Reinforcement learning-based adaptive beam alignment in a photodiode-integrated array antenna module.

We successfully demonstrated an intelligent adaptive beam alignment scheme using a reinforcement learning (RL) algorithm integrated with an 8 × 8 photonic array antenna operating in the 40 GHz millimeter wave (MMW) band. In our proposed scheme, the three key elements of RL: state, action, and reward, are represented as the phase values in the photonic array antenna, phase changes with specified steps, and an obtained error vector magnitude (EVM) value, respectively. Furthermore, thanks to the Q-table, the RL agent can effectively choose the most suitable action based on its prior experiences. As a result, the proposed scheme autonomously achieves the best EVM performance by determining the optimal phase. In this Letter, we verify the capability of the proposed scheme in single- and multiple-user scenarios and experimentally demonstrate the performance of beam alignment to the user's location optimized by the RL algorithm. The achieved results always meet the signal quality requirement specified by the 3rd Generation Partnership Project (3GPP) criterion for 64-QAM orthogonal frequency division multiplexing (OFDM).

Synthesis and Biological Activities of Scleropentaside D.

We report the first total synthesis of scleropentaside D, a unique C-glycosidic ellagitannin, from the ketal derivative of scleropentaside A employing site-selective O4-protection of C-acyl glycoside and copper-catalyzed oxidative coupling reaction of galloyl groups as the key steps. Our study confirms the proposed structure of this natural product, scleropentaside D, and demonstrates its effectiveness as an inhibitor of α-glycosidase.

Recent Advances and Challenges in Protein Structure Prediction.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Thromboprophylaxis for outpatients with COVID-19: a Systematic Review and Meta-analysis.

Patients with COVID-19 develop an increased risk of thromboembolism. Thromboprophylaxis is recommended for hospitalized COVID-19 patients, but the role of thromboprophylaxis in outpatients with COVID-19 is less well defined. We conducted a systematic review and meta-analysis to evaluate the safety and efficacy of thromboprophylaxis among outpatients with COVID-19. We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus from inception to August 2023. The outcomes of interest were venous thromboembolic events including deep venous thrombosis and pulmonary embolism, all-cause mortality, cardiovascular events, hospitalization, major bleeding events, and non-major bleeding events. We included 6 trials comprising 3352 patients. Patients who received thromboprophylaxis had an approximately 70% reduction in venous thromboembolism (RR, 0.28 [95% CI, 0.08 to 0.93]) compared to patients who did not receive thromboprophylaxis. The risk of mortality (RR, 0.79 [95% CI, 0.35 to 1.77]), cardiovascular events (RR, 0.91 [95% CI, 0.30 to 2.73]), and hospitalization (RR, 1.09 [95% CI, 0.81 to 1.47]) were similar between the two groups. Patients who received thromboprophylaxis had a higher risk of non-major bleeding (RR, 3.48 [95% CI, 1.72 to 7.05) compared to patients who did not receive thromboprophylaxis. Thromboprophylaxis reduced the risk of venous thromboembolism but not mortality, cardiovascular events, or hospitalization among outpatients with COVID-19.

Lewis Acidic Support Boosts C-C Coupling in the Pulsed Electrochemical CO2 Reaction.

Copper-oxide electrocatalysts have been demonstrated to effectively perform the electrochemical CO2 reduction reaction (CO2RR) toward C2+ products, yet preserving the reactive high-valent CuOx has remained elusive. Herein, we demonstrate a model system of Lewis acidic supported Cu electrocatalyst with a pulsed electroreduction method to achieve enhanced performance for C2+ products, in which an optimized electrocatalyst could reach ∼76% Faradaic efficiency for C2+ products (FEC2+) at ∼-0.99 V versus reversible hydrogen electrode, and the corresponding mass activity can be enhanced by ∼2 times as compared to that of conventional CuOx. In situ time-resolved X-ray absorption spectroscopy investigating the dynamic chemical/physical nature of Cu during CO2RR discloses that an activation process induced by the KOH electrolyte during pulsed electroreduction greatly enriched the Cuδ+O/Znδ+O interfaces, which further reveals that the presence of Znδ+O species under the cathodic potential could effectively serve as a Lewis acidic support for preserving the Cuδ+O species to facilitate the formation of C2+ products, and the catalyst structure-property relationship of Cuδ+O/Znδ+O interfaces can be evidently realized. More importantly, we find a universality of stabilizing Cuδ+O species for various metal oxide supports and to provide a general concept of appropriate electrocatalyst-Lewis acidic support interaction for promoting C2+ products.

Effect of metformin on outcomes of patients treated with immune checkpoint inhibitors: a retrospective cohort study.

BACKGROUND: Immune checkpoint inhibitors have transformed the treatment landscape of cancer treatment, but only a fraction of patients responds to treatment, leading to an increasing effort to repurpose clinically approved medications to augment ICI therapy. Metformin has been associated with improved survival outcomes in patients undergoing conventional chemotherapy. However, whether metformin provides survival benefits in patients receiving immune checkpoint inhibitors (ICIs) is unknown. METHODS: We performed a retrospective cohort study at two tertiary referral centers in Taiwan. All adult diabetes mellitus patients who were treated with ICIs between January 2015 and December 2021 were included. The primary and secondary outcomes were overall survival (OS) and progression-free survival (PFS), respectively. RESULTS: In total, 878 patients were enrolled in our study, of which 86 patients used metformin and 78 patients used non-metformin diabetes medications. Compared with non-users, metformin users had a longer median OS (15.4 [IQR 5.6-not reached] vs. 6.1 [IQR, 0.8-21.0] months, P = 0.003) and PFS (5.1 [IQR 2.0-14.3] vs. 1.9 [IQR 0.7-8.6] months, P = 0.041). In a univariate Cox proportional hazard analysis, the use of metformin was associated with a reduction in the risk of mortality (HR: 0.53 [95% confidence interval: 0.35-0.81], P = 0.004) and disease progression (HR: 0.69 [95% CI 0.49-0.99], P = 0.042). The use of metformin remained associated with a lower risk of mortality after adjusting for baseline variables such as age, cancer stage, and underlying comorbidities (OS, HR: 0.55 [95% CI 0.34-0.87], P = 0.011). Similarly, the use of metformin was associated with a lower risk of disease progression. Importantly, the use of metformin before ICI initiation was not associated with a reduction in mortality (HR: 0.61 [95% CI 0.27-1.42], P = 0.25) or disease progression (HR: 0.69 [95% CI 0.33-1.43], P = 0.32). CONCLUSION: The use of metformin is associated with survival benefits in patients undergoing immunotherapy. Prospective clinical trials are warranted to define the role of metformin in augmenting immunotherapy.

Structural analogue-based protein structure domain assembly assisted by deep learning.

MOTIVATION: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning. RESULTS: In this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the energy function with an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled 293 human multi-domain proteins, where the average TM-score of the full-chain model after the assembly by SADA is 1.1% higher than that of the model by AlphaFold2. To conclude, we find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling. AVAILABILITY AND IMPLEMENTATION: http://zhanglab-bioinf.com/SADA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Involvement of free-space optics in Raman distributed temperature sensing.

This Letter demonstrates the successful use of free-space optics (FSO) as a transition channel for an air segment in transmitting Raman backscattering signals for distributed temperature sensing (DTS). A barrier-free air segment link shaped by an FSO is part of the Raman-based DTS (RDTS) fiber optic transmission route. For this plan, the FSO enables delivery of the RDTS's pulse with the low-loss transmission over the air segment while also returning to the RDTS the varied Raman backscattered signals from the probing temperature variations for signal interpretation. The difference between various temperatures sensed and the referential air temperature remains nearly the same before and after passing the FSO. The viability of this technology provides a crucial basis for tackling the high expense of installing and repairing DTS cables and the challenges associated with doing so owing to topographical restrictions.

Efficacy of Lipophilic Statins on Outcomes of Patients Treated with Immune Checkpoint Inhibitors.

BACKGROUND: Statins are associated with improved survival outcomes in patients receiving immune checkpoint inhibitors (ICIs), but the impact of lipophilic and hydrophilic statin properties on patient outcomes is unknown. OBJECTIVES: We aim to investigate if statins with lipophilic properties are associated with clinical outcomes in patients receiving ICIs. METHOD: We conducted a retrospective cohort study at two tertiary referral centers in Taiwan comprising patients receiving ICIs between January 2015 and December 2021. We compared the comparative effect of lipophilic and hydrophilic statins on patient outcomes. The primary outcome was overall survival (OS) and the secondary outcome was progression-free survival (PFS). RESULTS: Among 734 patients receiving ICIs, there were 51 lipophilic statin users, 25 hydrophilic statin users, and 658 nonusers. Lipophilic statin users had a longer median OS (38.0 [IQR, 16.7-not reached] vs. 15.2 [IQR, 8.2-not reached] months vs. 18.9 [IQR, 5.4 51.6] months) and PFS (13.0 [IQR, 4.7-41.5] vs. 8.2 [IQR, 2.2-14.7] months vs. 5.6 [2.3-18.7] months) than hydrophilic statin users and non-statin users. In Cox proportional hazard analyses, the use of lipophilic statins was associated with a 40-50% lower risk of mortality and disease progression compared with hydrophilic statin or non-statin users. CONCLUSIONS: The use of lipophilic statins seems to be associated with survival benefits in patients undergoing immunotherapy.

Base-mediated chalcogenoaminative annulation of 2-alkynylanilines for direct access to 3-sulfenyl/selenyl-1H-indoles.

An efficient and transition metal-free synthesis of 3-sulfenyl/selenyl-1H-indoles via a base-assisted chalcogenoaminative annulation of 2-alkynyl aniline with disulfides/diselenides is described. A series of 2-alkynylanilines were found compatible with dichalcogenides in this transformation providing 3-sulfenyl/selenyl-1H-indoles in good to excellent yields. The presented methodology has the advantages of easily available raw materials, functional group tolerance, and a wide range of substrates that provide access to 3-sulfenylindoles and 3-selenylindoles.

The effect of patient volume on mortality and morbidity of extremely low birth weight infants in Taiwan.

BACKGROUND: To assess whether the number of extremely low birth weight (ELBW) infants treated annually in neonatal intensive care units (NICUs) in Taiwan affects the mortality and morbidity of this patient population. METHODS: This retrospective cohort study included preterm infants with ELBW (≤1000 g). NICUs were divided into three subgroups according to the annual admissions of ELBW infants (low, ≤10; medium, 11-25; and high, >25). Perinatal characteristics, mortality, and short-term morbidities were compared between groups. RESULTS: A total of 1945 ELBW infants from 17 NICUs were analyzed (low-volume, n = 263; medium-volume, n = 420; and high-volume, n = 1262). After risk adjustments, infants from NICUs with low patient volumes were at a higher risk of death. The risk-adjusted odds ratios (aOR) for mortality were 0.61 (95% CI, 0.43-0.86) in the high-volume NICUs and 0.65 (95% CI, 0.43-0.98) in medium-volume NICUs, compared with infants admitted to low-volume NICUs. Infants in medium-volume NICUs had the lowest incidence of prenatal steroid exposure (58.1%, P < 0.001) and were associated with the highest risk of necrotizing enterocolitis (aOR, 2.35 [95% CI, 1.48-3.72]), severe intraventricular hemorrhage (aOR, 1.55 [95% CI, 1.01-2.28]), and bronchopulmonary dysplasia (aOR, 1.61 [95% CI, 1.10-2.35]). However, survival without major morbidity did not differ between the groups. CONCLUSION: The mortality risk was higher among ELBW infants admitted to NICUs with a low annual patient volume. This may emphasize the importance of systematically referring patients from these vulnerable populations to appropriate care settings.

High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network.

In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance.

Integrated Sensor-Optics Communication System Using Bidirectional Fiber and FSO Channels and Hybrid Deep Learning Techniques.

This paper introduces a new bidirectional integration approach that combines fiber sensor/free space optics (FSO) communication using an intensity and wavelength division multiplexer (IWDM) techniques-based long-distance fiber Bragg grating (FBG) sensor strain-sensing system. By implementing coarse wavelength division multiplexing (CWDM), the system achieves the simultaneous transmission of optical communication and fiber optical sensor (FOS) sensing signals, resulting in a highly capable, flexible, and cost-effective solution. The proposed FSO transmission technique addresses complex fiber cable installation concerns with topographical limitations. This bidirectional structure ensures the reliability and stability of the long-distance FBG sensor system, supported by extensive research and experimentation. A hybrid stacked gated recurrent units and long short-term memory (SGRU-LSTM) model is proposed to enhance strain measurement accuracy by predicting and measuring the central wavelength of overlapped strain-sensing FBG sensor signals. The results demonstrate the superiority of the proposed model in peak wavelength detection accuracy. The primary benefit of integrating communication and sensing is the significant reduction in construction costs by eliminating the requirement for two individual fiber optic systems, as the integration allows for a single system to fulfill both functions, resulting in more efficient and cost-effective implementation. Overall, this paper contributes to advancing long-distance FBG sensor systems by integrating fiber sensor/FSO communication and deep learning techniques, improving transmission distance, multiplexing capacity, measurement accuracy, system survivability, and cost-effectiveness.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

MOTIVATION: Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy. RESULTS: A sequential niche multimodal conformational sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm overcome high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high-energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins, 24 CASP13 and 19 CASP14 FM targets. Results show that SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta restrained by distance (Rosetta-dist), SNfold achieves higher average TM-score and improves the sampling efficiency by more than 100 times. On several CASP FM targets, SNfold also shows good performance compared with four state-of-the-art servers in CASP. As a plug-in conformational sampling algorithm, SNfold can be extended to other protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION: The source code and executable versions are freely available at https://github.com/iobio-zjut/SNfold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

29 GHz single-mode vertical-cavity surface-emitting lasers passivated by atomic layer deposition.

The fabrication processes of high-speed oxide-confined single-mode (SM)-vertical-cavity surface-emitting lasers (VCSELs) are complex, costly, and often held back by reliability and yield issues, which substantially set back the high-volume processing and mass commercialization of SM-VCSELs in datacom or other applications. In this article, we report the effects of Al2O3 passivation films deposited by atomic layer deposition (ALD) on the mesa sidewalls of high-speed 850-nm SM-VCSELs. The ALD-deposited film alleviates the trapping of carriers by sidewall defects and is an effective way to improve the performance of SM-VCSELs. The ALD-passivated SM-VCSELs showed statistically significant static performance improvements and reached a believed to be record-breaking SM-modulation bandwidth of 29.1 GHz. We also propose an improved microwave small-signal equivalent circuit model for SM-VCSELs that accounts for the losses attributed to the mesa sidewalls. These findings demonstrate that ALD passivation can mitigate processing-induced surface damage, enhance the performance of SM-VCSELs, and enable mass production of high-quality SM-VCSELs for mid- to long-reach optical interconnects.

Reinforcement learning for W-band radio-over-fiber system using a polarization modulator.

Reinforcement learning (RL) is applied to improve the performance of the polarization modulator (PolM)-based W-band radio-over-fiber (RoF) system in this Letter. By controlling the polarization angle of the dual-wavelength laser source in the PolM-based scheme, the RF response can be easily modified and therefore it hugely increases the available bandwidth in the RoF system. In the proposed RL scheme, the state is described as the value of the angle from the polarization controller (PC). We use changing the angle of the polarizer (P) as the actions of the RL agent to optimize the frequency response. The agent also receives a reward from the system and learns from the environment and previous experience. Moreover, the reward is the value of error vector magnitude at each state. Therefore, the proposed scheme of RL is implemented and demonstrated in a multi-channel RoF system, and the results show that an RL agent provides an effective intelligent technique to obtain the best quality of data transmission.

The incidence and risk of cardiovascular events associated with immune checkpoint inhibitors in Asian populations.

OBJECTIVES: Immune checkpoint inhibitors are associated with adverse cardiovascular events. However, there are no data characterizing cardiovascular events among Asians on immune checkpoint inhibitors. We aim to determine the incidence and risk of cardiac events associated with immune checkpoint inhibitors in an Asian population. METHODS: We performed a retrospective, propensity score-matched cohort study at two tertiary referral centers in Taiwan. Immune checkpoint inhibitor users were matched with non-immune checkpoint inhibitor users based on predetermined clinical variables. The primary outcome was major adverse cardiovascular events, defined as a composite of myocardial infarction, ischemic stroke, acute peripheral occlusive disease, pulmonary embolism, deep venous thrombosis, heart failure, pericardial disease, myocarditis, cardiac arrhythmias and conduction block. RESULTS: Between January 2010 and November 2021, 868 immune checkpoint inhibitor users were matched 1:1 with non-immune checkpoint inhibitor users. Among immune checkpoint inhibitor users, 67 (7.7%) patients developed major adverse cardiovascular events. During a median follow-up period of 188 days, the incidence rate of major adverse cardiovascular events for immune checkpoint inhibitor and non-immune checkpoint inhibitor users was 94.8 and 46.2 per 1000 patient-years, respectively, resulting in an incidence rate ratio of 2.1 [95% confidence interval: 1.5-2.9]. In multivariate Cox proportional hazard models, immune checkpoint inhibitor users had a 60% increased risk for major adverse cardiovascular events [hazard ratio, 1.6 (95% confidence interval: 1.1-2.3)]. Immune checkpoint inhibitors use was independently associated with increased risk of ischemic stroke [hazard ratio, 3.0 (95% confidence interval: 1.0-9.0)] and pulmonary embolism [hazard ratio, 5.5 (95% confidence interval: 1.4-21.3)]. In multivariate logistic regression analysis, age > 65, metastatic disease, hypertension and baseline platelet-to-lymphocyte ratio < 180 were risk factors for major adverse cardiovascular events. CONCLUSIONS: Among Asians, immune checkpoint inhibitors were associated with an increased risk of major adverse cardiovascular events, particularly ischemic stroke and pulmonary embolism.

Adsorption-desorption and degradation of colistin in soils under aerobic conditions.

Colistin has broad-spectrum activity against Gram-negative bacteria and has been considered as the last-resort treatment for multiantibiotic-resistant Gram-negative bacteria infections in human. And it is also world widely utilized as a veterinary medicine for the promotion of growth, prevention and control of diseases in livestock and poultry. Extensive use of colistin in husbandry results in the introduction of large amounts of colistin to the surrounding environment via animals' urine and feces, potentially inducing the prevalence of colistin resistance bacteria and the impact of the ecological environment. The study investigated the adsorption, desorption and degradation of colistin in soils using high sensitivity UPLC-MS/MS assays. An MS based assay was established to directly determine colistin in the soil. It was observed that the moderate adsorption affinity of colistin to the three soils with adsorption strength (1/n) ranging from 0.6897 to 1.3333. Colistin exhibited the highest adsorption affinity to the sandy loam, followed by the sand and loam. Despite of different characteristics of three soils, the adsorption capacity of the three soils was comparable. The adsorption of colistin to the three types of soils analyzed was irreversible. The degradation experiments showed that the degradation of colistin in the sandy loam was relatively slow with a degradation half-life in a range of 13.2-29.7 days when colistin was applied to the sandy loam at a level of 10 ~ 40 µg/g. The degradation of colistin occurred in the mixture of the sandy loam and feces recovered from the colistin treated broiler as well. 25% of colistin remained in the mixture under environmental conditions after 14 days. Composting the sandy loam by directly covering the soil surface with colistin treated broilers' feces resulted in the introduction of colistin to the sandy loam. Colistin was observed in both the topsoil from the contact surface and sandy loam samples collected 20 cm below the contact surface. The understanding of adsorption-desorption behaviors, degradation and mobility of colistin in soils might offer insights into the potential impact of colistin on the emergence and prevalence of resistant bacteria and the ecological environment.

The role of fibrosis index FIB-4 in predicting liver fibrosis stage and clinical prognosis: A diagnostic or screening tool?

This review evaluates the ability of the fibrosis index based on four factors (FIB-4) identifying fibrosis stages, long-time prognosis in chronic liver disease, and short-time outcomes in acute liver injury. FIB-4 was accurate in predicting the absence or presence of advanced fibrosis with cut-offs of 1.0 and 2.65 for viral hepatitis B, 1.45 and 3.25 for viral hepatitis C, 1.30 (<65 years), 2.0 (≥65 years), and 2.67 for non-alcoholic fatty liver disease (NAFLD), respectively, but had a low-to-moderate accuracy in alcoholic liver disease (ALD) and autoimmune hepatitis. It performed better in excluding fibrosis, so we built an algorithm for identifying advanced fibrosis by combined methods and giving work-up and follow-up suggestions. High FIB-4 in viral hepatitis, NAFLD, and ALD was associated with significantly high hepatocellular carcinoma incidence and mortality. Additionally, FIB-4 showed the ability to predict high-risk varices with cut-offs of 2.87 and 3.91 in cirrhosis patients and predict long-term survival in hepatocellular carcinoma patients after hepatectomy. In acute liver injury caused by COVID-19, FIB-4 had a predictive value for mechanical ventilation and 30-day mortality. Finally, FIB-4 may act as a screening tool in the secondary prevention of NAFLD in the high-risk population.

Site-Specified Two-Dimensional Heterojunction of Pt Nanoparticles/Metal-Organic Frameworks for Enhanced Hydrogen Evolution.

Heterojunction nanostructures usually exhibit enhanced properties in compariosn with their building blocks and are promising catalyst candidates due to their combined surface and unique interface. Here, for the first time we realized the oriented growth of ultrasmall metal nanoparticles (NPs) on metal-organic framework nanosheets (MOF NSs) by precisely regulating the reduction kinetics of metal ions with solvents. In particular, a rapid reduction of metal ions leads to the random distribution of metal NPs on the surface of MOF NSs, while a slow reduction of metal ions results in the oriented growth of NPs on the edge of MOF NSs. Impressively, the strong synergy between Pt NPs and MOF NSs significantly enhances the hydrogen evolution reaction (HER) performance, and the optimal catalyst displays HER activities superior to those of a composite with a random growth of Pt NPs and commercial Pt/C under both acidic and alkaline conditions. Moreover, the versatility of such oriented growth has been extended to other metal NPs, such as Pd, Ag, and Au. We believe this work will promote research interest in material design for many potential applications.