2012 to Jul. 2013 to complete a set of questionnaires, including a 26-item SPAI modified from the Chinese Internet Addiction Scale and phantom vibration and ringing syndrome questionnaire. There were 260 males and 23 females, with ages 22.9 ± 2.0 years. Exploratory factor analysis, internal-consistency test, test-retest, and correlation analysis were conducted to verify the reliability and validity of the SPAI. Correlations between each subscale and phantom vibration and ringing were also explored. Exploratory factor analysis yielded four factors: compulsive behavior, functional impairment, withdrawal and tolerance. Test-retest reliabilities (intraclass correlations  = 0.74-0.91) and internal consistency (Cronbach's α = 0.94) were all satisfactory. The four subscales had moderate to high correlations (0.56-0.78), but had no or very low correlation to phantom vibration/ringing syndrome. This study provides evidence that the SPAI is a valid and reliable, self-administered screening tool to investigate smartphone addiction. Phantom vibration and ringing might be independent entities of smartphone addiction.