Search

Abstract Background Mutations in CHCHD2 have been linked to Parkinson’s disease, however, their exact pathophysiologic roles are unclear. The p32 protein has been suggested to interact with CHCHD2, however, the physiological functions of such interaction in the context of PD have not been clarified. Methods Interaction between CHCHD2 and p32 was confirmed by co-immunoprecipitation experiments. We studied the effect of p32-knockdown in the transgenic Drosophila and Hela cells expressing the wild type and the pathogenic variants of hCHCHD2. We further investigated the rescue ability of a custom generated p32-inhibitor in these models as well as in the human fibroblast derived neural precursor cells and the dopaminergic neurons harboring hCHCHD2-Arg145Gln. Results Our results showed that wildtype and mutant hCHCHD2 could bind to p32 in vitro, supported by in vivo interaction between human CHCHD2 and Drosophila p32. Knockdown of p32 reduced mutant hCHCHD2 levels in Drosophila and in vitro. In Drosophila hCHCHD2 models, inhibition of p32 through genetic knockdown and pharmacological treatment using a customized p32-inhibitor restored dopaminergic neuron numbers and improved mitochondrial morphology. These were correlated with improved locomotor function, reduced oxidative stress and decreased mortality. Consistently, Hela cells expressing mutant hCHCHD2 showed improved mitochondrial morphology and function after treatment with the p32-inhibitor. As compared to the isogenic control cells, large percentage of the mutant neural precursor cells and dopaminergic neurons harboring hCHCHD2-Arg145Gln contained fragmented mitochondria which was accompanied by lower ATP production and cell viability. The NPCs harboring hCHCHD2-Arg145Gln also had a marked increase in α-synuclein expression. The p32-inhibitor was able to ameliorate the mitochondrial fragmentation, restored ATP levels, increased cell viability and reduced α-synuclein level in these cells. Conclusions Our study identified p32 as a modulator of CHCHD2, possibly exerting its effects by reducing the toxic mutant hCHCHD2 expression and/or mitigating the downstream effects. Inhibition of the p32 pathway can be a potential therapeutic intervention for CHCHD2-linked PD and diseases involving mitochondrial dysfunction.

Tropospheric and ground-level levels of nitrogen dioxide (NO2) may have changed due to environmental policies and technological changes. Here, multiyear ground-based measurement (2014–2022) and satellite-derived retrieval (October 2004 to May 2023) datasets of NO2 concentrations are used to study the long-term ground-level and tropospheric NO2 patterns and trends at four time scales (interannual, quarterly, monthly, and hourly) over the three regions with the highest global NO2 concentrations. Several tropospheric NO2 hotspots are observed over industrialized and highly populated coastal zone areas. Over the past 18 years, these hotpots have generally decreased due to the implementation of strong and effective environmental protection policies. A significant trend reversal was observed in 2011 in East-central China. The maximum values of tropospheric NO2 occurred in winter over East-central China, Western Europe and East-central USA, with concentrations of 13.010, 4.243 and 4.149 × 1015 molecules cm−2, respectively. The Bohai Sea exhibited disproportionately high tropospheric NO2 levels, suggesting it as a significant contributor to regional nitrogen pollution. Ground-level NO2 shows similar temporal patterns to tropospheric concentrations, with over 82.8 % of sites in East-central China exceeding 30 μg m−3, compared to 12.8 % in Western Europe and 3.8 % in East-central USA. The analysis of diurnal variations in ground-level NO2 concentrations shows a significantly decrease during the daytime compared to nighttime, reflecting that the peak occurs at approximately 08:00 and 20:00 LT, while the lowest concentrations at around 12:00 LT. These insights clarify the impacts of environmental policies on NO2 levels and identify critical areas for targeted pollution control, enhancing our understanding of NO2 dynamics in high-pollution regions globally.

This paper proposes a strategy for synergistic improvement of physical fitness and skills of basketball players with the content of big data analysis of decision-making of physical fitness training programs and estimation of the human posture of basketball players. The data related to physical training and testing are processed using neural network methods, and a priori algorithms with continuous attributes discretize the data to realize data fragmentation and prevent data distortion caused by intrinsic correlation so as to build up a technical and methodological platform for physical training programs. At the same time, the basketball action posture estimation algorithm based on multi-scale spatiotemporal correlation features is proposed, and the human body temporal sequence feature capture module based on Transformer is constructed to improve the accuracy of basketball action posture estimation to meet the needs of skill training. The men’s basketball team of Shandong Agricultural University in China was used as a research object to carry out basketball physical fitness and skill training practices. The experimental group’s basketball players’ performance in barbell bench press and weighted squat was 11.06kg and 10.05kg more than that of the control group. Their performance in 3/4-court sprint running, and return running. Restriction-area footwork was 0.52s, 2.16s, and 1.97s faster than that of the control group, with significant differences in basketball skills, such as 14-meter round-trip straight-line dribbling around the pole and 30-second in situ spotting of two-handed chest pass (the first time in the experimental group). Basketball skills showed significant differences (P

Abstract Brain metastasis accounts for a large number of cancer-related deaths. The host immune system, involved at each step of the metastatic cascade, plays an important role in both the initiation of the brain metastasis and their treatment responses to various modalities, through either local and or systemic effect. However, few reliable immune biomarkers have been identified in predicting the development and the treatment outcome in patients with cancer brain metastasis. Here, we provide a focused perspective of immune related biomarkers for cancer metastasis to the brain and a thorough discussion of the potential utilization of specific biomarkers such as tumor mutation burden (TMB), genetic markers, circulating and tumor-infiltrating immune cells, cytokines, in predicting the brain disease progression and regression after therapeutic intervention. We hope to inspire the field to extend the research and establish practical guidelines for developing and validating immune related biomarkers to provide personalized treatment and improve treatment outcomes in patients with metastatic brain cancers.

Abstract A detailed understanding of the drilling fluid rheology and filtration properties is essential to assuring reduced fluid loss during the transport process. As per literature review, silica nanoparticle is an exceptional additive to enhance drilling fluid rheology and filtration properties enhancement. However, a correlation based on nano-SiO2-water-based drilling fluid that can quantify the rheology and filtration properties of nanofluids is not available. Thus, two data-driven machine learning approaches are proposed for prediction, i.e. artificial-neural-network and least-square-support-vector-machine (LSSVM). Parameters involved for the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate filtration volume. A feed-forward multilayer perceptron is constructed and optimised using the Levenberg–Marquardt learning algorithm. The parameters for the LSSVM are optimised using Couple Simulated Annealing. The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R 2 (coefficient of determination) value higher than 0.99 and MAE (mean absolute error) and MAPE (mean absolute percentage error) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data.

Background The Short Physical Performance Battery (SPPB) is a well-established functional assessment tool used for the screening and assessment of frailty and sarcopenia. However, the SPPB requires trained staff experienced in conducting the standardized protocol, which may limit its widespread use in clinical settings. The automated SPPB (eSPPB) was developed to address this potential barrier; however, its validity among frail older adults remains to be established. Therefore, this exploratory study compared the eSPPB and manual SPPB in patients attending a tertiary fall clinic in relation to their construct validity, reliability, and agreement. Methods We studied 37 community-dwelling older adults (mean age, 78.5±6.8 years; mean FRAIL score, 1.2±1.0; 65% pre-frail) attending a tertiary falls clinic. The participants used the mSPPB and eSPPB simultaneously. We evaluated the convergent validity, discriminatory ability, reliability, and agreement using partial correlation adjusted for age and sex, an SPPB cutoff of ≤8 to denote sarcopenia, intraclass correlation coefficients (ICC), and Bland-Altman plots, respectively. Results The eSPPB showed strong correlations with the mSPPB (r=0.933, p

Conventional synergy theory explains the inhibitory effects of drug combinations at specific times. Determining the magnitude of inhibition is crucial for exploring the synergy effect. In the results of previous studies, the Chou–Talalay multiple drug effect analysis demonstrated that the combination of a mutant oncolytic herpes virus (G207) and the chemotherapeutic agent (paclitaxel) is the most effective strategy for treating anaplastic thyroid cancer, compared to other combinations such as G207 and NV1023 or paclitaxel and doxorubicin. However, the mechanism behind the synergy effect of G207 and paclitaxel remains unknown, and measuring the synergy effect over time is challenging and expensive. In this study, we formulated a mathematical model to quantify the synergy of G207, paclitaxel, and both over time using the dataset. We conducted a Bayesian estimation of tumor cell proliferation over 16 days using Markov chain Monte Carlo sampling. The Bliss independence was incorporated into the model to compare the observed and expected responses to combination therapy. The expected antitumor effect was significantly lower than the experimental data, suggesting a synergistic effect. Our result showed that the antitumor effect was influenced by the rate of inhibition of tumor growth and the absolute growth delay. Additionally, we found that combination therapy achieved an additional 24% antitumoral effect and a 12‐day delay in cell growth. This modeling approach suggests the possibility of quantifying synergistic effects. [ABSTRACT FROM AUTHOR]