Wnt10b knockdown promotes UCP1 expression in brown adipose tissue in mice.

The effect of Wnt10b overexpression on adipose tissue development has been reported. However, the impact of Wnt10b knockdown on the function of brown adipose tissue (BAT) is yet largely unknown. Here, we used the CRISPR/Cas9 technique to generate Wnt10b-knockdown (Wnt10b+/- ) mice. We compared the development and thermogenic gene expression of interscapular BAT (iBAT) between Wnt10b+/- and Wnt10b+/+ mice under a chow diet, high-fat diet (HFD), and cold exposure conditions. Moreover, the effect of Wnt10b knockdown on brown adipocyte function was tested via in vitro experiments. Results indicated that Wnt10b knockdown decreased the iBAT mass and the brown adipocyte size and enhanced thermogenic gene expression, including UCP1, under chow diet conditions. In addition, Wnt10b+/- mice appeared to be able to maintain their body temperature better than the control in a cold environment, accompanied by higher UCP1 protein expression. Intriguingly, even under HFD conditions, Wnt10b+/- mice still showed higher UCP1 expression, which was associated with an alleviated obesity phenotype. In vitro studies further evidenced the Wnt10b knockdown stimulation of UCP1 expression and suppression of the adipogenic program. This study indicates that Wnt10b knockdown enhances UCP1 expression and inhibits the adipogenic differentiation of brown adipocytes, providing a novel option for therapeutic interventions in adiposity.

Wnt10b knockdown regulates the relative balance of adipose tissue-resident T cells and inhibits white fat deposition.

BACKGROUND: Wnt10b is one of critical Wnt family members that being involved in networks controlling stemness, pluripotency and cell fate decisions. However, its role in adipose-resident T lymphocytes and further in fat metabolism yet remains largely unknown. METHODS AND RESULTS: In the present study, we demonstrated a distinctive effect for Wnt10b on the relative balance of T lymphocytes in adipose tissue by using a Wnt10b knockdown mouse model. Wnt10b knockdown led to a reduction of adipose-resident CD4+ T cells and an elevation of Foxp3+/CD4+ Treg cells. Wnt10b-knockdown mice fed with standard diet showed less white fat deposition owing to the suppressed adipogenic process. Moreover, under high fat diet conditions, Wnt10b knockdown resulted in an alleviated obesity symptoms, as well as an improvement of glucose homeostasis and hepatic steatosis. CONCLUSIONS: Collectively, we reveal an unexpected and novel function for Wnt10b in mediating the frequency of adipose-resident T cell subsets, that when knockdown skewing toward a Treg-dominated phenotype and further improving fat metabolism.

Direct Photo-Patterning of Efficient and Stable Quantum Dot Light-Emitting Diodes via Light-Triggered, Carbocation-Enabled Ligand Stripping.

Next generation displays based on quantum dot light-emitting diodes (QLEDs) require robust patterning methods for quantum dot layers. However, existing patterning methods mostly yield QLEDs with performance far inferior to the state-of-the-art individual devices. Here, we report a light-triggered, carbocation-enabled ligand stripping (CELS) approach to pattern QLEDs with high efficiency and stability. During CELS, photogenerated carbocations from triphenylmethyl chlorides remove native ligands of quantum dots, thereby producing patterns at microscale precision. Chloride anions passivate surface defects and endow patterned quantum dots with preserved photoluminescent quantum yields. It works for both cadmium-based and heavy-metal-free quantum dots. CELS-patterned QLEDs show remarkable external quantum efficiencies (19.1%, 17.5%, 12.0% for red, green, blue, respectively) and a long operation lifetime (T95 at 1000 nits up to 8700 h). Both are among the highest for patterned QLEDs and approach the records for nonpatterned devices, which makes CELS promising for building high-performance QLED displays and related integrated devices.

Ion Addition by Electrolysis to Improve the Quantitative Analysis of Bacteria with MALDI-TOF MS.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is commonly applied to the identification of bacteria but rarely used for quantitative detection due to the inhomogeneous crystallization of the matrix leading to the unsatisfactory linear relationship between the sample amount and the mass spectrum signals. Herein, we proposed a noninterference ion addition (NIA) method by electrolysis to improve homogeneous crystallization during the evaporation progress of sample droplets on the target plates. The active metal wire was inserted in the droplet as the anode electrode, and metal ions were released through electrolysis. The directional migration of metal ions under the electric field can hinder the migration of matrix molecules to the boundary and homogenize the matrix crystals by forming spherical crystals. Simultaneously, trace cationic surfactant was added to the droplet for pinning the contact surface to define the circle crystallization region. The metal ions from the anode electrode wire were deposited on the surface of the target plates which served as the cathode. Therefore, ion addition has no interference effect on ionization during MALDI-MS detection. This NIA method benefits the homogeneous crystallization and so improves the quantitative analysis. NIA is suitable for biological samples with different matrices, and bacterial samples could be quantitatively analyzed.

Hydrogen Sulfide Regulates Glucose Uptake in Skeletal Muscles via S-Sulfhydration of AMPK in Muscle Fiber Type-Dependent Way.

BACKGROUND: The effect of hydrogen sulfide (H2S) on glucose homeostasis remains to be elucidated, especially in the state of insulin resistance. OBJECTIVES: In the present study, we aimed to investigate H2S-regulated glucose uptake in the M. pectoralis major (PM) muscle (which mainly consists of fast-twitch glycolytic fibers) and M. biceps femoris (BF) muscle (which mainly consists of slow-twitch oxidative fibers) of the chicken, a potential model of insulin resistance. METHODS: Chicks were subjected to intraperitoneal injection of sodium hydrosulfide (NaHS, 50 µmol/kg body mass/day) twice a day to explore glucose homeostasis. In vitro, myoblasts from PM and BF muscles were used to detect glucose uptake and utilization. Effects of AMP-activated protein kinase (AMPK) phosphorylation, AMPK S-sulfhydration, and mitogen-activated protein kinase (MAPK) pathway induction by NaHS were detected. RESULTS: NaHS enhanced glucose uptake and utilization in chicks (P < 0.05). In myoblasts from PM muscle, NaHS (100 µM) increased glucose uptake by activating AMPK S-sulfhydration, AMPK phosphorylation, and the AMPK/p38 MAPK pathway (P < 0.05). However, NaHS decreased glucose uptake in myoblasts from BF muscle by suppressing the p38 MAPK pathway (P < 0.05). Moreover, NaHS increased S-sulfhydration and, in turn, the phosphorylation of AMPK (P < 0.05). CONCLUSIONS: This study reveals the role of H2S in enhancing glucose uptake and utilization in chicks. The results suggest that NaHS is involved in glucose uptake in skeletal muscle in a fiber type-dependent way. The AMPK/p38 pathway and protein S-sulfhydration promote glucose uptake in fast-twitch glycolytic muscle fibers, which provides a muscle fiber-specific potential therapeutic target to ameliorate glucose metabolism.

Differentiation and Identification of Endophytic Bacteria from Populus Based on Mass Fingerprints and Gene Sequences.

Plant endophytic bacteria play important roles in plants' growth and resistance to stress. It is important to characterize endophytic bacteria to be able to understand their benefits. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has become a powerful technique for bacterial identification due to its high throughput and simple procedures. In this study, the endophytic bacteria separated from Populus (the leaves, roots and stems of Populus tomentosa Carrière; stems of Populus nigra Linn. var. nigra; and stems of Populus canadensis Moench) were identified and classified based on MALDI-TOF MS data and 16S rRNA gene sequencing. The sampling and preparation of bacteria were optimized to obtain meaningful protein mass fingerprints. The composite correlation index (CCI) values of the inter-genera and inter-species protein mass fingerprints demonstrated sufficient differences between the strains. In the CCI value matrix for ten species in the same genus, all the CCI values were less than 0.5. Among the species, 95.6% of all the CCI values were less than 0.5. After data processing, the classification capacity of the protein mass fingerprints was verified using inter-specific and inter-generic PCoA. To compare different methods' potential for differentiation and phylogenetic analysis, a dendrogram of the MS profiles and a phylogenetic tree based on the 16S rRNA gene sequences were constructed using 61 endophytic bacteria found in Populus. The clustering and grouping results show that the phylogenetic analysis based on MALDI-TOF MS is similar to that based on 16S rRNA gene sequencing. This study provides a valuable reference for differentiating and identifying endophytic bacteria according to their protein mass fingerprints.

The regulating pathway of creatine on muscular protein metabolism depends on the energy state.

Creatine (Cr) is beneficial for increasing muscle mass and preventing muscle atrophy via involving in energy metabolism through the Cr and phosphocreatine (PCr) system. This study aimed to evaluate the supplemental effect of Cr on protein metabolism under normal and starvation conditions. The primary myoblasts were obtained from the breast muscle of chicks. The mammalian target of rapamycin (mTOR)/P70S6 kinase (P70S6K), ubiquitin-proteasome (UP) pathways, and mitochondrial function of myotubes were evaluated at normal or starvation state and with or without glucose supplementation. Under normal condition, Cr supplementation enhanced protein synthesis rate as well as upregulated the total and phosphorylated P70S6K expressions. Cr had little influence on protein catabolism and mitochondrial function. In a starvation state, however, Cr alleviated myotube atrophy and enhanced protein accretion by inhibiting Atrogin1 and myostatin (MSTN) expression. Furthermore, Cr treatment upregulated the transcriptional coactivators peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) expression and decreased reactive oxygen species (ROS) accumulation under starvation condition. In the presence of glucose, however, the favorable effect of Cr on protein content and myotube diameter did not occur under starvation condition. The present result indicates that at a normal state, Cr stimulated protein synthesis via the mTOR/P70S6K pathway. In a starvation state, Cr mainly takes a favorable effect on protein accumulation via suppression of the UP pathway and mediated mitochondrial function mainly by serving as an energy supplier. The result highlights the potential clinical application for the modulation of muscle mass under different nutritional conditions.

A Specific Mass-Tag Approach for Detection of Foodborne Pathogens Using MALDI-TOF Mass Spectrometry.

Pathogen infections present a considerable threat to global health owing to the high morbidity and mortality, and usually multiple pathogens coexist in food and the environment. Consequently, it is in urgent need to develop some multiplexed and sensitive approaches for pathogen detection. Here, we presented a novel strategy using mass tag-mediated surface engineering for simultaneous detection of multiple bacteria by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). Following aptamer binding, primer amplification, and DNA hybridization, bacteria were specifically labeled by their corresponding mass tags, which could be released and ionized after laser irradiation. This strategy converted the detection of bacteria to the analysis of mass tags, allowing simultaneous detection of multiple bacteria and avoiding the dependence of microbial mass spectra databases. In addition, this approach applied two rolling circle amplification (RCA) reactions to improve both the capture efficiency and detection sensitivity of the target bacteria. The specificity and the real sample detection were evaluated, and the results demonstrated a potential application of this approach in milk safety monitoring.

FGF2 positively regulates osteoclastogenesis via activating the ERK-CREB pathway.

Fibroblast growth factor 2 (FGF2) plays crucial roles in the growth and development of several tissues. However, its function in bone homeostasis remains controversial. Here, we found that exogenous FGF2 supplementation inhibited the mineralization of bone marrow stromal cells (BMSCs), at least partially, via up-regulating the gene expression of osteoclastogenesis. The FGF receptor (FGFR) allosteric antagonist SSR128129E modestly, whereas the FGFR tyrosine kinase inhibitor AZD4547 significantly antagonized the effects of FGF2. Mechanistically, FGF2 stimulated ERK phosphorylation, and the ERK signaling inhibitor PD325901 strongly blocked FGF2 enhancement of osteoclastogenesis. Moreover, the phosphorylation of CREB was also activated in response to FGF2, thereby potentiating the interaction of p-CREB with the promoter region of Rankl gene. Notably, FGF2-deficient BMSCs exhibited higher mineralization capability and lower osteoclastogenic gene expression. Correspondingly, FGF2-knockout mice showed increased bone mass and attenuated expression of osteoclast-related markers, which were associated with moderate inhibition of the ERK signaling. In conclusion, FGF2 positively regulates osteoclastogenesis via stimulating the ERK-CREB pathway. These findings establish the importance of FGF2 in bone homeostasis, hinting the potential use of FGF2/ERK/CREB specific inhibitors to fight against bone-related disorders, such as osteoporosis.

The fibroblast growth factor receptor antagonist SSR128129E inhibits fat accumulation via suppressing adipogenesis in mice.

BACKGROUND: AS an allosteric inhibitor of fibroblast growth factor receptors (FGFRs), SSR128129E (SSR) extensively inhibits the fibroblast growth factor (FGF) signaling. Given the metabolic importance of FGFs and the global epidemic of obesity, we explored the effect of SSR on fat metabolism. METHODS AND RESULTS: Three-week-old male mice were administered intragastrically with SSR (30 mg/kg/day) or PBS for 5 weeks. The effects of SSR on white and brown fat metabolism were investigated by respiratory metabolic monitoring, histological assessment and molecular analysis. Results indicated that SSR administration significantly reduced the body weight gain and the fat content of mice. SSR did not increase, but decreased the thermogenic capability of both brown and white fat. However, SSR markedly suppressed adipogenesis of adipose tissues. Further study demonstrated the involvement of ERK signaling in the action of SSR. CONCLUSIONS: SSR may be a promising drug candidate for the prevention of obesity via suppressing adipogenesis. However, the influence of SSR on thermogenesis in humans should be further investigated before its clinical application.

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.

OBJECTIVES: To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients. METHODS: This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1-3, day 3-5, or day 1-5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis. RESULTS: Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3-5 data performed better than day 1-3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset. CONCLUSIONS: Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.

[Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method].

The finite element method is a new method to study the mechanism of brain injury caused by blunt instruments. But it is not easy to be applied because of its technology barrier of time-consuming and strong professionalism. In this study, a rapid and quantitative evaluation method was investigated to analyze the craniocerebral injury induced by blunt sticks based on convolutional neural network and finite element method. The velocity curve of stick struck and the maximum principal strain of brain tissue (cerebrum, corpus callosum, cerebellum and brainstem) from the finite element simulation were used as the input and output parameters of the convolutional neural network The convolutional neural network was trained and optimized by using the 10-fold cross-validation method. The Mean Absolute Error (MAE), Mean Square Error (MSE), and Goodness of Fit ( R 2) of the finally selected convolutional neural network model for the prediction of the maximum principal strain of the cerebrum were 0.084, 0.014, and 0.92, respectively. The predicted results of the maximum principal strain of the corpus callosum were 0.062, 0.007, 0.90, respectively. The predicted results of the maximum principal strain of the cerebellum and brainstem were 0.075, 0.011, and 0.94, respectively. These results show that the research and development of the deep convolutional neural network can quickly and accurately assess the local brain injury caused by the sticks blow, and have important application value for understanding the quantitative evaluation and the brain injury caused by the sticks struck. At the same time, this technology improves the computational efficiency and can provide a basis reference for transforming the current acceleration-based brain injury research into a focus on local brain injury research.

Strike Velocity Prediction of Stick Blunt Instruments Based on Backpropagation Neural Network.

OBJECTIVES: To analyze and predict the striking velocity range of stick blunt instruments in different populations, and to provide basic data for the biomechanical analysis of blunt force injuries in forensic identification. METHODS: Based on the Photron FASTCAM SA3 high-speed camera, Photron FASTCAM Viewer 4.0 and SPSS 26.0 software, the tester's maximum striking velocity of stick blunt instruments and related factors were calculated and analyzed, and inputed to the backpropagation (BP) neural network for training. The trained and verified BP neural network was used as the prediction model. RESULTS: A total of 180 cases were tested and 470 pieces of data were measured. The maximum striking velocity range was 11.30-35.99 m/s. Among them, there were 122 female data, the maximum striking velocity range was 11.63-29.14 m/s; there were 348 male data, the maximum striking velocity range was 20.11-35.99 m/s. The maximum striking velocity of stick blunt instruments increased with the increase of weight and height, but there was no obvious increase trend in the male group; the maximum striking velocity decreased with age, but there was no obvious downward trend in the female group. The maximum striking velocity of stick blunt instruments has no significant correlation with the material and strike posture. The root mean square error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) of the prediction results by using BP neural network were 2.16, 1.63 and 0.92, respectively. CONCLUSIONS: The prediction model of BP neural network can meet the demand of predicting the maximum striking velocity of different populations.

Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.

PURPOSE: This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy. MATERIALS AND METHOD: This study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002-2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline-cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests. RESULTS: Molecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3-5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance. CONCLUSION: AI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.

Survival of COVID-19 Patients With Respiratory Failure is Related to Temporal Changes in Gas Exchange and Mechanical Ventilation.

Background: Respiratory failure due to coronavirus disease of 2019 (COVID-19) often presents with worsening gas exchange over a period of days. Once patients require mechanical ventilation (MV), the temporal change in gas exchange and its relation to clinical outcome is poorly described. We investigated whether gas exchange over the first 5 days of MV is associated with mortality and ventilator-free days at 28 days in COVID-19. Methods: In a cohort of 294 COVID-19 patients, we used data during the first 5 days of MV to calculate 4 daily respiratory scores: PaO2/FiO2 (P/F), oxygenation index (OI), ventilatory ratio (VR), and Murray lung injury score. The association between these scores at early (days 1-3) and late (days 4-5) time points with mortality was evaluated using logistic regression, adjusted for demographics. Correlation with ventilator-free days was assessed (Spearman rank-order coefficients). Results: Overall mortality was 47.6%. Nonsurvivors were older (P < .0001), more male (P = .029), with more preexisting cardiopulmonary disease compared to survivors. Mean PaO2 and PaCO2 were similar during this timeframe. However, by days 4 to 5 values for all airway pressures and FiO2 had diverged, trending lower in survivors and higher in nonsurvivors. The most substantial between-group difference was the temporal change in OI, improving 15% in survivors and worsening 11% in nonsurvivors (P < .05). The adjusted mortality OR was significant for age (1.819, P = .001), OI at days 4 to 5 (2.26, P = .002), and OI percent change (1.90, P = .02). The number of ventilator-free days correlated significantly with late VR (-0.166, P < .05), early and late OI (-0.216, P < .01; -0.278, P < .01, respectively) and early and late P/F (0.158, P < .05; 0.283, P < .01, respectively). Conclusion: Nonsurvivors of COVID-19 needed increasing intensity of MV to sustain gas exchange over the first 5 days, unlike survivors. Temporal change OI, reflecting both PaO2 and the intensity of MV, is a potential marker of outcome in respiratory failure due to COVID-19.

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

Objective: This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission versus intensive care unit (ICU) admission and to predict mortality in COVID-19 pandemic. Materials and methods: This retrospective study consisted of 1874 persons-under-investigation for COVID-19 between February 7, 2020, and May 27, 2020 at Stony Brook University Hospital, New York. Two primary outcomes were ICU admission and mortality compared to COVID-19 positive patients in general hospital admission. Demographic, vitals, symptoms, imaging findings, comorbidities, and laboratory tests at presentation were collected. Predictions of mortality and ICU admission were made using machine learning with 80% training and 20% testing. Performance was evaluated using receiver operating characteristic (ROC) area under the curve (AUC). Results: A total of 635 patients were included in the analysis (age 60±11, 40.2% female). The top 6 mortality predictors were age, procalcitonin, C-creative protein, lactate dehydrogenase, D-dimer and lymphocytes. The top 6 ICU admission predictors are procalcitonin, lactate dehydrogenase, C-creative protein, pulse oxygen saturation, temperature and ferritin. The best machine learning algorithms predicted mortality with 89% AUC and ICU admission with 79% AUC. Conclusion: This study identifies key independent clinical parameters that predict ICU admission and mortality associated with COVID-19 infection. The predictive model is practical, readily enhanced and retrained using additional data. This approach has immediate translation and may prove useful for frontline physicians in clinical decision making under time-sensitive and resource-constrained environment.

Continued In-Hospital Angiotensin-Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Use in Hypertensive COVID-19 Patients Is Associated With Positive Clinical Outcome.

BACKGROUND: This study investigated continued and discontinued use of angiotensin-converting enzyme inhibitors (ACEi) or angiotensin II receptor blockers (ARB) during hospitalization of 614 hypertensive laboratory-confirmed COVID-19 patients. METHODS: Demographics, comorbidities, vital signs, laboratory data, and ACEi/ARB usage were analyzed. To account for confounders, patients were substratified by whether they developed hypotension and acute kidney injury (AKI) during the index hospitalization. RESULTS: Mortality (22% vs 17%, P > .05) and intensive care unit (ICU) admission (26% vs 12%, P > .05) rates were not significantly different between non-ACEi/ARB and ACEi/ARB groups. However, patients who continued ACEi/ARBs in the hospital had a markedly lower ICU admission rate (12% vs 26%; P = .001; odds ratio [OR] = 0.347; 95% confidence interval [CI], .187-.643) and mortality rate (6% vs 28%; P = .001; OR = 0.215; 95% CI, .101-.455) compared to patients who discontinued ACEi/ARB. The odds ratio for mortality remained significantly lower after accounting for development of hypotension or AKI. CONCLUSIONS: These findings suggest that continued ACEi/ARB use in hypertensive COVID-19 patients yields better clinical outcomes.

Visible-Light-Driven [2 + 2] Photocycloadditions between Benzophenone and C═C Bonds in Unsaturated Lipids.

The [2 + 2] photocycloaddition of alkenes and carbonyls is of fundamental interest and practical importance, as this process is extensively involved in oxetane-ring constructions. Although individual carbonyl group or alkene moiety has been utilized as photoactive species for oxetane formations upon ultraviolet photoexcitation, direct excitation of the entire noncovalent complex involving alkene and carbonyl substrates to achieve [2 + 2] photocycloadditions is rarely addressed. Herein, complexes with noncovalent interactions between benzophenone and C═C bonds in unsaturated lipids have been successfully characterized, and for the first time a [2 + 2] cycloaddition leading to the formation of oxetanes has been identified under visible-light irradiation. The mechanism of this reaction is distinctly different from the well-studied Paternò-Büchi reaction. The entire complexes characterized as dimeric proton-bonded alkene and carbonyl substrates can be excited under visible light, leading to electron transfer from the alkene moiety in fatty acyls to the carbonyl group within the complex. These results provide new insight into utilizing noncovalent complexes for the synthesis of oxetanes in which the excitation wavelength becomes independent of each individual substrate.

Combination Stiffness Gradient with Chemical Stimulation Directs Glioma Cell Migration on a Microfluidic Chip.

Tumor cells respond actively to the extracellular microenvironment via biomechanical and biochemical stimulation. Microchips provide a flexible platform to integrate multiple factors for cell research. In this work, we developed a subtle microfluidic chip that can generate a controllable stiffness gradient and orthogonal chemical stimulation to study the behaviors of glioma cells. Fibronectin-conjugated polyacrylamide (PAA) hydrogel with a longitudinal stiffness gradient ranging from about 1 kPa to 40 kPa is integrated within the cell culture chamber while lateral diffusion-based chemical stimulation is generated through circumambient microchannel arrays. The synergistic effect of epidermal growth factor (EGF) stimulation and hydrogel stiffness gradient on U87-MG cell migration was studied. By tracing cell migration, we discovered that hydrogel stiffness can promote cell chemotaxis, while the EGF gradient can accelerate cell migration. In addition, cell morphology showed typical cell spreading, increased aspect ratios, and decreased circularity in response to a stiffer substrate and plateaued at a certain stiffness level. Meanwhile, the content of intracellular reactive oxygen species (ROS) on the hydrogel soft end is enhanced by approximately 2 fold compared with that on the hydrogel stiff end. The enhancement of substrate stiffness on cell chemotaxis is very significant for in vitro model simulation and tissue engineering.

Inhibition Effect of Negative Air Ions on Adsorption between Volatile Organic Compounds and Environmental Particulate Matter.

This work focused on the chemisorption of volatile organic compounds (VOCs) on particulate matter of less than 2.5 µm (PM2.5). The detection results illustrated that VOCs on PM2.5 containing hydroxyl, carbonyl, and ester groups and CxHy on PM2.5 were sequentially decreased as 70.02, 21.35, 6.42, and 2.21%, respectively. The chemisorption mechanism showed that the stronger the electronegativity of oxygen-containing functional groups of VOCs, the easier it is to adsorb them on the silicate PM2.5 due to hydrogen bond formation. Strong electronegative oxygen-containing functional groups readily interacted through hydrogen bonds with silanol groups, which was the main component of PM2.5, resulting in VOC adsorption on PM2.5. Negative air ions (NAIs) can weaken the offset ability of the lone pair of electrons in oxygen-containing functional groups in VOCs, which could significantly weaken the possibility of forming hydrogen bonds with silanol groups. Therefore, NAIs can effectively inhibit the adsorption between VOCs and PM2.5, leading to a significant reduction in VOCs on the surface of PM2.5.