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Abstract Background Pakistani’s health services delivery system has been rarely evaluated regarding patient satisfaction. This study examined the performance of the Pakistani health system from the perspective of doctor services (DS), digital payment system (DPS), nurses’ services (NS), laboratory services (LS), pharmacy services (PHS), registration services (RS), physical services (environmentally and tangible) and doctor-patient communication (DPC) about patient satisfaction. A random sampling technique was adopted for data collection. Methodology The Social Science Statistical Package (SPSS), analysis of moment structures (AMOS), and structural equation modeling were used to analyze the data for reliability, validity, correlations, and descriptive findings. The 879 responses were used for study analysis. Results The study revealed that patient satisfaction was found to be significantly affected positively by LS, PHS, DS, NS, and DPS, while DPC, RS, and PF were impacted non-significantly. Consequently, there is a considerable communication gap in the doctor-patient interaction, and Pakistan's healthcare system is confronted with a shortage of physical infrastructure and challenges in the digital system. Conclusion Furthermore, the insufficient emphasis on registration services necessitates immediate action to improve the entire patient experience and satisfaction. Identifying these shortcomings has the potential to result in a healthcare system that is more efficient and focused on the needs of the patients.

Histone deacetylases (HDACs) are proteases that play a key role in chromosome structural modification and gene expression regulation, and the involvement of HDACs in cancer, the nervous system, and the metabolic and immune system has been well reviewed. Our understanding of the function of HDACs in the vascular system has recently progressed, and a significant variety of HDAC inhibitors have been shown to be effective in the treatment of vascular diseases. However, few reviews have focused on the role of HDACs in the vascular system. In this study, the role of HDACs in the regulation of the vascular system mainly involving endothelial cells and vascular smooth muscle cells was discussed based on recent updates, and the role of HDACs in different vascular pathogenesis was summarized as well. Furthermore, the therapeutic effects and prospects of HDAC inhibitors were also addressed in this review.

Abstract The COVID-19 pandemic, as well as the complex response techniques employed to halt its spread, are both detrimental to mental and emotional well-being. Students’ lives have been damaged by social alienation and self-isolation. These effects must be detected, analyzed, and dealt with to make sure the well-being of individuals, specifically students. This research examines the impact of parent–child relationships, parental autonomy support, and social support on enhancing students’ mental well-being using data collected from post-COVID-19. The Potential participants were students from several universities in Pakistan. For this reason, we chose Pakistan’s Punjab province, with 8 prominent institutions, as the primary focus for data collection. A questionnaire was created to gather information from 355 students. For descriptive statistics, SPSS was used, while AMOS structural equation modeling was used to test hypotheses. The findings revealed that social support on mental well-being (standardized β = 0.43, t = 7.57, p

IntroductionThe telehealth service increased attention both during and after the Covid-19 outbreak. Nevertheless, there is a dearth of research in developing countries, including Pakistan. Hence, the objective of this study was to examine telehealth service quality dimensions to promote the telehealth behavior intention and sustainable growth of telehealth in Pakistan.MethodsThis study employed a cross-sectional descriptive design. Data were collected from doctors who were delivering telehealth services through a well-designed questionnaire. To examine the hypothesis of the study, we employed the Smart PLS structural equation modeling program, namely version 0.4.ResultsThe study findings indicate that medical service quality, affordability, information quality, waiting time, and safety have a positive impact on the intention to engage in telehealth behavior. Furthermore, the adoption of telehealth behavior has a significant favorable effect on the actual utilization of telehealth services, which in turn has a highly good impact on sustainable development.ConclusionThe study determined that telehealth services effectively decrease the amount of time and money spent on travel, while still offering convenient access to healthcare. Furthermore, telehealth has the potential to revolutionize payment methods, infrastructure, and staffing in the healthcare industry. Implementing a well-structured telehealth service model can yield beneficial results for a nation and its regulatory efforts in the modern age of technology.

PurposeThe causal associations between inflammatory factors and atrial fibrillation (AF) remained unclear. We aimed to investigate whether genetically predicted inflammatory proteins are related to the risk of AF, and vice versa.MethodsA bidirectional two-sample Mendelian randomization study was performed. The genetic variation of 91 inflammatory proteins were derived from genome-wide association study (GWAS) data of European ancestry (n = 14,824). Summary statistics for AF were obtained from a published meta-analysis study (n = 1,030,836) and the FinnGen study (n = 261,395).ResultsGenetically predicted fibroblast growth factor 5 (FGF5) was significantly positively associated with risk of AF [[odds ratio (OR): 1.07; 95% CI: 1.04–1.10; P

Background: Ras association domain family member 1 (RASSF1) encodes the RASSF1A protein, serving as a scaffold protein situated at the intersection of a complex signalling network. Aims: To evaluate the immunological and prognostic significance of RASSF1 expression in various types of human cancers, with a specific focus on lung cancer. Methods: Differential expression analysis of RASSF1 was conducted based on data from The Cancer Genome Atlas, Genotype-Tissue Expression, and Cancer Cell Line Encyclopaedia databases. Prognostic analysis was performed using the Cox regression test and Kaplan-Meier test. Spearman's test was utilized for correlation analysis. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) gene sets were employed to enrich the associated signaling pathways. Immunohistochemical staining and quantitative real-time PCR were employed to detect protein and mRNA expression levels, respectively. Results: RASSF1 expression was significantly lower in tumour tissues than in normal tissues in most cancers, and Cox regression analysis demonstrated a significant correlation between RASSF1 expression and the prognosis of over 12 types of cancer. Specifically, high RASSF1 expression was associated with poor OS in nine cancer types, including GBMLGG (HR = 4.98, P = 1.2e-31), LGG (HR = 3.72, P = 2.5e-10), and LAML (HR = 1.48, P = 2.4e-3). Further analysis showed that RASSF1 expression was significantly correlated with immune checkpoint- and immune-related genes. Moreover, RASSF1 expression is involved in tumour microenvironment (TME), RNA modification, genomic heterogeneity, and tumour stemness. GO and KEGG analyses showed that RASSF1 was closely related to tumour immune-related pathways. Finally, RASSF1A was moderately correlated with PD-L1 (R = 0.556), and RASSF1A overexpression significantly affected the expression of several genes involved in the Th17 cell differentiation signalling pathway in lung cancer. Conclusions: RASSF1 was differentially expressed in 29 human cancers and played a critical role in tumour immunity. Thus, RASSF1 has the potential to be used as a prognostic marker and reference for achieving more precise immunotherapy, particularly in lung cancer.

Precipitation inversion techniques are crucial in meteorological research, aiming to accurately detect the location and intensity of precipitation events, which is vital for extreme weather response strategies. This study introduces a new infrared precipitation inversion method, residual deep forest, which is based on a deep forest model with two key enhancements. First, a feature optimization filter module is used to ensure high correlation among features in the cascade forest, a component of the deep forest model. This optimization reduces computational burden while maintaining efficiency. Second, embedding a residual structure within the cascade forest creates the residual cascade forest, improving feature expression, information processing, and computational efficiency on high-dimensional, large-scale precipitation data. Experimental results show that residual deep forest surpasses traditional deep forest models and other classical machine learning techniques in precipitation inversion accuracy, especially in identifying high-intensity rainfall. The model achieves an average recall (AR) of 85.97%, a probability of detection of 75.46%, and a false alarm ratio of 49.13%. Ablation experiments demonstrate that the feature optimization filter module reduces training time by 3.56%, testing time by 1.24%, and memory usage by 3.97%, while the residual cascade forest module reduces these metrics by 1.30%, 0.90%, and 2.23%, respectively. The improvements in AR, probability of detection, and false alarm ratio, along with the reduction in computational resources, highlight the model's enhanced efficiency and performance. This method leverages infrared remote sensing to enhance the scope and accuracy of precipitation monitoring, reducing reliance on radar and other ground-based instruments, and significantly improving precipitation inversion accuracy while minimizing spatial and temporal complexity.

The critical role of the educational sector in improving and reforming society and the world has aroused the interest of governments and agencies to exert substantial financial resources to reduce the financial burden of parents in educating children and attaining a holistic approach to national development. This paper evaluates the impact of variables such as professional needs, form of training, duration period, perceived impact, and feedback effect on teacher professional development (PD) in Ghana. To facilitate the study objective, we used a survey design approach to collect data from 400 teachers in the Central region of Ghana. This paper adopted PLS-SEM for analysis. The study concludes that: (1) The duration period of PD training significantly affects teacher feedback effect and professional needs. (2) Feedback effect from teachers has a significant effect on PD. (3) The form of training that teachers receive has a direct effect on PD. (4) Perceived impact influences feedback effect and PD. (5) Teachers' professional needs impact their feedback effect. To achieve the target goals, stakeholders, teachers, and PD planners for SHS, must ask more specifically from teachers about what knowledge or content they would like to learn and their prioritized mode of training and duration period needed.

BackgroundPsoriasis is an immune-mediated disorder influenced by environmental factors on a genetic basis. Despite advancements, challenges persist, including the diminishing efficacy of biologics and small-molecule targeted agents, alongside managing recurrence and psoriasis-related comorbidities. Unraveling the underlying pathogenesis and identifying valuable biomarkers remain pivotal for diagnosing and treating psoriasis.MethodsWe employed a series of bioinformatics (including single-cell sequencing data analysis and machine learning techniques) and statistical methods to integrate and analyze multi-level data. We observed the cellular changes in psoriatic skin tissues, screened the key genes Fatty acid binding protein 5 (FABP5) and The killer cell lectin-like receptor B1 (KLRB1), evaluated the efficacy of six widely prescribed drugs on psoriasis treatment in modulating the dendritic cell-associated pathway, and assessed their overall efficacy. Finally, RT-qPCR, immunohistochemistry, and immunofluorescence assays were used to validate.ResultsThe regulatory influence of dendritic cells (DCs) on T cells through the CD70/CD27 signaling pathway may emerge as a significant facet of the inflammatory response in psoriasis. Notably, FABP5 and KLRB1 exhibited up-regulation and co-localization in psoriatic skin tissues and M5-induced HaCaT cells, serving as potential biomarkers influencing psoriasis development.ConclusionOur study analyzed the impact of DC-T cell crosstalk in psoriasis, elucidated the characterization of two biomarkers, FABP5 and KLRB1, in psoriasis, and highlighted the promise and value of tofacitinib in psoriasis therapy targeting DCs.

Abstract Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.

This research leverages bibliometric methodologies, enhanced by the visual analytics capabilities of CiteSpace, to meticulously examine the evolution and current trends in energy trading within power markets, analyzing 642 scholarly articles from the Web of Science Core Collection spanning from 1996 to 2023. The study aims to illuminate the prevailing research landscape, growth patterns, and future directions in energy trading dynamics. Key findings include: (1) A noticeable escalation in the volume of publications, especially from 2021 to 2023, indicating a burgeoning interest and rapid evolution in this research area; (2) The author and institutional collaboration networks are in a nascent stage, with a predominantly China-centric international collaboration pattern, including significant partnerships with the United States, Australia, and the United Kingdom; (3) The focal points of research are centered around themes such as “energy management”, “demand-side innovation”, “decentralized energy trading”, and “strategic optimization”, covering areas such as intelligent grid technologies, energy market dynamics, and sustainable energy solutions. The study recommends enhancing collaborative networks, fusing technological and strategic dimensions in research, increasing focus and funding for emerging technologies, and promoting wider international and cross-disciplinary collaborations to enrich the understanding of energy trading dynamics in the context of electricity markets.

The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology.

N-terminal acetylation is a specific protein modification that occurs only at the N-terminus but plays a significant role in protein stability, folding, subcellular localization and protein-protein interactions. Computational methods enable finding N-terminal acetylated sites from large-scale proteins efficiently. However, limited by the number of the labeled proteins, existing tools only focus on certain subtypes of N-terminal acetylated sites on frequently detected amino acids. For example, NetAcet focuses on alanine, glycine, serine and threonine only, and N-Ace predicts on alanine, glycine, methionine, serine and threonine. With the growth of experimental N-terminal acetylated site data, it is observed that N-terminal protein acetylation occurs on nearly ten types of amino acids. To facilitate comprehensive analysis, we have developed MTNA (Multiple Types of N-terminal Acetylation), a deep learning network capable of accurately predicting N-terminal protein acetylation sites for various amino acids at the N-terminus. MTNA not only outperforms existing tools but also has the capability to identify rare types of N-terminal protein acetylated sites occurring on less studied amino acids.

With the increasing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unanticipated adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a serious public health issue for polypharmacy safety. Traditional experimental methods for detecting DDIs are expensive and time-consuming. Therefore, many computational methods have been developed in recent years to predict DDIs with the growing availability of data and advancements in artificial intelligence. In silico methods have proven to be effective in predicting DDIs, but detecting potential interactions, especially for newly discovered drugs without an existing DDI network, remains a challenge. In this study, we propose a predicting method of DDIs named HAG-DDI based on graph attention networks. We consider the differences in mechanisms between DDIs and add learning of semantic-level attention, which can focus on advanced representations of DDIs. By treating interactions as nodes and the presence of the same drug as edges, and constructing small subnetworks during training, we effectively mitigate potential bias issues arising from limited data availability. Our experimental results show that our method achieves an F1-score of 0.952, proving that our model is a viable alternative for DDIs prediction. The codes are available at: https://github.com/xtnenu/DDIFramework.

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS.

Abstract Background In Pakistan, the COVID-19 outbreak posed a significant challenge for healthcare workers in the country’s public hospitals. The HCWs faced several problems in terms of the COVID-19 pandemic. Therefore this study investigated how the COVID-19 pandemic has affected the medical staff at the public hospital in Sindh Province, Pakistan. Methods In this study, a qualitative exploratory design was used. Semi-Structure interviews (SSI) were conducted by using an open-indeed questionnaire (OIQ) for data collection. An inductive approach was used for theoretical data analysis. A total of 320 HCWs participated to complete the criteria of the study from 10 different public hospitals. Results The study result showed the Sindh public hospital’s insufficient infrastructure, lack of health protective equipment, shortages of isolation rooms and beds, and emergencies during the COVID-19 pandemic caused HCWs to experience physical and psychological weariness, sleep disturbance, mental stress, and fear of infection. Conclusion The study concluded that public hospitals’ insufficient infrastructure, furniture, emergency wards, and safety equipment during the COVID-19 pandemic significantly damaged HCWs’ physical and psychological health, generating fear of infection and sleep disturbance. Additionally, Sindh healthcare workers’ fear of illness and isolation may impair family connections.

Abstract Atmospheric chemistry transport models have been extensively applied in aerosol forecasts over recent decades, whereas they are facing challenges from uncertainties in emission rates, meteorological data, and over-simplified chemical parameterizations. Here, we developed a spatial-temporal deep learning framework, named PPN (Pollution-Predicting Net for PM2.5), to accurately and efficiently predict regional PM2.5 concentrations. It has an encoder-decoder architecture and combines the preceding PM2.5 observations and numerical weather prediction. Besides, the model proposes a weighted loss function to promote the forecasting performance in extreme events. We applied the proposed model to forecast 3-day PM2.5 concentrations over the Beijing-Tianjin-Hebei region in China on a three-hour-by-three-hour basis. Overall, the model showed good performance with R 2 and RMSE values of 0.7 and 17.7 μg m−3, respectively. It could capture the high PM2.5 concentration in the south and relatively low concentration in the north and exhibit better performance within the next 24 h. The use of the weighted loss function decreased the level of “high values underestimation, low values overestimation”, while incorporating the preceding PM2.5 observations into the encoder phase improved the predictive accuracy within 24 h. We also compared the model result with that from a state-of-the-art numerical model (WRF-Chem with pollutant data assimilation). The temporal R 2 and RMSE from the WRF-Chem were 0.30−0.77 and 19−45 μg m−3 while those from the PPN model were 0.42−0.84 and 15−42 μg m−3. The proposed model shows powerful capacity in aerosol forecasts and provides an efficient and accurate tool for early warning and management of regional pollution events.

Challenging times have put organizations in a perilous and chaotic state that demands immediate resolution and calls for effective leadership to help navigate out of the crisis. In this context, we focused on psychosocial safety climate theory to investigate the influence of adaptive leadership on safety citizenship behaviors by looking at the mediating effect of readiness to change and the moderating impact of psychosocial safety climate and proactive personality, particularly in the Pakistani healthcare sector. To test the hypotheses, the data were collected from 397 employees working in the healthcare sector of Pakistan at two different times. The results of this study supported the model. The moderated path analysis revealed that psychosocial safety climate strengthens the direct effect of adaptive leadership on readiness to change, whereas the moderating impact of a proactive personality also strengthens the relationship between readiness to change and safety citizenship behaviors. Similarly, both moderators significantly moderated the indirect impact of adaptive leadership on safety citizenship behaviors via readiness to change. To conclude, the present study has significant implications for organizations and practitioners in both steady and uncertain environments.

As members of the immune checkpoint family, PD-1 and its ligand PD-L1 play critical roles in maintaining the balance between autoimmunity and tolerance. The interaction of PD-1/PD-L1 is also involved in tumor evasion inside the tumor microenvironment, caused by reduced T cell activation, proliferation, cytotoxic secretion, and survival. Previous research has shown that the expression level of PD-1/PD-L1 may be regulated by ubiquitin-mediated proteasome degradation, which is an important mode of post-translational modification (PTM). PD-1/PD-L1 ubiquitin modification research in tumor immunotherapy is the subject of the present review, which aims to assess the most recent developments in this area. We offer a short explanation of PD-1/PD-L1 as well as some basic background information on the UPS system and discuss many routes that target E3s and DUBs, respectively, in the regulation of PD-1/PD-L1 in tumor immunotherapy. In addition, we offer numerous innovative prospective research areas for the future, as well as novel immunotherapy concepts and ideas. Taken together, the information compiled herein should serve as a comprehensive repository of information about tumor immunotherapy that is currently available, and it should be useful in the design of future studies, as well as the development of potential targets and strategies for future tumor immunotherapy.

Abstract Diabetic foot ulcers (DFUs) are associated with complex pathogenic factors and are considered a serious complication of diabetes. The potential mechanisms underlying DFUs have been increasingly investigated. Previous studies have focused on the three aspects of diabetic peripheral vascular disease, neuropathy, and wound infections. With advances in technology, researchers have been gradually conducting studies using immune cells, endothelial cells, keratinocytes, and fibroblasts, as they are involved in wound healing. It has been reported that the upregulation or downregulation of molecular signaling pathways is essential for the healing of DFUs. With a recent increase in the awareness of epigenetics, its regulatory role in wound healing has become a much sought‐after trend in the treatment of DFUs. This review focuses on four aspects involved in the pathogenesis of DFUs: physiological and pathological mechanisms, cellular mechanisms, molecular signaling pathway mechanisms, and epigenetics. Given the challenge in the treatment of DFUs, we are hopeful that our review will provide new ideas for peers.

Abstract As significant posttranslational modifications, ubiquitination and deubiquitination, whose balance is modulated by ubiquitin-conjugating enzymes and deubiquitinating enzymes (DUBs), can regulate many biological processes, such as controlling cell cycle progression, signal transduction and transcriptional regulation. Belonging to DUBs, ubiquitin-specific protease 28 (USP28) plays an essential role in turning over ubiquitination and then contributing to the stabilization of quantities of substrates, including several cancer-related proteins. In previous studies, USP28 has been demonstrated to participate in the progression of various cancers. Nevertheless, several reports have recently shown that in addition to promoting cancers, USP28 can also play an oncostatic role in some cancers. In this review, we summarize the correlation between USP28 and tumor behaviors. We initially give a brief introduction of the structure and related biological functions of USP28, and we then introduce some concrete substrates of USP28 and the underlying molecular mechanisms. In addition, the regulation of the actions and expression of USP28 is also discussed. Moreover, we concentrate on the impacts of USP28 on diverse hallmarks of cancer and discuss whether USP28 can accelerate or inhibit tumor progression. Furthermore, clinical relevance, including impacting clinical prognosis, influencing therapy resistance and being the therapy target in some cancers, is depicted systematically. Thus, assistance may be given to future experimental designs by the information provided here, and the potential of targeting USP28 for cancer therapy is emphasized.

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability.

Hydrodynamic pressure sensors offer an auxiliary approach for ocean exploration by unmanned underwater vehicles (UUVs). However, existing hydrodynamic pressure sensors often lack the ability to monitor subtle hydrodynamic stimuli in deep-sea environments. In this study, we present the development of a deep-sea hydrodynamic pressure sensor (DSHPS) capable of operating over a wide range of water depths while maintaining exceptional hydrodynamic sensing performance. The DSHPS device was systematically optimized by considering factors such as piezoelectric polyvinylidene fluoride–trifluoroethylene/barium titanate [P(VDF-TrFE)/BTO] nanofibers, electrode configurations, sensing element dimensions, integrated circuits, and packaging strategies. The optimized DSHPS exhibited a remarkable pressure gradient response, achieving a minimum pressure difference detection capability of approximately 0.11 Pa. Additionally, the DSHPS demonstrated outstanding performance in the spatial positioning of dipole sources, which was elucidated through theoretical charge modeling and fluid–structure interaction (FSI) simulations. Furthermore, the integration of a high Young’s modulus packaging strategy inspired by fish skull morphology ensured reliable sensing capabilities of the DSHPS even at depths of 1000 m in the deep sea. The DSHPS also exhibited consistent and reproducible positioning performance for subtle hydrodynamic stimulus sources across this wide range of water depths. We envision that the development of the DSHPS not only enhances our understanding of the evolutionary aspects of deep-sea canal lateral lines but also paves the way for the advancement of artificial hydrodynamic pressure sensors.

Polygonatum cyrtonema Hua, the dried rhizome of Polygonum multiflorum from the Liliaceae family, is a widely used medicinal herb with a long history of application. Its main active ingredients are polysaccharides, which have been demonstrated in contemporary studies to effectively delay the aging process. In the present study, homogeneous polysaccharide (PCP-1) was obtained after the purification and isolation of polysaccharides from Polygonatum cyrtonema Hua (PCP). The anti-aging activities of both were compared, and the possible mechanism of action for exerting anti-aging activity was explored using Caenorhabditis elegans (C. elegans). Research has indicated that PCP and PCP-1 exhibit potent anti-oxidant and anti-aging properties. Of particular note is that PCP-1 acts better than PCP. The two were able to prolong the lifespan of nematodes, improve the stress resistance of nematodes, reduce the accumulation of lipofuscin in the intestine, decrease the content of ROS and MDA in the body, increase the activity of the antioxidant enzymes SOD and CAT, promote the nuclear translocation of DAF-16, down-regulate the mRNA levels of the age-1 and daf-2 genes of the IIS pathway in nematodes, and up-regulate the expression of the daf-16, skn-1, sod-3, and hsp-16.2 genes. Based on the aforementioned findings, it is possible that the mechanism by which PCP and PCP-1 exert anti-aging effects may be through negative regulation of the IIS pathway, activation of the transcription factor DAF-16/FOXO, and enhancement of oxidative defenses and stress resistance in nematodes. Overall, the present study illustrated the great potential of polysaccharides from Polygonatum cyrtonema Hua in anti-aging and antioxidant activities. Specifically, PCP-1 demonstrated superior characteristics, which provides a reference for the future development of Polygonatum cyrtonema Hua polysaccharides.

ABSTRACT Objective: To determine the absolute number of serum T lymphocytes and cytokine levels and the characteristics of patients with active pulmonary tuberculosis and to assess their effect on the immune status of these patients and their diagnostic and predictive value for tuberculosis. Methods: We included 1,069 patients with active tuberculosis, 51 patients with latent tuberculosis infection, and 600 health individuals. Absolute serum T-lymphocyte counts and cytokine levels were quantified. Results: T lymphocytes were significantly reduced in patients with active tuberculosis when compared with healthy individuals. The immune function of patients gradually decreased with age and was stronger in female patients than in males. Th1 cells expressed higher levels of cytokines than did Th2 cells. Logistic regression analysis showed that reduced CD3+ T, CD8+ T, and NK cell counts, as well as reduced IL-4 and IFN-g expression, were independent influencing factors for active tuberculosis. ROC analysis showed that the sensitivity and specificity of absolute CD3+ T and CD8+ T lymphocyte counts and combined factors were significantly higher than were those of IL-4 and IFN-g for diagnosing active tuberculosis. Conclusions: Serum T-lymphocyte counts and cytokine levels can assess the immune status of tuberculosis patients; they are also useful biomarkers for predicting and diagnosing tuberculosis.

An ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI) accurately predicts PPIs through protein language models and outperforms state-of-the-art methods on multiple benchmark sets.

Abstract Choroidal melanoma (CM) is the most common type of diagnosed uveal melanoma (UM), which is prone to metastasis and exhibits a poor prognosis. The molecular mechanisms underlying CM progression need further elucidation to research effective therapeutic strategies. Histone deacetylase 7 (HDAC7) is very important in regulating cancer progression, but the significance and effect of HDAC7 on CM progression are unclear. In the present study, we found that HDAC7 is overexpressed in CM tissues versus normal tissues. We built HDAC7 overexpressing CM cell lines to study the functions of HDAC7 in CM progression and verified that upregulation of HDAC7 promoted the proliferation and metastasis of CM cells, while pharmacological inhibition of HDAC7 suppressed both the proliferation and metastasis of CM cells. Furthermore, we found that the aforementioned cancer-promoting effect of HDAC7 was mediated by c-Myc. Targeted inhibition of c-Myc inhibited CM progression by interfering with the HDAC7/c-Myc signaling pathway. Our study highlighted the function of targeting the HDAC7/c-Myc signaling pathway to intervene in the pathological process of CM, which provides potential therapeutic strategies for CM treatment.

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

Photothermal therapy (PTT) has become one of the most promising therapies in cancer treatment as its noninvasiveness, high selectivity, and favorable compliance in clinic. However, tumor thermotolerance and distal metastasis reduce its efficacy, becoming the bottleneck of applying PTT in clinic. In this study, a chidamide-loaded magnetic polypyrrole nanocomposite (CMPP) has been fabricated as a visualized cancer photothermal agent (PTA) to counter tumor thermotolerance and metastasis. The efficacy of CMPP was characterized by in vitro and in vivo assays. As a result, this kind of magnetic polypyrrole nanocomposites were black spherical nanoparticles, possessing a favorable photothermal effect and the suitable particle size of 176.97 ± 1.45 nm with a chidamide loading rate of 12.92 ± 0.45%. Besides, comparing with PTT, CMPP exhibited significantly higher cytotoxicity and cellular apoptosis rate in two tumor cell lines (B16-F10 and HepG2). In vivo study, the mice showed obvious near-infrared (NIR) and magnetic resonance imaging (MRI) dual-modal imaging at tumor sites and sentinel lymph nodes (SLNs); on the other hand, magnetic targeting guided CMPP achieved a cure level on melanoma-bearing mice through preventing metastasis and thermotolerance. Overall, with high loading efficiency of chidamide and strong magnetic targeting to tumor sites and SLNs, CMPP could significantly raise efficiency of PTT by targeting tumor thermotolerance and metastasis, and this strategy may be exploited therapeutically to upgrade PTT with MPP as one of appropriate carriers for histone deacetylase inhibitors (HDACis).