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Mechanics are intrinsic properties which appears throughout the formation, development, and aging processes of biological systems. Mechanics have been shown to play important roles in regulating the development and metastasis of tumors, and understanding tumor mechanics has emerged as a promising way to reveal the underlying mechanisms guiding tumor behaviors. In particular, tumors are highly complex diseases associated with multifaceted factors, including alterations in cancerous cells, tissues, and organs as well as microenvironmental cues, indicating that investigating tumor mechanics on multiple levels is significantly helpful for comprehensively understanding the effects of mechanics on tumor progression. Recently, diverse techniques have been developed for probing the mechanics of tumors, among which atomic force microscopy (AFM) has appeared as an excellent platform enabling simultaneously characterizing the structures and mechanical properties of living biological systems ranging from individual molecules and cells to tissue samples with unprecedented spatiotemporal resolution, offering novel possibilities for understanding tumor physics and contributing much to the studies of cancer. In this review, we survey the recent progress that has been achieved with the use of AFM for revealing micro/nanoscale mechanics in tumor development and metastasis. Challenges and future progress are also discussed.

Background Sleeping late has been a common phenomenon and brought harmful effects to our health. The purpose of this study was to investigate the association between sleep timing and major adverse cardiovascular events (MACEs) in patients with percutaneous coronary intervention (PCI). Methods Sleep onset time which was acquired by the way of sleep factors questionnaire in 426 inpatients was divided into before 22:00, 22:00 to 22:59, 23:00 to 23:59 and 24:00 and after. The median follow-up time was 35 months. The endpoints included angina pectoris (AP), new myocardial infarction (MI) or unplanned repeat revascularization, hospitalization for heart failure, cardiac death, nonfatal stroke, all-cause death and the composite endpoint of all events mentioned above. Cox proportional hazards regression was applied to analyze the relationship between sleep timing and endpoint events. Results A total of 64 composite endpoint events (CEEs) were reported, including 36 AP, 15 new MI or unplanned repeat revascularization, 6 hospitalization for heart failure, 2 nonfatal stroke and 5 all-cause death. Compared with sleeping time at 22:00–22:59, there was a higher incidence of AP in the bedtime ≥ 24:00 group (adjusted HR: 5.089; 95% CI: 1.278–20.260; P  = 0.021). In addition, bedtime ≥ 24:00 was also associated with an increased risk of CEEs in univariate Cox regression (unadjusted HR: 2.893; 95% CI: 1.452–5.767; P  = 0.003). After multivariable adjustments, bedtime ≥ 24:00 increased the risk of CEEs (adjusted HR: 3.156; 95% CI: 1.164–8.557; P  = 0.024). Conclusion Late sleeping increased the risk of MACEs and indicated a poor prognosis. It is imperative to instruct patients with PCI to form early bedtime habits.

Leveraging the conserved cancer genomes across mammals has the potential to transform driver gene discovery in orphan cancers. Here, we combine cross-species genomics with validation across human–dog–mouse systems to uncover a new bone tumor suppressor gene. Comparative genomics of spontaneous human and dog osteosarcomas (OS) expose Disks Large Homolog 2 (DLG2) as a tumor suppressor candidate. DLG2 copy number loss occurs in 42% of human and 56% of canine OS. Functional validation through pertinent human and canine OS DLG2 -deficient cell lines identifies a regulatory role of DLG2 in cell division, migration and tumorigenesis. Moreover, osteoblast-specific deletion of Dlg2 in a clinically relevant genetically engineered mouse model leads to acceleration of OS development, establishing DLG2 as a critical determinant of OS. This widely applicable cross-species approach serves as a platform to expedite the search of cancer drivers in rare human malignancies, offering new targets for cancer therapy.

A systematic study of various metal-insulator transition (MIT) associated phases of VO 2 , including metallic R phase and insulating phases (T, M1, M2), is required to uncover the physics of MIT and trigger their promising applications. Here, through an oxide inhibitor-assisted stoichiometry engineering, we show that all the insulating phases can be selectively stabilized in single-crystalline VO 2 beams at room temperature. The stoichiometry engineering strategy also provides precise spatial control of the phase configurations in as-grown VO 2 beams at the submicron-scale, introducing a fresh concept of phase transition route devices. For instance, the combination of different phase transition routes at the two sides of VO 2 beams gives birth to a family of single-crystalline VO 2 actuators with highly improved performance and functional diversity. This work provides a substantial understanding of the stoichiometry-temperature phase diagram and a stoichiometry engineering strategy for the effective phase management of VO 2 . Control of the phases associated with the metal-insulator transition in VO 2 underpins its applications as a phase change material. Here, the authors report phase management by means of oxide inhibitor-assisted growth and present high-performance VO 2 actuators based on asymmetric phase transition routes.

Self-assembled monolayers (SAMs) have become pivotal in achieving high-performance perovskite solar cells (PSCs) and organic solar cells (OSCs) by significantly minimizing interfacial energy losses. In this study, we propose a co-adsorb (CA) strategy employing a novel small molecule, 2-chloro-5-(trifluoromethyl)isonicotinic acid (PyCA-3F), introducing at the buried interface between 2PACz and the perovskite/organic layers. This approach effectively diminishes 2PACz’s aggregation, enhancing surface smoothness and increasing work function for the modified SAM layer, thereby providing a flattened buried interface with a favorable heterointerface for perovskite. The resultant improvements in crystallinity, minimized trap states, and augmented hole extraction and transfer capabilities have propelled power conversion efficiencies (PCEs) beyond 25% in PSCs with a p-i-n structure (certified at 24.68%). OSCs employing the CA strategy achieve remarkable PCEs of 19.51% based on PM1:PTQ10:m-BTP-PhC6 photoactive system. Notably, universal improvements have also been achieved for the other two popular OSC systems. After a 1000-hour maximal power point tracking, the encapsulated PSCs and OSCs retain approximately 90% and 80% of their initial PCEs, respectively. This work introduces a facile, rational, and effective method to enhance the performance of SAMs, realizing efficiency breakthroughs in both PSCs and OSCs with a favorable p-i-n device structure, along with improved operational stability. Self-assembled monolayers are essential for achieving high performance solar cells by minimizing interfacial energy losses. Here, authors the develop a co-adsorb strategy with a small molecule to provide a favorable heterointerface, realizing high efficiency in p-i-n perovskite and organic devices.

A nitrogen‐doped carbon nitride (NCN) has been synthesized and utilized for the sustainable generation of reactive nitrogen and oxygen radicals under mild photocatalytic conditions. This material exhibits a significant improvement in the separation and transfer of photoexcited charge carriers, which facilitates the direct activation of N─H and O─H bonds to achieve a variety of radical carboamination, oxyamination and deoxygenation reactions. The NCN catalyst has further demonstrated its robust photoredox activity through several successful applications, including the aerobic oxidation of boronic acids and the controllable oxidation of alcohols to aldehydes and carboxylic acids. Moreover, the NCN catalyst maintains its reactivity and efficiency even after multiple cycles of use, showcasing its durability and potential for practical applications in sustainable chemistry. A nitrogen‐doped carbon nitride (NCN) has been synthesized and applied for the sustainable generation of reactive nitrogen and oxygen radicals. The NCN catalyst facilitates radical carboamination, oxyamination, and deoxygenation reactions, as well as the oxidation of boronic acids and alcohols, all under mild photocatalytic conditions.

Background The application of laparoscopic liver resection (LLR) has expanded rapidly in recent decades. Although multiple authors have reported LLR shows improved safety and efficacy in treating hepatocellular carcinoma (HCC) compared with open liver resection (OLR), laparoscopic (LMLR) and open (OMLR) major liver resections for HCC treatment remain inadequately evaluated. This work aimed to test the hypothesis that LMLR is safer and more effective than OMLR for HCC. Methods Comparative cohort and registry studies on LMLR and OMLR, searched in PubMed, the Science Citation Index, EMBASE, and the Cochrane Library, and published before March 31, 2018, were collected systematically and meta-analyzed. Fixed- and random-effects models were employed for generating pooled estimates. Heterogeneity was assessed by the Q-statistic. Results Nine studies (1173 patients) were included. Although the pooled data showed operation time was markedly increased for LMLR in comparison with OMLR (weighted mean difference [WMD] 74.1, 95% CI 35.1 to 113.1, P  = 0.0002), blood loss was reduced (WMD = − 107.4, 95% CI − 179.0 to − 35.7, P  = 0.003), postoperative morbidity was lower (odds ratio [OR] 0.47, 95% CI 0.35 to 0.63, P  <  0.0001), and hospital stay was shorter (WMD = − 3.27, 95% CI − 4.72 to − 1.81, P  <  0.0001) in the LMLR group. Although 1-year disease-free survival (DFS) was increased in patients administered LMLR (OR = 1.55, 95% CI 1.04 to 2.31, P  = 0.03), other 1-, 3-, and 5-year survival outcomes (overall survival [OS] and/or DFS) were comparable in both groups. Conclusions Compared with OMLR, LMLR has short-term clinical advantages, including reduced blood loss, lower postsurgical morbidity, and shorter hospital stay in HCC, despite its longer operative time. Long-term oncological outcomes were comparable in both groups.

AbstractBavachinin (BVC) is a natural small molecule from the Chinese herb Fructus Psoraleae. It exhibits numerous pharmacological effects, including anti-cancer, anti-inflammation, anti-oxidation, anti-bacterial, anti-viral, and immunomodulatory properties. BVC may serve as a novel drug candidate for the treatment of rheumatoid arthritis (RA). Nevertheless, the effects and mechanisms of BVC against RA are still unknown. BVC targets were selected by Swiss Target Prediction and the PharmMapper database. RA-related targets were collected from the GeneCards, OMIM, DrugBank, TTD, and DisGeNET databases. PPI network construction and enrichment analysis were conducted by taking the intersection target of BVC targets and RA-related targets. Hub targets were further screened using Cytoscape and molecular docking. MH7A cell lines and collagen‐induced arthritis (CIA) mice were used to confirm the preventive effect of BVC on RA and its potential mechanism. Fifty-six RA-related targets of BVC were identified through databases. These genes were primarily enriched in PI3K/AKT signaling pathway according to KEGG enrichment analysis. Molecular docking analysis suggested that BVC had the highest binding energy with PPARG. The qPCR and western blotting results showed that BVC promoted the expression of PPARG at both the mRNA level and protein level. Western blotting indicated that BVC might affect MH7A cell functions through the PI3K/AKT pathway. Furthermore, treatment with BVC inhibited the proliferation, migration, and production of inflammatory cytokines in MH7A cells and induced cell apoptosis to a certain extent. In vivo, BVC alleviated joint injury and inflammatory response in CIA mice. This study revealed that BVC may inhibit the proliferation, migration, and production of inflammatory cytokines in MH7A cells, as well as cell apoptosis through the PPARG/PI3K/AKT signaling pathway. These findings provide a theoretical foundation for RA therapy.

Background Depression and sarcopenia are common diseases in the elderly population. However, the association between them is controversial. Based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database, a cross-sectional study was conducted to explore the relationship of calf circumference and physical performance with depression. Methods From the 8th wave of CLHLS conducted in 2018, data on calf circumference, physical performance, depressive symptoms, and demographic, socioeconomic, and health-related characteristics were collected. Multiple logistic regression was conducted to explore the impact of calf circumference, physical performance and their combination on depressive symptoms. Results We enrolled a total of 12,227 participants aged 83.4 ± 11.0 years, including 5689 (46.5%) men and 6538 (53.5%) women. Patients with depression were more likely to have low calf circumference (2274 [68.2%] vs. 5406 [60.8%], p <0.001) and poor physical performance (3[0, 6] vs. 1[0, 4], p <0.001). A significant multiplicative interaction was found between calf circumference and physical performance in their effect on depression. After adjusting for confounding factors, multiple logistic regression showed that a significant inverse correlation persisted between physical performance and depressive symptoms in normal (odds ratio [OR] = 1.20, 95% confidence interval [CI]: 1.15–1.26, p <0.001) and low (OR = 1.14, 95% CI: 1.11–1.18, p <0.001) calf circumference group, while the association between calf circumference and depression disappeared. Participants with low calf circumference and poor physical performance were 2.21 times more likely to have depression than those with normal calf circumference and physical performance. All results were found to be robust in sensitivity analyses. Conclusions Physical performance was significantly associated with depression in the elderly Chinese population. Attention should be paid to assess depressive symptoms in patients with poor physical performance.

To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations.