Factors influencing depressive symptoms in Chinese female breast cancer patients: a meta-analysis.

Objective: To systematically evaluate and explore the factors influencing depressive symptoms in female breast cancer patients in China through meta-analysis. Methods: Relevant data were retrieved from cross-sectional studies or cohort studies on depressive symptoms of Chinese breast cancer within the following databases: PubMed, Embase, Cohrane Library, Web of 105 Science, Database of Medical Literature (CBM), Wan Fang Data, CNKI, and VIP databases. The literature screening, data extraction and literature quality evaluation were performed by two researchers by carefully reading the title, abstract and full text, and meta-analysis was performed using Stata 1.5 software after extracting relevant data. Results: Fourteen papers were finally included, with a cumulative total of 3,071 people surveyed, and a total of 1,298 breast cancer patients were detected with depression, with a detection rate of depressive symptoms of 42.26%; meta analysis showed that age less than 40 years old, unmarried, less than undergraduate education, monthly income <5,000 yuan, advanced breast cancer, radical breast cancer surgery, family history, living in rural areas, underlying disease stage and chemotherapy were associated with an increased incidence of depression in breast cancer patients. Conclusion: The detection rate of depressive symptoms in female breast cancer patients is high, and there is a need to strengthen depression-related psychological screening of breast cancer patients and provide them with individualized interventions to reduce the incidence of depression in breast cancer patients and to lower the level of depression already present in the patients.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Tensor Network Message Passing.

When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the exponential growth of the state space as the system size increases. Many standard methods have significant weaknesses. For instance, message-passing algorithms can be inaccurate and even fail to converge due to short loops, while tensor network methods can have exponential computational complexity in large graphs due to long loops. In this Letter, we propose a new method called "tensor network message passing." This approach allows us to compute local observables like marginal probabilities and correlations by combining the strengths of tensor networks in contracting small subgraphs with many short loops and the strengths of message-passing methods in globally sparse graphs, thus addressing the crucial weaknesses of both approaches. Our algorithm is exact for systems that are globally treelike and locally dense-connected when the dense local graphs have a limited tree width. We have conducted numerical experiments on synthetic and real-world graphs to compute magnetizations of Ising models and spin glasses, and have demonstrated the superiority of our approach over standard belief propagation and the recently proposed loopy message-passing algorithm. In addition, we discuss the potential applications of our method in inference problems in networks, combinatorial optimization problems, and decoding problems in quantum error correction.

Psychological impacts of thermal ablation and conventional thyroidectomy in BTN patients: a prospective observational study.

BACKGROUND: Thermal ablation and conventional thyroidectomy are effective therapeutic methods for treating benign thyroid nodules (BTNs), but the psychological impacts of these methods in BTN patients are largely unknown. MATERIALS AND METHODS: This survey study prospectively enrolled patients who were admitted to our hospital between July 2021 and July 2022. The four validated scales were applied to quantify psychological distress and sleep quality at five points (the day admitted to the hospital, the day discharged from the hospital, and 1, 3, and 6 months after treatment). Participants who were diagnosed with BTNs and completed the questionnaires were ultimately enrolled and divided into thermal ablation and conventional thyroidectomy groups. A propensity score matching (PSM) cohort was subsequently developed to evaluate longitudinal and cross-sectional changes in psychological-related indicators. RESULTS: Among 548 eligible BTN patients, 460 patients completed all the questionnaires throughout the follow-up (response rate: 83.94%), including 368 (80.00%) patients who underwent thermal ablation and 92 (20.00%) patients who underwent conventional thyroidectomy. After PSM, a total of 342 patients were enrolled (256 patients underwent thermal ablation, and 86 patients underwent conventional thyroidectomy). The psychological-related indicators of patients in the thermal ablation group remained relatively stable during the 6-month follow-up, but patients in the conventional thyroidectomy group may have experienced greater anxiety and sleep quality concerns in the longitudinal assessment. Additionally, in the cross-sectional evaluation, the sleep quality of the thermal ablation group was also better than that of the conventional thyroidectomy group postoperatively. CONCLUSIONS: Thermal ablation is superior to conventional thyroidectomy for BTN patients in terms of psychological-related indicators.

Artificial Funnel Nanochannel Device Emulates Synaptic Behavior.

Creating artificial synapses that can interact with biological neural systems is critical for developing advanced intelligent systems. However, there are still many difficulties, including device morphology and fluid selection. Based on Micro-Electro-Mechanical System technologies, we utilized two immiscible electrolytes to form a liquid/liquid interface at the tip of a funnel nanochannel, effectively enabling a wafer-level fabrication, interactions between multiple information carriers, and electron-to-chemical signal transitions. The distinctive ionic transport properties successfully achieved a hysteresis in ionic transport, resulting in adjustable multistage conductance gradient and synaptic functions. Notably, the device is similar to biological systems in terms of structure and signal carriers, especially for the low operating voltage (200 mV), which matches the biological neural potential (∼110 mV). This work lays the foundation for realizing the function of iontronics neuromorphic computing at ultralow operating voltages and in-memory computing, which can break the limits of information barriers for brain-machine interfaces.

Shell thickness-induced thermal dependence: highly sensitive core-shell CdSe/ZnS/POSS-based temperature probes.

Fluorescence nanothermometry based on quantum dots is a current research hotspot for novel non-contact temperature monitoring, and is of vital significance for the modulation and design of the sensing properties of sensors. Herein, a design strategy to modulate the temperature-sensing characteristics of quantum dots based on the thickness of a shell is proposed. In this study, CdSe/ZnS quantum dot/POSS-based temperature probe films with varying fluorescence characteristics were developed, and the influence of the ZnS shell on temperature sensing was examined by varying the thickness of the ZnS shell. The temperature dependency, linearity, range of applications, and reversibility of quantum dot thin film probes were all considerably regulated by the ZnS shell, according to research on quantum dot/POSS-based films coated with various shell thicknesses. The CdSe/ZnS temperature probe with 4 monolayers (MLs) stood out among the rest due to its strong thermal stability (at least 5 cycles), large usable temperature range (20-80 °C), and excellent temperature sensitivity (R2 > 0.994). The results demonstrated that the temperature sensing performance of quantum dots was the consequence of the combined effect of multiple temperature response properties induced by the thickness of the shell, and the shell control of quantum dots to optimize the temperature sensing performance was an essential approach for the design of temperature probes. This work demonstrates the great potential of the shell in tuning the temperature sensing performance of quantum dots and provides a viable approach for the design of quantum dot temperature probes.

Efficient automated high dynamic range 3D measurement via deep reinforcement learning.

High dynamic range 3D measurement technology, utilizing multiple exposures, is pivotal in industrial metrology. However, selecting the optimal exposure sequence to balance measurement efficiency and quality remains challenging. This study reinterprets this challenge as a Markov decision problem and presents an innovative exposure selection method rooted in deep reinforcement learning. Our approach's foundation is the exposure image prediction network (EIPN), designed to predict images under specific exposures, thereby simulating a virtual environment. Concurrently, we establish a reward function that amalgamates considerations of exposure number, exposure time, coverage, and accuracy, providing a comprehensive task definition and precise feedback. Building upon these foundational elements, the exposure selection network (ESN) emerges as the centerpiece of our strategy, acting decisively as an agent to derive the optimal exposure sequence selection. Experiments prove that the proposed method can obtain similar coverage (0.997 vs. 1) and precision (0.0263 mm vs. 0.0230 mm) with fewer exposures (generally 4) compared to the results of 20 exposures.

Case report of pediatric TTMV-related acute promyelocytic leukemia with central nervous system infiltration and rapid accumulation of RARA-LBD mutations.

TTMV::RARA is a recently reported fusion gene associated with acute promyelocytic leukemia (APL), caused by the integration of torque teno mini virus (TTMV) genomic fragments into the second intron of the RARA gene. Currently, there have been only six documented cases, with clinical presentations showing significant variability. Although initial responses to all-trans retinoic acid (ATRA) treatment may be observed in patients with TTMV::RARA-APL, the overall prognosis remains unfavorable among infrequent reported cases. This article presents a pediatric case that manifested as PML::RARA-negative APL with central nervous system involvement at onset. The patient experienced both intramedullary and extramedullary relapse one year after undergoing allogeneic hematopoietic stem cell transplantation. Upon identification as TTMV::RARA-APL and subsequent administration of two rounds of ATRA-based treatment, the patient rapidly developed multiple RARA ligand-binding domain mutations and demonstrated extensive resistance to ATRA and various other therapeutic interventions. Additionally, the patient experienced ARID1A mutant clone expansion and progressed MYC-targeted gene activation. This case represents the first documentation of extramedullary involvement at both the initial diagnosis and relapse stages, emphasizing the intricate clinical features and challenges associated with the rapid accumulation of multiple ATRA-resistant mutations in TTMV::RARA-APL, characterizing it as a distinct and complex sub-entity of atypical APL.

Responses in Plant Growth and Root Exudates of Pistia stratiotes under Zn and Cu Stress.

At present, the situation regarding heavy metal pollution in aquatic environments is becoming more and more serious. The bioaccumulation of heavy metals in aquatic plants causes obvious phytotoxicity, which can also induce secondary pollution in the aquatic environment. Zinc and copper, as indispensable elements for plant growth, are also prominent heavy metals in water pollution in China, and their concentrations play a crucial role in plant growth. In this study, we investigated the response of Pistia stratiotes (P. stratiotes) to different concentrations of Zn and Cu, and the results showed that plant growth and photosynthesis were inhibited under both Zn (1, 2, 4, and 8 mg/L) and Cu (0.2, 0.4, 0.8, and 1 mg/L) stresses. The relative growth rates of P. stratiotes under 8 mg/L Zn or 1 mg/L Cu stress were 6.33% and 6.90%, which were much lower than those in the control group (10.86%). Meanwhile, Zn and Cu stress caused insignificant change in the relative water contents of plants. The decrease in phlorophyll fluorescence parameters and chlorophyll contents suggested the significant photoinhibition of Zn and Cu stress. Chemical analysis of plant root exudates showed that the root secretion species obtained by gas chromatography-mass spectrometry (GC-MS) mainly included amino acids, alkanes, aldehydes, ketones, phenols, and more. Compared with the control group, the influence of Zn or Cu on the reduction in relative amounts of exudates was greater than that on the increase. The results of this study provide important data for the utilization of P. stratiotes in heavy metal-polluted water environments.

Knockdown of circ-Gatad1 alleviates LPS induced HK2 cell injury via targeting miR-22-3p/TRPM7 axis in septic acute kidney.

BACKGROUND: Sepsis is a life-threatening, systemic inflammatory disease that can lead to a variety of conditions, including septic acute kidney injury (AKI). Recently, multiple circular Rnas (circRNAs) have been implicated in the development of this disease. METHODS: In this study, we aimed to elucidate the role of circ-Gatad1 in sepsis induced AKI and its potential mechanism of action. High-throughput sequencing was used to investigate abnormal expression of circRNA in AKI and healthy volunteer. Bioinformatics analysis and luciferase reporting analysis were used to clarify the interacted relationship among circRNA, miRNA and mRNA. HK2 cells were treated with lipopolysaccharide (LPS) to establish septic AKI cell model. HK2 cells were employ to analysis the ROS, inflammatory cytokines expression, proliferation and apoptosis under LPS condition. RESULTS: The result show that the expression of circ-Gatad1 was increased in septic acute kidney patients. Downregulation circ-Gatad1 suppressed LPS-treated induced HK2 cells injury including apoptosis, proliferation ability, ROS and inflammatory cytokines level. Bioinformatics and luciferase report analysis confirmed that both miR-22-3p and TRPM7 were downstream targets of circ-Gatad1. Overexpression of TRPM7 or downregulation of miR-22-3p reversed the protective effect of si-circ-Gatad1 to HK2 after exposure to LPS (5 µg/ml) microenvironment. CONCLUSION: In conclusion, knockdown of circ-Gatad1 alleviates LPS induced HK2 cell injury via targeting miR-22-3p/TRPM7 axis in septic acute kidney.

Advances in zeolitic-imidazolate-framework-based catalysts for photo-/electrocatalytic water splitting, CO2 reduction and N2 reduction applications.

Harnessing electrical or solar energy for the renewable production of value-added fuels and chemicals through catalytic processes (such as photocatalysis and electrocatalysis) is promising to achieve the goal of carbon neutrality. Owing to the large number of highly accessible active sites, highly porous structure, and charge separation/transfer ability, as well as excellent stability against chemical and electrochemical corrosion, zeolite imidazolate framework (ZIF)-based catalysts have attracted significant attention. Strategic construction of heterojunctions, and alteration of the metal node and the organic ligand of the ZIFs effectively regulate the binding energy of intermediates and the reaction energy barriers that allow tunable catalytic activity and selectivity of a product during reaction. Focusing on the currently existing critical issues of insufficient kinetics for electron transport and selective generation of ideal products, this review starts from the characteristics and physiochemical advantages of ZIFs in catalytic applications, then introduces promising regulatory approaches for advancing the kinetic process in emerging CO2 reduction, water splitting and N2 reduction applications, before proposing perspective modification directions.

Comparative Bioequivalence and Food Effect of Two Formulations of 30-mg Nifedipine Controlled-Release Tablets in Healthy Chinese Adults.

Nifedipine is a potent antihypertensive medication classified as a dihydropyridine calcium channel blocker. The objective of this trial was to assess the bioequivalence of a 30-mg nifedipine controlled-release tablet and a reference drug in a cohort of healthy Chinese individuals. Two independent open-label, randomized, single-dose, crossover studies were conducted, 1 under fasting conditions (N = 44, with 1 participant dropping out midway) and the other under fed conditions (N = 44, with 4 participants dropping out midway). Plasma concentrations of nifedipine were determined using liquid chromatography-mass spectrometry, and pharmacokinetic (PK) parameters were calculated using noncompartmental analysis with Phoenix WinNonlin 8.0 software. In both fasting and fed studies, reasonable bioequivalence was observed for the PK parameters of both the test product and the reference drug. A good safety profile was demonstrated for both the test product and reference drug, with no serious adverse events reported, and both were similarly well tolerated. An important observation with food coadministration was that systemic exposure to nifedipine (based on area under the curve, AUC0-∞) was reduced by approximately 12%. The bioequivalence of the test product and reference drug under fasting/fed conditions in healthy subjects in China was demonstrated by the study results.

Abnormal functional connectivity of the reward circuit associated with early satiety in patients with postprandial distress syndrome.

Postprandial distress syndrome (PDS) is the most common functional dyspepsia (FD) subtype. Early satiety is one of the cardinal symptoms of the PDS subtype in FD patients. The heterogeneity of symptoms in FD patients hampered therapy for patients based on specific symptoms, necessitating a symptom-based understanding of the pathophysiology of FD. To investigate the correlation between reward circuit and symptom severity of PDS patients, seed (Nucleus accumbens, NAc, a key node in the reward circuit) based resting-state functional connectivity (FC) was applied in the neuroimaging data analysis. The results demonstrated that the patients with PDS manifested strengthened FC between NAc and the caudate, putamen, pallidum, amygdala, hippocampus, thalamus, anterior cingulate cortex (ACC), and insula. Moreover, the FC between NAc and ACC, insula, thalamus, and hippocampus exhibited significant positive associations with symptom severity. More importantly, the strengthened FC between NAc and the ACC, insula, amygdala, and hippocampus were found associated with the early satiety symptom of patients with PDS. This study indicated that the altered FC of reward circuit regions may play a role in the pathophysiology of patients with PDS, and some of the aberrant NAc-based FC within the reward circuit were more related to the early satiety of patients with PDS. These findings improve our symptom-based understanding of the central pathophysiology of FD, lay the groundwork for an objective diagnosis of FD, and shed light on the precise prescription for treating FD based on symptoms.

Dynamically or Kinetically Controlled? Computational Study of the Mechanisms of Electrophilic Aminoalkenylation of Heteroaromatics with Keteniminium Ions.

Quantum chemical calculations and molecular dynamics simulations were applied to study the electrophilic aminoalkenylation of heteroaromatics with keniminium ions. Post-transition state bifurcation (PTSB) was found in the electrophilic addition step for the aminoalkenylation of pyrroles and indoles, and the selectivity for these reactions was dynamically controlled. However, the aminoalkenylation of furan was kinetically controlled because no apparent PTSB was found in the electrophilic addition step. The substituents on the keteniminium ions can also affect the dynamic results for the aminoalkenylations to pyrroles: the C2-aminoalkenylated product is much more favored over the C3-aminoalkenylated product for keteniminium ions with electron-donating substituents, while the product ratio (C2 product/C3 product) decreased when stronger electron-withdrawing substituents were applied.

Unraveling quantum coherences mediating primary charge transfer processes in photosystem II reaction center.

Photosystem II (PSII) reaction center (RC) is a unique complex that is capable of efficiently separating electronic charges across the membrane. The primary energy- and charge-transfer (CT) processes occur on comparable ultrafast timescales, which makes it extremely challenging to understand the fundamental mechanism responsible for the near-unity quantum efficiency of the transfer. Here, we elucidate the role of quantum coherences in the ultrafast energy and CT in the PSII RC by performing two-dimensional (2D) electronic spectroscopy at the cryogenic temperature of 20 kelvin, which captures the distinct underlying quantum coherences. Specifically, we uncover the electronic and vibrational coherences along with their lifetimes during the primary ultrafast processes of energy and CT. We construct an excitonic model that provides evidence for coherent energy and CT at low temperature in the 2D electronic spectra. The principles could provide valuable guidelines for creating artificial photosystems with exploitation of system-bath coupling and control of coherences to optimize the photon conversion efficiency to specific functions.

Life-stage specificity and temporal variations in transcriptomes and DNA methylomes of the reef coral Pocillopora damicornis in response to thermal acclimation.

Understanding the acclimation capacity of reef corals across generations to thermal stress and its underlying molecular underpinnings could provide insights into their resilience and adaptive responses to future climate change. Here, we acclimated adult brooding coral Pocillopora damicornis to high temperature (32 °C vs. 29 °C) for three weeks and analyzed the changes in phenotypes, transcriptomes and DNA methylomes of adult corals and their brooded larvae. Results showed that although adult corals did not show noticeable bleaching after thermal exposure, they released fewer but larger larvae. Interestingly, larval cohorts from two consecutive lunar days exhibited contrasting physiological resistance to thermal stress, as evidenced by the divergent responses of area-normalized symbiont densities and photochemical efficiency to thermal stress. RNA-seq and whole-genome bisulfite sequencing revealed that adult and larval corals mounted distinct transcriptional and DNA methylation changes in response to thermal stress. Remarkably, larval transcriptomes and DNA methylomes also varied greatly among lunar days and thermal treatments, aligning well with their physiological metrics. Overall, our study shows that changes in transcriptomes and DNA methylomes in response to thermal acclimation can be highly life stage-specific. More importantly, thermally-acclimated adult corals could produce larval offspring with temporally contrasting photochemical performance and thermal resilience, and such variations in larval phenotypes are associated with differential transcriptomes and DNA methylomes, and are likely to increase the likelihood of reproductive success and plasticity of larval propagules under thermal stress.

Structure-Guided Discovery of Diverse Cytotoxic Dimeric Xanthones/Chromanones from Penicillium chrysogenum C-7-2-1 and Their Interconversion Properties.

Xanthone-chromanone homo- or heterodimers are regarded as a novel class of topoisomerase (Topo) inhibitors; however, limited information about these compounds is currently available. Here, 14 new (1-14) and 6 known tetrahydroxanthone chromanone homo- and heterodimers (15-20) are reported as isolated from Penicillium chrysogenum C-7-2-1. Their structures and absolute configurations were unambiguously demonstrated by a combination of spectroscopic data, single-crystal X-ray diffraction, modified Mosher's method, and electronic circular dichroism analyses. Plausible biosynthetic pathways are proposed. For the first time, it was discovered that tetrahydroxanthones can convert to chromanones in water, whereas chromone dimerization does not show this property. Among them, compounds 5, 7, 8, and 16 exhibited significant cytotoxicity against H23 cell line with IC50 values of 6.9, 6.4, 3.9, and 2.6 µM, respectively.

Correlation between serum microRNA-122 and VEGF expression and pregnancy outcome in gestational diabetes mellitus patients.

Objective: Gestational diabetes mellitus (GDM) seriously influences the health of mothers and babies, and there are still no effective early diagnostic markers. Therefore, our study planned to probe the correlation between serum microRNA-122 and VEGF expression and pregnancy outcome in GDM patients. Methods: This was a retrospective study of the correlation between serum microRNA-122 and vascular endothelial growth factor (VEGF) expression and pregnancy outcome in GDM patients. Sixty GDM patients admitted to the Fourth Hospital of Shijiazhuang from January 2021 to October 2022 were included in the research group (RG), and another 60 healthy pregnant women were included in the control group (CG). Serum miR-122 and VEGF levels were quantified using quantitative real-time polymerase chain reaction. The value of miR-122 and VEGF in predicting adverse pregnancy outcomes was analyzed by receiver operating characteristic curve. Results: Serum miR-122 and VEGF levels in the RG were higher relative to the CG. The total occurrence of adverse pregnancy outcomes in the RG was higher relative to the CG (P<0.05). Serum miR-122 together with VEGF levels in the poor outcome group was higher relative to the good outcome group (P<0.05). ROC analysis revealed that miR-122 and VEGF could be used to predict adverse pregnancy outcome (P<0.0001). The area under the curve of miR-122 was 0.860, 95% confidence interval (CI) =0.793-0.926, and the area under the curve of VEGF was 0.780, 95% CI =0.694-0.866. Serum levels of miR-122, VEGF were positively related with abortion, preterm delivery, low birth weight infants, macrogenesis infants, and fetal development abnormalities (P<0.001). Conclusion: The higher serum miR-122 and VEGF levels in GDM patients with satisfactory blood glucose control, the greater the probability of adverse pregnancy outcome, which should be paid attention to by clinicians.

Learning nonequilibrium statistical mechanics and dynamical phase transitions.

Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent behavior further requires tracking nonequilibrium systems under a control parameter. While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. Here, we develop a general computational framework to study the time evolution of nonequilibrium systems in statistical mechanics by leveraging variational autoregressive networks, which offer an efficient computation on the dynamical partition function, a central quantity for discovering the phase transition. We apply the approach to prototype models of nonequilibrium statistical mechanics, including the kinetically constrained models of structural glasses up to three dimensions. The approach uncovers the active-inactive phase transition of spin flips, the dynamical phase diagram, as well as new scaling relations. The result highlights the potential of machine learning dynamical phase transitions in nonequilibrium systems.