Innovative Thermocatalytic H2 Sensor with Double-Sided Pd Nanocluster Films on an Ultrathin Mica Substrate.

Hydrogen (H2) is crucial in the future global energy landscape due to its eco-friendly properties, but its flammability requires precise monitoring. This study introduces an innovative thermocatalytic H2 sensor utilizing ultrathin mica sheets as substrates, coated on both sides with Pd nanocluster (NC) films. The ultrathin mica substrate ensures robustness and flexibility, enabling the sensor to withstand high temperatures and mechanical deformation. Additionally, it simplifies the fabrication process by eliminating the need for complex microelectro-mechanical systems (MEMS) technology. Constructed through cluster beam deposition, the sensor exhibits exceptional characteristics, including a wide concentration range (from 500 ppm to 4%), rapid response and recovery times (3.1 and 2.4 s for 1% H2), good selectivity, high stability, and repeatability. The operating temperature can be as low as 40 °C, achieving remarkably low power consumption. The study explores the impact of double-sided versus single-sided catalytic layers, revealing significantly higher sensitivity and response with the double-sided configuration due to the increased catalytic surface area. Additionally, the research investigates the relationship between the deposition amount of Pd NCs and the sensor's sensitivity, identifying an optimal value that maximizes performance without excessive use of Pd. The sensor's innovative design and excellent performance position it as a promising candidate for meeting the demands of a hydrogen-based energy economy.

Development of Pd/In2O3 hybrid nanoclusters to optimize ethanol vapor sensing.

In this study, we successfully synthesize palladium-decorated indium trioxide (Pd/In2O3) hybrid nanoclusters (NCs) using an advanced dual-target cluster beam deposition (CBD) method, a significant stride in developing high-performance ethanol sensors. The prepared Pd/In2O3 hybrid NCs exhibit exceptional sensitivity, stability, and selectivity to low concentrations of ethanol vapor, with a maximum response value of 101.2 at an optimal operating temperature of 260 °C for 6 at% Pd loading. The dynamic response of the Pd/In2O3-based sensor shows an increase in response with increasing ethanol vapor concentrations within the range of 50 to 1000 ppm. The limit of detection is as low as 24 ppb. The sensor exhibits a high sensitivity of 28.24 ppm-1/2, with response and recovery times of 2.7 and 4.4 seconds, respectively, for 100 ppm ethanol vapor. Additionally, the sensor demonstrates excellent repeatability and stability, with only a minor decrease in response observed over 30 days and notable selectivity for ethanol compared to other common volatile organic compounds. The study highlights the potential of Pd/In2O3 NCs as promising materials for ethanol gas sensors, leveraging the unique capabilities of CBD for controlled synthesis and the catalytic properties of Pd for enhanced gas-sensing performance.

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. METHODS: We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. RESULTS: Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322-0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250-0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060-0.8966) and 0.5355 (95%CI = 0.4988-0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340-0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345-0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. CONCLUSION: GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

Metabolomics identifies and validates serum androstenedione as novel biomarker for diagnosing primary angle closure glaucoma and predicting the visual field progression.

Background: Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness in Asia, and no reliable, effective diagnostic, and predictive biomarkers are used in clinical routines. A growing body of evidence shows metabolic alterations in patients with glaucoma. We aimed to develop and validate potential metabolite biomarkers to diagnose and predict the visual field progression of PACG. Methods: Here, we used a five-phase (discovery phase, validation phase 1, validation phase 2, supplementary phase, and cohort phase) multicenter (EENT hospital, Shanghai Xuhui Central Hospital), cross-sectional, prospective cohort study designed to perform widely targeted metabolomics and chemiluminescence immunoassay to determine candidate biomarkers. Five machine learning (random forest, support vector machine, lasso, K-nearest neighbor, and GaussianNaive Bayes [NB]) approaches were used to identify an optimal algorithm. The discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by Hosmer-Lemeshow tests and calibration plots. Results: Studied serum samples were collected from 616 participants, and 1464 metabolites were identified. Machine learning algorithm determines that androstenedione exhibited excellent discrimination and acceptable calibration in discriminating PACG across the discovery phase (discovery set 1, AUCs=1.0 [95% CI, 1.00-1.00]; discovery set 2, AUCs = 0.85 [95% CI, 0.80-0.90]) and validation phases (internal validation, AUCs = 0.86 [95% CI, 0.81-0.91]; external validation, AUCs = 0.87 [95% CI, 0.80-0.95]). Androstenedione also exhibited a higher AUC (0.92-0.98) to discriminate the severity of PACG. In the supplemental phase, serum androstenedione levels were consistent with those in aqueous humor (r=0.82, p=0.038) and significantly (p=0.021) decreased after treatment. Further, cohort phase demonstrates that higher baseline androstenedione levels (hazard ratio = 2.71 [95% CI: 1.199-6.104], p=0.017) were associated with faster visual field progression. Conclusions: Our study identifies serum androstenedione as a potential biomarker for diagnosing PACG and indicating visual field progression. Funding: This work was supported by Youth Medical Talents - Clinical Laboratory Practitioner Program (2022-65), the National Natural Science Foundation of China (82302582), Shanghai Municipal Health Commission Project (20224Y0317), and Higher Education Industry-Academic-Research Innovation Fund of China (2023JQ006).

A dataset of low-carbon energy transition index for Chinese cities 2003-2019.

Cities are at the heart of climate change mitigation as they account for over 70% of global carbon emissions. However, cities vary in their energy systems and socioeconomic capacities to transition to renewable energy. To address this heterogeneity, this study proposes an Energy Transition Index (ETI) specifically designed for cities, and applies it to track the progress of energy transition in Chinese cities. The city-level ETI framework is based on the national ETI developed by the World Economic Forum (WEF) and comprises two sub-indexes: the Energy System Performance sub-index, which evaluates the current status of cities' energy systems in terms of energy transition, and the Transition Readiness sub-index, which assesses their socioeconomic capacity for future energy transition. The initial version of the dataset includes ETI and its sub-indexes for 282 Chinese cities from 2003 to 2019, with annual updates planned. The spatiotemporal data provided by the dataset facilitates research into the energy transition roadmap for different cities, which can help China achieve its energy transition goals.

Messenger RNA-Based Therapeutics and Vaccines: What's beyond COVID-19?

With the rapid success in the development of mRNA vaccines against COVID-19 and with a number of mRNA-based drugs ahead in the pipelines, mRNA has catapulted to the forefront of drug research, demonstrating its substantial effectiveness against a broad range of diseases. As the recent global pandemic gradually fades, we cannot stop thinking about what the world has gained: the realization and validation of the power of mRNA in modern medicine. A significant amount of research has now been concentrated on developing mRNA drugs and vaccine platforms against infectious and immune diseases, cancer, and other debilitating diseases and has demonstrated encouraging results. Here, based on the CAS Content Collection, we provide a landscape view of the current state, outline trends in the research and development of mRNA therapeutics and vaccines, and highlight some notable patents focusing on mRNA therapeutics, vaccines, and delivery systems. Analysis of diseases disclosed in patents also reveals highly investigated diseases for treatments with these medicines. Finally, we provide information about mRNA therapeutics and vaccines in clinical trials. We hope this Review will be useful for understanding the current knowledge in the field of mRNA medicines and will assist in efforts to solve its remaining challenges and revolutionize the treatment of human diseases.

Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM.

Background/aims: Timely detection and treatment of retinal detachment (RD) could effectively save vision and reduce the risk of progressing visual field defects. High myopia (HM) is known to be associated with an increased risk of RD. Evidently, it should be clearly discriminated the individuals with high or low risk of RD in patients with HM. By using multi-parametric analysis, risk assessment, and other techniques, it is crucial to create cutting-edge screening programs that may be utilized to improve population eye health and develop person-specific, cost-effective preventative, and targeted therapeutic measures. Therefore, we propose a novel, routine blood parameters-based prediction model as a screening program to help distinguish who should offer detailed ophthalmic examinations for RD diagnosis, prevent visual field defect progression, and provide personalized, serial monitoring in the context of predictive, preventive, and personalized medicine (PPPM/3 PM). Methods: This population-based study included 20,870 subjects (HM = 19,284, HMRD = 1586) who underwent detailed routine blood tests and ophthalmic evaluations. HMRD cases and HM controls were matched using a nested case-control design. Then, the HMRD cases and HM controls were randomly assigned to the discovery cohort, validation cohort 1, and validation cohort 2 maintaining a 6:2:2 ratio, and other subjects were assigned to the HM validation cohort. Receiver operating characteristic curve analysis was performed to select feature indexes. Feature indexes were integrated into seven algorithm models, and an optimal model was selected based on the highest area under the curve (AUC) and accuracy. Results: Six feature indexes were selected: lymphocyte, basophil, mean platelet volume, platelet distribution width, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Among the algorithm models, the algorithm of conditional probability (ACP) showed the best performance achieving an AUC of 0.79, a diagnostic accuracy of 0.72, a sensitivity of 0.71, and a specificity of 0.74 in the discovery cohort. A good performance of the ACP model was also observed in the validation cohort 1 (AUC = 0.81, accuracy = 0.72, sensitivity = 0.71, specificity = 0.73) and validation cohort 2 (AUC = 0.77, accuracy = 0.71, sensitivity = 0.70, specificity = 0.72). In addition, ACP model calibration was found to be good across three cohorts. In the HM validation cohort, the ACP model achieved a diagnostic accuracy of 0.81 for negative classification. Conclusion: We have developed a routine blood parameters-based model with an ACP algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring and predicting the occurrence of RD in HM and can facilitate the prevention of HM progression to RD. According to the current study, routine blood measures are essential in patient risk classification, predictive diagnosis, and targeted therapy. Therefore, for high-risk RD persons, novel screening programs and prompt treatment plans are essential to enhance individual outcomes and healthcare offered to the community with HM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00319-3.

Time-series analysis of the contributors and drivers of Zhejiang's carbon emissions and intensity since China's accession to the WTO.

Carbon abatement efforts in China are penetrating to sub-national level. Zhejiang province, as the 4th wealthy region in China and important player in global market (e.g., textiles, cloth, petroleum, and chemical products), lacks in-depth study of its climate change mitigation efforts, especially after China's accession to the WTO in 2001. This paper analyzed carbon emissions and intensity in Zhejiang province using most comprehensive and time-series dataset available to date since 2002. Its carbon emissions were growing at a declining speed, and carbon intensity was almost halved. The leading emitters largely agglomerate around Hangzhou Bay, close to Zhoushan Port and Shanghai Port. The growth in absolute emissions primarily arose from higher investment (323.1 Mt) and exports (280.6 Mt), while the slower growth was mainly due to efficiency improvements (- 245.7 Mt) stemming from energy and carbon abatement policies and inflows of emission-intensive products from the rest of China. Similarly, declined carbon intensity was mainly attributed to efficiency improvement. Major contributors were sector S13-Non-metallic minerals, S12-Chemicals, S26-Transport, storage and post, S07-Textiles, and S10-Paper. Economic restructuring played different roles on emissions and intensity during various development stages. Policy implications of the findings are discussed for future developments.

Molecular Mechanism of Cold Tolerance of Centipedegrass Based on the Transcriptome.

Low temperature is an important limiting factor in the environment that affects the distribution, growth and development of warm-season grasses. Transcriptome sequencing has been widely used to mine candidate genes under low-temperature stress and other abiotic stresses. However, the molecular mechanism of centipedegrass in response to low-temperature stress was rarely reported. To understand the molecular mechanism of centipedegrass in response to low-temperature stress, we measured physiological indicators and sequenced the transcriptome of centipedegrass under different stress durations. Under cold stress, the SS content and APX activity of centipedegrass increased while the SOD activity decreased; the CAT activity, POD activity and flavonoid content first increased and then decreased; and the GSH-Px activity first decreased and then increased. Using full-length transcriptome and second-generation sequencing, we obtained 38.76 G subreads. These reads were integrated into 177,178 isoforms, and 885 differentially expressed transcripts were obtained. The expression of AUX_IAA and WRKY transcription factors and HSF transcription-influencing factors increased during cold stress. Through KEGG enrichment analysis, we determined that arginine and proline metabolism, plant circadian rhythm, plant hormone signal transduction and the flavonoid biosynthesis pathways played important roles in the cold stress resistance of centipedegrass. In addition, by using weighted gene coexpression network analysis (WGCNA), we determined that the turquoise module was significantly correlated with SS content and APX activity, while the blue module was significantly negatively correlated with POD and CAT activity. This paper is the first to report the response of centipedegrass to cold stress at the transcriptome level. Our results help to clarify the molecular mechanisms underlying the cold tolerance of warm-season grasses.

The plant FYVE domain-containing protein FREE1 associates with microprocessor components to repress miRNA biogenesis.

FYVE domain protein required for endosomal sorting 1 (FREE1), originally identified as a plant-specific component of the endosomal sorting complex required for transport (ESCRT) machinery, plays diverse roles either in endosomal sorting in the cytoplasm or in transcriptional regulation of abscisic acid signaling in the nucleus. However, to date, a role for FREE1 or other ESCRT components in the regulation of plant miRNA biology has not been discovered. Here, we demonstrate a nuclear function of FREE1 as a cofactor in miRNA biogenesis in plants. FREE1 directly interacts with the plant core microprocessor component CPL1 in nuclear bodies and disturbs the association between HYL1, SE and CPL1. Inactivation of FREE1 in the nucleus increases the binding affinity between HYL1, SE, and CPL1 and causes a transition of HYL1 from the inactive hyperphosphorylated version to the active hypophosphorylated form, thereby promoting miRNA biogenesis. Our results suggest that FREE1 has evolved as a negative regulator of miRNA biogenesis and provides evidence for a link between FYVE domain-containing proteins and miRNA biogenesis in plants.

Serum complement component 3, complement component 4 and complement component 1q levels predict progressive visual field loss in older women with primary angle closure glaucoma.

AIM: To evaluate the association between serum levels of complement component (C) 3, C4 and C1q and visual field (VF) loss in patients with primary angle closure glaucoma (PACG). METHODS: In this prospective cohort study, a total of 308 patients with PACG were included. The patients were followed up every 6 months (at least 2 years), with clinical examination and VF testing. Based on their sex and age, the subjects were stratified into male and female subgroups, and by age at <60 and ≥60 years per subgroup. RESULTS: One hundred twenty-three (39.94%) patients showed glaucoma VF progression. The serum levels of C3, C4 and C1q were significantly lower (p<0.05) in the progression group compared with the non-progression group in the ≥60 years female subgroup. In female patients with age ≥60 years, (1) lower levels of baseline C3 (HR=0.98, p<0.001), C4 (HR=0.96, p=0.01) and C1q levels (HR=0.99, p=0.003) were associated with a greater risk of VF progression; (2) patients with lower C3 levels had significantly (p<0.05) higher rates of VF loss progression, similar to those with lower C4 and lower C1q levels; and (3) the generalised additive model revealed a negative correlation between baseline C3 (p<0.001), C4 (p<0.001) and C1q (p<0.001) levels with the risk of VF progression. No statistical significance was observed in the male (<60 and ≥60 years) and female (<60 years) subgroups. CONCLUSION: Decreased C3, C4 and C1q levels at baseline were significantly associated with a greater risk of VF loss progression only in older women with PACG.

Association between Serum Potassium with Risk of Onset and Visual Field Progression in Patients with Primary Angle Close Glaucoma: A Cross-Sectional and Prospective Cohort Study.

Evidence suggests that ion metabolism may be associated with oxidative stress in the ocular tissue in glaucoma patients. This study is aimed at determining whether serum ion levels are associated with the onset and/or visual field (VF) progression of PACG. A total of 265 PACG and 166 healthy subjects were included in the cross-sectional study. Meanwhile, 265 subjects with PACG were followed up every six months for at least two years in the cohort study. All subjects were evaluated for serum concentrations of ions (calcium, phosphorus, potassium (K+), sodium, and chlorine) and underwent VF examination. Logistic regression analysis was performed to assess the risk factors for PACG. Cox regression analyses and Kaplan-Meier survival analyses were performed to identify factors associated with VF progression in PACG subjects. In the cross-sectional study, the K+ level (4.31 ± 0.39 mmol/L) was significantly higher in the PACG group than in the normal group (4.16 ± 0.35 mmol/L, P < 0.001). Multiple logistic regression showed that the increased K+ level was a risk factor of PACG (OR = 2.94, 95%CI = 1.63-5.32, P < 0.001). In the cohort study, there were 105 PACG subjects with progression and 160 PACG subjects without progression. The progression group had significantly higher baseline serum K+ levels (4.41 ± 0.37 mmol/L) than the no progression group (4.25 ± 0.39 mmol/L) (P = 0.002). The increased level of K+ at baseline was associated with faster VF progression (HR = 2.07, 95%CI = 1.23-3.46, P = 0.006). PACG subjects with higher baseline K+ levels had significantly lower VF nonprogression rates (51.94%) than subjects with lower K+ levels (68.38%, log-rank test P = 0.01). This study found that increased serum K+ level is a risk factor of PACG and is associated with faster VF progression in PACG, which might result from its influence on the oxidative stress process.

Physiological and Proteomic Responses of Contrasting Alfalfa (Medicago sativa L.) Varieties to High Temperature Stress.

High temperature (HT) is an important factor for limiting global plant distribution and agricultural production. As the global temperature continues to rise, it is essential to clarify the physiological and molecular mechanisms of alfalfa responding the high temperature, which will contribute to the improvement of heat resistance in leguminous crops. In this study, the physiological and proteomic responses of two alfalfa (Medicago sativa L.) varieties contrasting in heat tolerance, MS30 (heat-tolerant) and MS37 (heat-sensitive), were comparatively analyzed under the treatments of continuously rising temperatures for 42 days. The results showed that under the HT stress, the chlorophyll content and the chlorophyll fluorescence parameter (Fv/Fm) of alfalfa were significant reduced and some key photosynthesis-related proteins showed a down-regulated trend. Moreover, the content of Malondialdehyde (MDA) and the electrolyte leakage (EL) of alfalfa showed an upward trend, which indicates both alfalfa varieties were damaged under HT stress. However, because the antioxidation-reduction and osmotic adjustment ability of MS30 were significantly stronger than MS37, the damage degree of the photosynthetic system and membrane system of MS30 is significantly lower than that of MS37. On this basis, the global proteomics analysis was undertaken by tandem mass tags (TMT) technique, a total of 6,704 proteins were identified and quantified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that a series of key pathways including photosynthesis, metabolism, adjustment and repair were affected by HT stress. Through analyzing Venn diagrams of two alfalfa varieties, 160 and 213 differentially expressed proteins (DEPs) that had dynamic changes under HT stress were identified from MS30 and MS37, respectively. Among these DEPs, we screened out some key DEPs, such as ATP-dependent zinc metalloprotease FTSH protein, vitamin K epoxide reductase family protein, ClpB3, etc., which plays important functions in response to HT stress. In conclusion, the stronger heat-tolerance of MS30 was attributed to its higher adjustment and repair ability, which could cause the metabolic process of MS30 is more conducive to maintaining its survival and growth than MS37, especially at the later period of HT stress. This study provides a useful catalog of the Medicago sativa L. proteomes with the insight into its future genetic improvement of heat-resistance.

Association of systemic inflammation indices with visual field loss progression in patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches.

RELEVANCE: Accumulating evidence suggests a dysfunction of the para-inflammation in the retinal ganglion cell layer and the optic nerve head in patients with glaucoma. Currently, circulating blood platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) are regarded as novel indicators of systemic inflammation. Biomarkers allow early identification of patients with visual field (VF) loss progression and timely implementation of replacement therapies. OBJECTIVE: This study aimed to investigate whether higher inflammatory indices (PLR, NLR, and LMR) were associated with VF loss progression in patients with primary angle-closure glaucoma (PACG) for the predictive diagnostics, targeted prevention, and personalization of medical services. METHODS: This prospective cohort study followed up 277 patients with PACG for at least 24 months, with clinical examination and VF testing every 6 months. Inflammatory cell quantification, including platelets, neutrophils, lymphocytes, and monocytes, was measured using the Sysmex XN-A1 automated inflammatory cells quantification system. Three systemic inflammatory indices, PLR, NLR, and LMR, were determined on the basis of baseline neutrophil, lymphocyte, monocyte, and platelet counts in patients with PACG. The risk factors for PACG were analyzed using logistic regression, Cox proportional hazards regression, and the Kaplan-Meier curve. RESULTS: Our results revealed that 111 (40.07%) patients showed VF loss progression. The PLR was significantly higher (P = 0.046) in the progression group than in the non-progression group. A higher PLR (OR 1.05, 95% CI 1.01-1.08, P = 0.004) was a risk factor for PACG progression. In multivariate analyses, PLR independently predicted VF loss progression (HR 1.01, 95% CI 1.00-1.01, P = 0.04). Kaplan-Meier curve analysis showed that higher PLR indicated significantly higher rates of VF loss progression (66.91% vs. 52.90%, P = 0.03). Comparable results were observed in the male and female subgroups. CONCLUSION: Our findings revealed the significant association between a high PLR and a greater risk of VF loss progression in patients with PACG. PLR may be highly recommended as a novel predictive/diagnostic tool for the assessment of VF loss progression from the perspectives of predictive, preventive, and personalized medicine in vulnerable populations and for individual screening. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00260-3.

A Comprehensive Review of the Global Efforts on COVID-19 Vaccine Development.

This report examines various vaccine platforms including inactivated vaccines, protein-based vaccines, viral vector vaccines, and nucleic acid (DNA or mRNA) vaccines, and their ways of producing immunogens in cells.

A semi-quantitative risk ranking of potential human exposure to engineered nanoparticles (ENPs) in Europe.

Large quantities of engineered nanoparticles (ENPs) have emerged on the European market with the rapid development of nanotechnology, however knowledge of potential health risks to humans remains in its infancy. The ENP safety issue is of pressing concern as their novel physicochemical characteristics have been illustrated compared to other bulk-form counterparts. Therefore, it is critical to carry out a comprehensive risk assessment for ENPs to guide risk management in industrial sectors. Based on current data availability, a risk ranking model is developed in accordance with the European Chemicals Agency (ECHA) advice for ENP risk assessment. In this study a Quantity, Exposure, Hazard (QEH) risk scoring model was adopted for characterizing both quantitative and qualitative data, including potential exposure pathways and hazard information. Scores were assigned to quantities of ENPs used in consumer products, intake likelihoods (oral, inhalation, and dermal intake), and hazard potential. Exposure through environmental routes and through consumer products are regarded as significant potential exposure routes. This model prioritized ENPs used in Europe according to human health risk potential. Nano-titanium dioxide (TiO2) ranked the highest, resulting from exposure through consumer products. Silver nanoparticles (AgNP), as the second most critical ENP, is of most concern in terms of the risk from environmental sinks. Regarding the compartmentalization of total ENP risks to humans, the consumption of consumer products with nano-ingredients, especially nano-TiO2, nano-silicon dioxide (SiO2), and AgNP, constitutes the majority of the QEH risk index. The inadequacy of ENP risk management procedures is highlighted, not only during manufacturing, but also during nanomaterial waste disposal processes from marketplace through to the environment. Current risk assessments are based upon recent knowledge of the ENP class as novel pollutants, highlighting the need for further quantification of underlying risks as data emerges.

Effects of novel diabetic therapeutic footwear on preventing ulcer recurrence in patients with a history of diabetic foot ulceration: study protocol for an open-label, randomized, controlled trial.

BACKGROUND: Recurrence after the healing of a foot ulcer is very common among patients with diabetes mellitus. Novel diabetic therapeutic footwear consisted of merino wool, vibration chip, and orthopedic insoles is designed to influence multifaceted mechanisms of foot ulcer occurrence. The aim of this study is to examine the effect of the optimally designed therapeutic footwear on preventing ulcer recurrence in patients with a history of diabetic foot ulcers (DFU). METHODS/DESIGN: The trial is designed as a two arms, parallel-group, open-label randomized controlled intervention study. The Log-rank Test was used for calculating sample size based on the latest national multicenter survey data of DFU in China. Three hundred and twenty participants will be recruited from the Diabetic Foot Care Center, West China Hospital, Sichuan University. Adults with diabetic peripheral neuropathy, healed foot ulceration in the 3 months prior to randomization, and aged ≥18 years, will be recruited. Participants will be randomized to receive novel diabetic therapeutic footwear (n = 160) or their own footwear (n = 160). The primary outcome will be the incidence of ulcer recurrence. The secondary outcome will be measurements of barefoot dynamic plantar pressures, the influence of footwear adherence on ulcer recurrence, and the incidence of cardiovascular events. Assessment visits and data collection will be obtained at baseline, 1, 3, 6, 9, and 12 months. The intention-to-treat principle will be applied. A cox regression model will be used to calculate the hazard ratio for the incidence of ulcer recurrence. The change of barefoot dynamic plantar pressures will be assessed using repeated measures ANOVA. The study protocol has been approved by the Ethics Committee of The Biomedical Research Ethics Committee of West China Hospital, Sichuan University (Reference No. 2019(96)). DISCUSSION: This clinical trial will give information on the ability of novel diabetic footwear on preventing ulcer recurrence in patients with a history of diabetic foot ulceration. If the optimally designed therapeutic footwear does work well, the findings will contribute to the development of innovative treatment devices for preventing foot ulcer recurrence in high-risk patients. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900025538 . Registered on 31 August 2019.

Association Between Serum Lipid Levels and Patients With Primary Angle-Closure Glaucoma in China: A Cross Sectional, Case-Control Study.

Objective: To evaluate the serum lipid levels of patients with primary angle-closure glaucoma (PACG) and to investigate the relationship between serum lipid levels and PACG. Methods: In this cross-sectional, case-control study, a total of 320 PACG subjects and 242 age- and sex-matched control subjects were recruited. Serum high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (SDLDL-C), triglyceride (TRIG), and cholesterol (CHOL) levels were measured using enzymatic colorimetry. Serum apolipoprotein A (APOA), apolipoprotein B (APOB), apolipoprotein E (APOE), and lipoprotein(a) (LPa) levels were measured by immunoturbidimetry. Results: The serum LDL-C, TRIG, HDL-C, APOE, LPa, CHOL, APOB, and APOA levels were significantly higher (p < 0.05) in the PACG group than in the control group. Multiple liner regression analyses revealed that there was a statistically correlation between HDL-C levels and mean deviation MD (B = 0.389, P = 0.002, 95% confidence interval [CI] = -1.249 to -0.624); LDL-C levels and MD (B = 0.190, P = 0.019, 95% CI = -5.632 to -1.306); and CHOL levels and MD (B = 0.364, P = 0.27, 95% CI = -7.727 to -1.839). Logistic regression analyses showed that high serum HDL-C (odds ratio [OR] = 11.01, 95% CI = 5.616-21.587), LDL-C (OR = 1.330, 95% CI = 1.079-1.640), SDLDL-C (OR = 1.007, 95% CI = 1.005-1.008), APOA (OR = 13.621, 95% CI = 7.251-25.591), APOB (OR = 2.243, 95% CI = 1.060-4.732), LPa (OR = 0.999, 95% CI = 0.998-1.00), and CHOL (OR = 1.131, 95% CI = 1.005-1.326) levels were significantly associated with PACG. Conclusions: High serum HDL-C, LDL-C, APOA, APOB, LPa, and CHOL levels were associated with PACG.

Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections.

In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.