Network pharmacology combined with experimental validation to investigate the effect of Rongjin Niantong Fang on chondrocyte apoptosis in knee osteoarthritis.

Knee osteoarthritis (KOA) is a chronic degenerative disease that affects the quality of life of middle­aged and elderly individuals, and is one of the major factors leading to disability. Rongjin Niantong Fang (RJNTF) can alleviate the clinical symptoms of patients with KOA, but the molecular mechanism underlying its beneficial effects on KOA remains unknown. Using pharmacological analysis and in vitro experiments, the active components of RJNTF were analyzed to explore their potential therapeutic targets and mechanisms in KOA. The potential targets and core signaling pathways by which RJNTF exerts its effects on KOA were obtained from databases such as Gene Expression Omnibus, Traditional Chinese Medicine Systems Pharmacology and Analysis Platform. Subsequently, chondrocyte apoptosis was modeled using hydrogen peroxide (H2O2). Cell Counting Kit­8 assay involving a poly [ADP­ribose] polymerase­1 (PARP1) inhibitor, DAPI staining, reverse transcription­quantitative PCR, Annexin V­FITC/PI staining and flow cytometry, western blotting and co­immunoprecipitation analysis were used to determine the therapeutic efficacy of RJNTF on KOA and to uncover the molecular mechanism. It was found that PARP1­knockdown lentivirus, incubation with PARP1 inhibitor PJ34, medium and high doses of RJNTF significantly reduced H2O2­induced chondrocyte apoptosis. Medium and high doses of RJNTF downregulated the expression of cleaved caspase­3, cleaved PARP1 and PAR total proteins, as well as nucleus proteins of apoptosis­inducing factor (AIF) and migration inhibitory factor (MIF), and upregulated the expression of caspase­3, PARP1 total protein, as well as the cytoplasmic expression of AIF and MIF, suggesting that RJNTF may inhibit chondrocyte apoptosis through the PARP1/AIF signaling pathway.

A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties.

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Dendrobium mixture ameliorates hepatic injury induced by insulin resistance in vitro and in vivo through the downregulation of AGE/RAGE/Akt signaling pathway.

Dendrobium mixture (DM) is a patented Chinese herbal medicine which has been shown to ameliorate type 2 diabetes mellitus (T2DM) with non-alcoholic fatty liver disease (NAFLD) in vivo and in vitro. We aimed to investigate the underlying mechanism of DM as a therapeutic agent in attenuating liver steatosis in relation to type 2 diabetes mellitus (T2DM). DM (16.2 g/kg/d) was administered to db/db mice for 4 weeks. The db/m mice and db/db mice in the control and model groups were given normal saline. Additionally, DM (11.25 g/kg/d) was administered to Sprague-Dawley (SD) rats, and the serum was collected and used in an experiment involving palmitic acid (PA)-induced human liver HepG2 cells with abnormal lipid and glucose metabolism. In db/db mice, the administration of DM significantly alleviated liver steatosis, including histological damage and cell apoptosis. DM was found to prevent the upregulation of the RAGE and AKT1 proteins in liver tissues. The underlying mechanism of DM was further studied in PA-induced HepG2 cells. Post-DM administration serum from SD rats reduced lipid accumulation and regulated glucose metabolism in HepG2 cells. Consequently, it inhibited RAGE/AKT signaling and restored autophagy activity. The upregulated autophagy was associated with the mTOR-AMPK signaling pathway. Furthermore, post-DM administration serum reduced apoptosis of hepatocytes in PA-induced HepG2 cells. Our study supports the potential use of DM as a therapeutic agent for the treatment of NAFLD in T2DM. The mechanism underlying this therapeutic potential is associated with the downregulation of the AGE/RAGE/Akt signaling pathway.

Graph Reasoning Module for Alzheimer's Disease Diagnosis: A Plug-and-Play Method.

Recent advances in deep learning have led to increased adoption of convolutional neural networks (CNN) for structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) detection. AD results in widespread damage to neurons in different brain regions and destroys their connections. However, current CNN-based methods struggle to relate spatially distant information effectively. To solve this problem, we propose a graph reasoning module (GRM), which can be directly incorporated into CNN-based AD detection models to simulate the underlying relationship between different brain regions and boost AD diagnosis performance. Specifically, in GRM, an adaptive graph Transformer (AGT) block is designed to adaptively construct a graph representation based on the feature map given by CNN, a graph convolutional network (GCN) block is adopted to update the graph representation, and a feature map reconstruction (FMR) block is built to convert the learned graph representation to a feature map. Experimental results demonstrate that the insertion of the GRM in the existing AD classification model can increase its balanced accuracy by more than 4.3%. The GRM-embedded model achieves state-of-the-art performance compared with current deep learning-based AD diagnosis methods, with a balanced accuracy of 86.2%.

Inverse design of chiral functional films by a robotic AI-guided system.

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

Yersinia pestis and Plague: some knowns and unknowns.

Since its first identification in 1894 during the third pandemic in Hong Kong, there has been significant progress of understanding the lifestyle of Yersinia pestis, the pathogen that is responsible for plague. Although we now have some understanding of the pathogen's physiology, genetics, genomics, evolution, gene regulation, pathogenesis and immunity, there are many unknown aspects of the pathogen and its disease development. Here, we focus on some of the knowns and unknowns relating to Y. pestis and plague. We notably focus on some key Y. pestis physiological and virulence traits that are important for its mammal-flea-mammal life cycle but also its emergence from the enteropathogen Yersinia pseudotuberculosis. Some aspects of the genetic diversity of Y. pestis, the distribution and ecology of plague as well as the medical countermeasures to protect our population are also provided. Lastly, we present some biosafety and biosecurity information related to Y. pestis and plague.

The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries.

Background: The global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR. Method: Cross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1-30% in each country. Results: Overall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03-0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs. Conclusion: Booster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection.

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

Monkeypox outbreaks in the context of the COVID-19 pandemic: Network and clustering analyses of global risks and modified SEIR prediction of epidemic trends.

Background: Ninety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends. Method: Twenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis. Results: Between historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61-7.84 in the early stage and 0.70-4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022. Conclusions: In the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Progress on Prevention and Treatment of Cerebral Small Vascular Disease Using Integrative Medicine.

Cerebral small vessel disease (CSVD) is a senile brain lesion caused by the abnormal structure and function of arterioles, venules and capillaries in the aging brain. The etiology of CSVD is complex, and disease is often asymptomatic in its early stages. However, as CSVD develops, brain disorders may occur, such as stroke, cognitive dysfunction, dyskinesia and mood disorders, and heart, kidney, eye and systemic disorders. As the population continues to age, the burden of CSVD is increasing. Moreover, there is an urgent need for better screening methods and diagnostic markers for CSVD, in addition to preventive and asymptomatic- and mild-stage treatments. Integrative medicine (IM), which combines the holistic concepts and syndrome differentiations of Chinese medicine with modern medical perspectives, has unique advantages for the prevention and treatment of CSVD. In this review, we summarize the biological markers, ultrasound and imaging features, disease-related genes and risk factors relevant to CSVD diagnosis and screening. Furthermore, we discuss IM-based CSVD prevention and treatment strategies to stimulate further research in this field.

No evidence for persistent natural plague reservoirs in historical and modern Europe.

Caused by Yersinia pestis, plague ravaged the world through three known pandemics: the First or the Justinianic (6th-8th century); the Second (beginning with the Black Death during c.1338-1353 and lasting until the 19th century); and the Third (which became global in 1894). It is debatable whether Y. pestis persisted in European wildlife reservoirs or was repeatedly introduced from outside Europe (as covered by European Union and the British Isles). Here, we analyze environmental data (soil characteristics and climate) from active Chinese plague reservoirs to assess whether such environmental conditions in Europe had ever supported "natural plague reservoirs". We have used new statistical methods which are validated through predicting the presence of modern plague reservoirs in the western United States. We find no support for persistent natural plague reservoirs in either historical or modern Europe. Two factors make Europe unfavorable for long-term plague reservoirs: 1) Soil texture and biochemistry and 2) low rodent diversity. By comparing rodent communities in Europe with those in China and the United States, we conclude that a lack of suitable host species might be the main reason for the absence of plague reservoirs in Europe today. These findings support the hypothesis that long-term plague reservoirs did not exist in Europe and therefore question the importance of wildlife rodent species as the primary plague hosts in Europe.

Progress in Parkinson's disease animal models of genetic defects: Characteristics and application.

Parkinson's disease (PD) is the second major progressive neurodegenerative disease, which critically impacts patients' quality of life. Based on genetics, animal models of genetic defects created by gene editing technology have clear advantages in reflecting PD's pathogenesis and pathological characteristics and exploring potential therapeutic targets for PD. In this review, we summarized animal models of genetic defects in various pathogenesis of PD, including α-synuclein abnormal encoding, autophagy-lysosome system defects, ubiquitin protease system defects, and mitochondria-related dysfunction, and discuss their respective advantages, limitations, and application directions to provide a reference for the application of animal models of PD and research on anti-PD therapy.

Chiral biosensing at both interband transition and plasmonic extinction regions using twisted-stacked nanowire arrays.

Chiral metal nanostructures that exhibit strong chiroptical properties and enhanced light-matter interactions have recently attracted great interest due to their potential applications including chiral sensing and asymmetric synthesis. Most studies in this field focused on chiral sensing using circular dichroism (CD) responses at the plasmonic extinction region. In comparison, little is known about their CD responses at interband transition regions and their utility in chiral biosensing. Herein, we constructed a series of twisted-stacked silver nanowire arrays (TNAs) featuring CD signals at both the interband transition and plasmonic extinction regions and that are independently controllable. These TNAs are highly sensitive towards protein secondary structures. Proteins containing more ß-sheets are more sensitive toward strong chiral plasmonic fields, whereas proteins rich in α-helices tend to generate larger CD shifts at the interband transition region of TNAs. The mutually independent optical activities at the interband transition and plasmonic extinction regions complement each other, providing more sensitivity and reliability in chiral biosensing.

Jiangu granule ameliorated OVX rats bone loss by modulating gut microbiota-SCFAs-Treg/Th17 axis.

BACKGROUND: Postmenopausal osteoporosis (PMOP) is a common disease that has decreased bone strength as its main symptom after menopause. Effective treatment for PMOP remains lacking, but traditional Chinese medicine has some advantages in delaying bone loss. Jiangu granule is a traditional Chinese medicine prescription commonly used to treat PMOP. Previous studies have demonstrated its efficacy, but the mechanism of action remains uncharacterized. PURPOSE: This study aims to observe and discuss the mechanism of Jiangu granule to ameliorate bone loss in OVX rats by regulating the gut microbiota (GM)-short-chain fatty acids (SCFAs)- Treg/Th17 axis. METHODS: Female SD rats were divided into the sham operation (S), Jiangu granule (J), and model group (M). Bilateral ovaries were surgically removed from the rats in the J and M groups. After 6 and 12 weeks, rats were sacrificed, and femur, tibia, vertebrae, serum, spleen, colon, and feces samples were collected. We detected the strength of bones, gut microbiota structure, and SCFAs in feces, the Treg and Th17 cell levels in the spleen, and cytokine levels in the serum. RESULT: Jiangu granule restored the abundance of gut microbiota, increased the content of SCFAs, reduced the permeability of colon epithelium, increased the proportion of Treg cells in the spleen, changed the osteoimmunomodulation-related cytokines, effectively prevented bone loss, and enhanced bone strength. CONCLUSION: Jiangu granule can effectively improve bone loss in OVX rats, possibly by regulating the "GM-SCFAs-Treg/Th17″ axis.

Abundance and characteristics of microplastics in the surface water and sediment of parks in Xi'an city, Northwest China.

Microplastics (MPs), as a new type of pollutants, have attracted wide attention especially in recent years, but there was insufficient research on the distribution and characteristics of MPs in urban park water body. In this study, the pollution of MPs in water and sediment of Xi'an, the largest city in northwest China, was investigated. The MPs concentration in the surface water and sediment was 2900-6970 items/m3 and 940-3560 items/kg, respectively. According to the urban functions, the parks were divided into residential areas, commercial areas, tourism areas and industrial areas, and the highest abundance of MPs was observed in the tourism and residential areas, suggesting the impacts of human activities. MPs in these parks were mainly in four kinds of shapes, namely fiber, pellet, fragment and film, and dominated by fibers and fragments. Most of the extracted MPs were small in size, and 63-92% of them were smaller than 0.5 mm. Polypropylene and polyethylene terephthalate were the main polymer types in surface water and sediments, respectively. This study showed that the park water and sediment can be used as an important "sink" in MPs, which is of great significance for monitoring and alleviating the pollution of urban MPs. This study provided important reference for better understanding MPs levels in inland freshwaters.

Distribution and characteristics of microplastics in the basin of Chishui River in Renhuai, China.

As an emerging pollutant, microplastics widely exist in rivers all over the world. Due to the differences of economic development, economic structure and population in different regions, the abundance of microplastics in rivers is different. In those areas where agriculture is developed, the content of film microplastics is more, while in densely populated areas, the content of fibrous microplastics is more. Taking Renhuai Basin of Chishui River as the research object, the pollution characteristics and current situation of microplastics in the basin were analyzed, and the contamination risk of microplastics was evaluated. The abundance of microplastics in Renhuai basin of Chishui River ranges from 1.77 to 14.33 items/L. The main forms of microplastics were fibrous (59.4%), white (including transparent) (41.3%) and polychromatic (44.1%). The particle size of microplastics was mainly 500- 1000 µm (63.9%). According to the assessment, the risk of microplastics in the basin is 111.79, which is a secondary risk area. This study can provide a further reference for understanding the pollution characteristics of microplastics in rivers.

The occurrence and abundance of microplastics in surface water and sediment of the West River downstream, in the south of China.

Microplastic pollution has aroused great concern in inland waters. Freshwater is the transport routes and potential sources for plastic fragments to the oceans. However, information especially about the occurrence of microplastics in freshwater systems is unclear in certain key areas. This work studied the distribution and characteristics for microplastics in the downstream area of West River. Both sediment and surface water detected microplastics with abundance ranging from 2560 to 10,240 items/kg and 2.99 to 9.87 items/L, respectively. Small size (<0.5 mm) and fiber were the main size and type in both surface waters and sediments. Polypropylene, polyethylene, polyethylene, polyvinyl chloride and polyethylene terephthalate were the polymer types of microplastics, as identified using a Fourier transform infrared spectrometer. In addition, findings here might be in consideration of studying about the distribution of microplastics and the degree to which they were influenced by the use of land. In descending order, the highest microplastics abundance was observed in commercial/public/recreational > residential > industrial > natural areas. Our results indicate the occurrence of high abundance microplastics in river impacted by human activities, and suggest that spatial distribution of microplastics varies between different land use areas.

Bimetallic nickel-molybdenum/tungsten nanoalloys for high-efficiency hydrogen oxidation catalysis in alkaline electrolytes.

Hydroxide exchange membrane fuel cells offer possibility of adopting platinum-group-metal-free catalysts to negotiate sluggish oxygen reduction reaction. Unfortunately, the ultrafast hydrogen oxidation reaction (HOR) on platinum decreases at least two orders of magnitude by switching the electrolytes from acid to base, causing high platinum-group-metal loadings. Here we show that a nickel-molybdenum nanoalloy with tetragonal MoNi4 phase can catalyze the HOR efficiently in alkaline electrolytes. The catalyst exhibits a high apparent exchange current density of 3.41 milliamperes per square centimeter and operates very stable, which is 1.4 times higher than that of state-of-the-art Pt/C catalyst. With this catalyst, we further demonstrate the capability to tolerate carbon monoxide poisoning. Marked HOR activity was also observed on similarly designed WNi4 catalyst. We attribute this remarkable HOR reactivity to an alloy effect that enables optimum adsorption of hydrogen on nickel and hydroxyl on molybdenum (tungsten), which synergistically promotes the Volmer reaction.