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Inflammatory mechanisms play important roles in intracerebral hemorrhage (ICH) and have been linked to the development of stroke-associated pneumonia (SAP). The neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) are inflammatory indexes that influence systemic inflammatory responses after stroke. In this study, we aimed to compare the predictive value of the NLR, SII, SIRI and PLR for SAP in patients with ICH to determine their application potential in the early identification of the severity of pneumonia. Patients with ICH in four hospitals were prospectively enrolled. SAP was defined according to the modified Centers for Disease Control and Prevention criteria. Data on the NLR, SII, SIRI and PLR were collected at admission, and the correlation between these factors and the clinical pulmonary infection score (CPIS) was assessed through Spearman's analysis. A total of 320 patients were enrolled in this study, among whom 126 (39.4%) developed SAP. The results of the receiver operating characteristic (ROC) analysis revealed that the NLR had the best predictive value for SAP (AUC: 0.748, 95% CI: 0.695-0.801), and this outcome remained significant after adjusting for other confounders in multivariable analysis (RR=1.090, 95% CI: 1.029-1.155). Among the four indexes, Spearman's analysis showed that the NLR was the most highly correlated with the CPIS (r=0.537, 95% CI: 0.395-0.654). The NLR could effectively predict ICU admission (AUC: 0.732, 95% CI: 0.671-0.786), and this finding remained significant in the multivariable analysis (RR=1.049, 95% CI: 1.009-1.089, P=0.036). Nomograms were created to predict the probability of SAP occurrence and ICU admission. Furthermore, the NLR could predict a good outcome at discharge (AUC: 0.761, 95% CI: 0.707-0.8147). Among the four indexes, the NLR was the best predictor for SAP occurrence and a poor outcome at discharge in ICH patients. It can therefore be used for the early identification of severe SAP and to predict ICU admission.

A patient's infectivity is determined by the presence of the virus in different body fluids, secretions, and excreta. The persistence and clearance of viral RNA from different specimens of patients with 2019 novel coronavirus disease (COVID-19) remain unclear. This study analyzed the clearance time and factors influencing 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19, providing further evidence to improve the management of patients during convalescence. The clinical data and laboratory test results of convalescent patients with COVID-19 who were admitted to from January 20, 2020 to February 10, 2020 were collected retrospectively. The reverse transcription polymerase chain reaction (RT-PCR) results for patients' oropharyngeal swab, stool, urine, and serum samples were collected and analyzed. Convalescent patients refer to recovered non-febrile patients without respiratory symptoms who had two successive (minimum 24 h sampling interval) negative RT-PCR results for viral RNA from oropharyngeal swabs. The effects of cluster of differentiation 4 (CD4)+ T lymphocytes, inflammatory indicators, and glucocorticoid treatment on viral nucleic acid clearance were analyzed. In the 292 confirmed cases, 66 patients recovered after treatment and were included in our study. In total, 28 (42.4%) women and 38 men (57.6%) with a median age of 44.0 (34.0-62.0) years were analyzed. After in-hospital treatment, patients' inflammatory indicators decreased with improved clinical condition. The median time from the onset of symptoms to first negative RT-PCR results for oropharyngeal swabs in convalescent patients was 9.5 (6.0-11.0) days. By February 10, 2020, 11 convalescent patients (16.7%) still tested positive for viral RNA from stool specimens and the other 55 patients' stool specimens were negative for 2019-nCoV following a median duration of 11.0 (9.0-16.0) days after symptom onset. Among these 55 patients, 43 had a longer duration until stool specimens were negative for viral RNA than for throat swabs, with a median delay of 2.0 (1.0-4.0) days. Results for only four (6.9%) urine samples were positive for viral nucleic acid out of 58 cases; viral RNA was still present in three patients' urine specimens after throat swabs were negative. Using a multiple linear regression model (F = 2.669, P = 0.044, and adjusted R = 0.122), the analysis showed that the CD4+ T lymphocyte count may help predict the duration of viral RNA detection in patients' stools (t = -2.699, P = 0.010). The duration of viral RNA detection from oropharyngeal swabs and fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (15 days vs. 8.0 days, respectively; t = 2.550, P = 0.013) and the duration of viral RNA detection in fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (20 days vs. 11 days, respectively; t = 4.631, P < 0.001). There was no statistically significant difference in inflammatory indicators between patients with positive fecal viral RNA test results and those with negative results (P > 0.05). In brief, as the clearance of viral RNA in patients' stools was delayed compared to that in oropharyngeal swabs, it is important to identify viral RNA in feces during convalescence. Because of the delayed clearance of viral RNA in the glucocorticoid treatment group, glucocorticoids are not recommended in the treatment of COVID-19, especially for mild disease. The duration of RNA detection may relate to host cell immunity.

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

Halide perovskites are a compelling candidate for the next generation of clean-energy-harvesting technologies owing to their low cost, facile fabrication and outstanding semiconductor properties. However, photovoltaic device efficiencies are still below practical limits and long-term stability challenges hinder their practical application. Current evidence suggests that strain in halide perovskites is a key factor in dictating device efficiency and stability. Here we outline the fundamentals of strain within halide perovskites relevant to photovoltaic applications and rationalize approaches to characterize the phenomenon. We examine recent breakthroughs in eliminating the adverse impacts of strain, enhancing both device efficiencies and operational stabilities. Finally, we discuss further challenges and outline future research directions for placing stress and strain studies at the forefront of halide perovskite research. An extensive understanding of strain in halide perovskites is needed, which would allow effective strain management and drive further enhancements in efficiencies and stabilities of perovskite photovoltaics. This Review provides an outlook on current understanding of the role of strain on the performance and stability of perovskite solar cells, as well as on tools to characterize strain in halide perovskite films and on strain management strategies.

Background METTL3 is known to be involved in all stages in the life cycle of RNA. It affects the tumor formation by the regulation the m6A modification in the mRNAs of critical oncogenes or tumor suppressors. In bladder cancer, METTL3 could promote the bladder cancer progression via AFF4/NF-κB/MYC signaling network by an m6A dependent manner. Recently, METTL3 was also found to affect the m6A modification in non-coding RNAs including miRNAs, lincRNAs and circRNAs. However, whether this mechanism is related to the proliferation of tumors induced by METTL3 is not reported yet. Methods Quantitative real-time PCR, western blot and immunohistochemistry were used to detect the expression of METTL3 in bladder cancer. The survival analysis was adopted to explore the association between METTL3 expression and the prognosis of bladder cancer. Bladder cancer cells were stably transfected with lentivirus and cell proliferation and cell cycle, as well as tumorigenesis in nude mice were performed to assess the effect of METTL3 in bladder cancer. RNA immunoprecipitation (RIP), co-immunoprecipitations and RNA m6A dot blot assays were conducted to confirm that METTL3 interacted with the microprocessor protein DGCR8 and modulated the pri-miR221/222 process in an m6A-dependent manner. Luciferase reporter assay was employed to identify the direct binding sites of miR221/222 with PTEN. Colony formation assay and CCK8 assays were conducted to confirm the function of miR-221/222 in METTL3-induced cell growth in bladder cancer. Results We confirmed the oncogenic role of METTL3 in bladder cancer by accelerating the maturation of pri-miR221/222, resulting in the reduction of PTEN, which ultimately leads to the proliferation of bladder cancer. Moreover, we found that METTL3 was significantly increased in bladder cancer and correlated with poor prognosis of bladder cancer patients. Conclusions Our findings suggested that METTL3 may have an oncogenic role in bladder cancer through interacting with the microprocessor protein DGCR8 and positively modulating the pri-miR221/222 process in an m6A-dependent manner. To our knowledge, this is the first comprehensive study that METTL3 affected the tumor formation by the regulation the m6A modification in non-coding RNAs, which might provide fresh insights into bladder cancer therapy.

Perovskite light-emitting diodes (PeLEDs) have shown excellent performance in the green and near-infrared spectral regions, with high color purity, efficiency, and brightness. In order to shift the emission wavelength to the blue, compositional engineering (anion mixing) and quantum-confinement engineering (reduced-dimensionality) have been employed. Unfortunately, LED emission profiles shift with increasing driving voltages due to either phase separation or the coexistence of multiple crystal domains. Here we report color-stable sky-blue PeLEDs achieved by enhancing the phase monodispersity of quasi-2D perovskite thin films. We selected cation combinations that modulate the crystallization and layer thickness distribution of the domains. The perovskite films show a record photoluminescence quantum yield of 88% at 477 nm. The corresponding PeLEDs exhibit stable sky-blue emission under high operation voltages. A maximum luminance of 2480 cd m −2 at 490 nm is achieved, fully one order of magnitude higher than the previous record for quasi-2D blue PeLEDs. Perovskite light-emitting diodes show promising color tunability and device performance but suffer from emission color shift at higher driving voltages. Here Xing et al. report color stable blue light-emitting diodes by drastically increasing the phase purity of the quasi-2D perovskite thin films.

Planar perovskite solar cells (PSCs) made entirely via solution processing at low temperatures (<150°C) offer promise for simple manufacturing, compatibility with flexible substrates, and perovskite-based tandem devices. However, these PSCs require an electron-selective layer that performs well with similar processing. We report a contact-passivation strategy using chlorine-capped TiO₂ colloidal nanocrystal film that mitigates interfacial recombination and improves interface binding in low-temperature planar solar cells. We fabricated solar cells with certified efficiencies of 20.1 and 19.5% for active areas of 0.049 and 1.1 square centimeters, respectively, achieved via low-temperature solution processing. Solar cells with efficiency greater than 20% retained 90% (97% after dark recovery) of their initial performance after 500 hours of continuous room-temperature operation at their maximum power point under 1-sun illumination (where 1 sun is defined as the standard illumination at AM1.5, or 1 kilowatt/square meter).