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Giant cell tumor of bone (GCTB) is a benign neoplasm, which can sometimes be a diagnostic challenge, especially in small biopsies, due to its histologic benign and malignant mimics. We evaluated the role of H3.3 G34W immunohistochemistry (IHC) antibody in diagnosing GCTB and its role in differentiating it from its close histologic mimics. A total of 120 cases (80 cases of GCTB and 40 cases of histologic mimics) were retrieved and subjected to IHC. Of 80 cases of GCTB, 72 cases showed a positive nuclear immunoexpression, while all 40 cases of histologic mimics of GCTB showed a negative staining for H3.3 G34W IHC. Sensitivity and specificity of this mutation-specific antibody for diagnosis of GCTB was 90% and 100%, respectively, while, the positive predictive value and the negative predictive value were 100% and 83.3%, respectively. A positive expression of H3.3 G34W was seen in all 5 cases of GCTB, postdenosumab therapy, as well as, in all 3 cases of malignant giant cell tumor. The presented study showed that H3.3 G34W mutation-specific IHC is a reliable and specific marker for GCTB and can help distinguish it from the histologic mimics due to distinct therapeutic implications.Evidence from past research has shown that DNA methylation plays a key role in the pathogenesis of periodontitis, regulating gene expression levels and thereby affecting the occurrence of various diseases. Three sample sets of methylation data and gene expression data were downloaded from Gene Expression Omnibus (GEO) database. A diagnostic classifier is established based on gene expression data and CpG methylation data. Abnormal expression of immune-related pathways and methyltransferase-related genes in patients with periodontitis was detected. A total of 8,029 differentially expressed CpG (DMP) was annotated to the promoter region of 4,940 genes, of which 295 immune genes were significantly enriched. The CpG sites of 23 differentially co-expressed immune gene promoter regions were identified, and 13 CpG were generally hypermethylated in healthy group samples, while some were methylated in most patients. Five CpGs were screened as robust periodontitis biomarkers. The accuracy in the training data set, the two external verification data sets, and in the transcriptome was 95.5%, 80% and 78.3%, and 82.6%, respectively. This study provided new features for the diagnosis of periodontitis, and contributed to the personalized treatment of periodontitis.The association between endogenous estrogen exposure and Alzheimer's disease (AD) remains inconclusive in previous observational studies, and few Mendelian randomization (MR) studies have focused on their causality thus far. We performed a bidirectional MR study to clarify the causality and causal direction of age at menarche and age at menopause, which are indicators of endogenous estrogen exposure, on AD risk. We obtained all genetic datasets for the MR analyses using publicly available summary statistics based on individuals of European ancestry from the IEU GWAS database. The MR analyses indicated no significant causal relationship between the genetically determined age at menarche (outlier-adjusted inverse variance weighted odds ratio [IVWOR] = 0.926; 95% confidence interval [CI], 0.803-1.066) or age at menopause (outlier-adjusted IVWOR = 0.981; 95% CI, 0.941-1.022) and AD risk. Similarly, AD did not show any causal association with age at menarche or age at menopause. The sensitivity analyses yielded similar results. In contrast, an inverse association was detected between age at menarche and body mass index (BMI, outlier-adjusted IVW β = -0.043; 95% CI, -0.077 to -0.009). Our bidirectional MR study provides no evidence for a causal relationship between the genetically determined age at menarche or age at menopause and AD susceptibility, or vice versa. However, earlier menarche might be associated with higher adult BMI.

The first wave of the COVID-19 pandemic in early 2020 increased mental health problems globally. However, little is known about mental health problems during a low-incidence period of the pandemic without strict public health measures.

We aim to investigate whether COVID-19-related risk factors for mental health problems persist beyond lockdown measures. We targeted a vulnerable population that is at risk of developing low mental health and assessed their daily dynamics of mood and emotion regulation after a strict lockdown.

During a postlockdown period in Germany (between August 8, 2020, and November 1, 2020), we conducted an ecological momentary assessment with 131 participants who experienced at least mild COVID-19-related distress and loneliness. To estimate negative mood inertia, we built a lag-1 three-level autoregressive model.

We found that information exposure and active daily COVID-19 cases did not have an impact on negative mood amid a postlockdown period. However, there was a day-to-day carryover effect of negative mood. In addition, worrying about COVID-19, feeling restricted by COVID-19, and feeling lonely increased negative mood.

The mental health of a vulnerable population is still challenged by COVID-19-related stressors after the lifting of a strict lockdown. This study highlights the need to protect mental health during postpandemic periods.

The mental health of a vulnerable population is still challenged by COVID-19-related stressors after the lifting of a strict lockdown. This study highlights the need to protect mental health during postpandemic periods.

In recent years, robots have been considered a new tech industry that can be used to solve the shortage in human resources in the field of health care. Also, animal-assisted therapy has been used to provide assistance, companionship, and interaction among the elderly and has been shown to have a positive impact on their emotional and psychological well-being. Both pets and robots can provide dynamic communication and positive interaction patterns. However, preferences for middle-aged and older adults in this regard are not clear.

This study explored the degree of acceptance of robots and pets as partners in later life and to determine the needs and preferences of elderly individuals related to companion robots.

A total of 273 middle-aged and older adults aged ≥45 years and living in the community were invited to answer a structured questionnaire after watching a companion robot video. Sociodemographic data, physical health status and activities, experience with technology, eHealth literacy, and acceptantions to address loneliness in later life in fast-aging societies.This article aims at analyzing and designing the multivalued high-capacity-associative memories based on recurrent neural networks with both asynchronous and distributed delays. In order to increase storage capacities, multivalued activation functions are introduced into associative memories. The stored patterns are retrieved by external input vectors instead of initial conditions, which can guarantee accurate associative memories by avoiding spurious equilibrium points. Some sufficient conditions are proposed to ensure the existence, uniqueness, and global exponential stability of the equilibrium point of neural networks with mixed delays. For neural networks with n neurons, m-dimensional input vectors, and 2k-valued activation functions, the autoassociative memories have (2k)n storage capacities and heteroassociative memories have min storage capacities. That is, the storage capacities of designed associative memories in this article are obviously higher than the 2n and min storage capacities of the conventional ones. Three examples are given to support the theoretical results.Under the assumption of rational economics, the opinions of decision makers should exhibit some transitivity properties. It is an important issue on how to measure the transitivity properties of the provided preference relations over a set of alternatives. In this study, we report the methods for measuring weak consistency (w-consistency) and weak transitivity (w-transitivity) of pairwise comparison matrices (PCMs) originating from the analytic hierarchy process (AHP). First, some interesting properties of PCMs with w-consistency and w-transitivity are studied. Second, novel methods are proposed to construct the quantification indices of w-consistency and w-transitivity of PCMs, respectively. Some comparisons with the existing methods are offered to illustrate the novelty of the proposed ones. Third, an optimization model is put forward to modify a PCM without any transitivity property to a new one with w-consistency and w-transitivity, respectively. The particle swarm optimization (PSO) algorithm is adopted to solve the nonlinear optimization problems. A novel decision-making model is established by considering the w-transitivity as the minimum requirement. Some numerical examples are carried out to illustrate the developed methods and models. It is observed that the proposed indices can be computed efficiently and reflect the inherent relations of the entries in a PCM with w-consistency and w-transitivity, respectively.This article proposes a novel fixed-time converging forward-backward-forward neurodynamic network (FXFNN) to deal with mixed variational inequalities (MVIs). A distinctive feature of the FXFNN is its fast and fixed-time convergence, in contrast to conventional forward-backward-forward neurodynamic network and projected neurodynamic network. It is shown that the solution of the proposed FXFNN exists uniquely and converges to the unique solution of the corresponding MVIs in fixed time under some mild conditions. It is also shown that the fixed-time convergence result obtained for the FXFNN is independent of initial conditions, unlike most of the existing asymptotical and exponential convergence results. Furthermore, the proposed FXFNN is applied in solving sparse recovery problems, variational inequalities, nonlinear complementarity problems, and min-max problems. Finally, numerical and experimental examples are presented to validate the effectiveness of the proposed neurodynamic network.We consider mechanical systems with uncertainty. The uncertainty may be time varying. The bound of the uncertainty is described by its fuzzy characteristics. To design a feasible control, we start with a robust phase, which renders a control scheme that guarantees the system performance regardless of the actual value of the uncertainty. This robust phase is then followed by an optimal phase. There are design parameters in the control, which can be fine-tuned. We proposed multiple performance objectives. The goal of the choice of the control design parameters is to minimize the performance objectives. However, since these objectives are nonconciliating (meaning one's minimum is not the other one's minimum), we invoke the Stackelberg strategy for the optimal parameters. The game strategy mimics two players one is the leader and one is the follower. Through the interplay between the two players, we show how to select the design parameters. selleck chemicals llc The design procedure in both robust and optimal phases is demonstrated by a coupled inverted pendulum system.Different cancer patients may respond differently to cancer treatment due to the heterogeneity of cancer. It is an urgent task to develop an efficient computational method to identify drug responses in different cell lines, which guides us to design personalized therapy for an individual patient. Hence, we propose an end-to-end algorithm, namely MOFGCN, to predict drug response in cell lines based on Multi-Omics Fusion and Graph Convolution Network. MOFGCN first fuses multiple omics data to calculate the cell line similarity and then constructs a heterogeneous network by combining the cell line similarity, drug similarity, and the known cell line-drug associations. Secondly, it learns the latent features for cancer cell lines and drugs by performing graph convolution operations on the heterogeneous network. Finally, MOFGCN applies the linear correlation coefficient to reconstruct the cancer cell line-drug correlation matrix to predict drug sensitivity. To our knowledge, this is the first attempt to combine graph convolutional neural network and linear correlation coefficient for this significant task.