Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

Syntheses of LSD1/HDAC Inhibitors with Demonstrated Efficacy against Colorectal Cancer: In Vitro and In Vivo Studies Including Patient-Derived Organoids.

Precedential evidence ascertaining the overexpression of LSD1 and HDACs in colorectal cancer spurred us to design a series of dual LSD1-HDAC inhibitors. Capitalizing on the modular nature of the three-component HDAC inhibitory model, tranylcypromine as a surface recognition motif was appended to zinc-binding motifs via diverse linkers. A compendium of hydroxamic acids was generated and evaluated for in vitro cytotoxicity against HCT-116 cells (human colorectal cancer cell lines). The most potent cell growth inhibitor 2 (GI50 = 0.495 µMm HCT-116 cells) shows promising anticancer effects by reducing colony formation and inducing cell cycle arrest in HCT-116 cells. It exhibits preferential inhibition of HDAC6, along with potent inhibition of LSD1 compared to standard inhibitors. Moreover, Compound 2 upregulates acetyl-tubulin, acetyl-histone H3, and H3K4me2, indicative of LSD1 and HDAC inhibition. In vivo, it demonstrates significant antitumor activity against colorectal cancer, better than irinotecan, and effectively inhibits growth in patient-derived CRC organoids.

Targeted metabolomics assisted rapid screening and characterization of aristolochic acids and their DNA adducts in aristolochia plants by ultra-high performance liquid chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry.

Aristolochic acids are one of the major compounds in aristolochia plants, which are nephrotoxic and carcinogenic. A method was established for the detection and identification of aristolochic acids and their DNA adducts in four different herbs using ultra-high performance liquid chromatography-ion mobility quadrupole time-of flight mass spectrometry. Solid phase extraction conditions were optimized to improve the sensitivity of the experiment by using 40 mg of C18 as adsorbent and 100 µL ethanol as elution solvent. At a collision energy of 10-40 eV, these compounds and cleavage patterns were precisely identified and analyzed by secondary fragmentation and collision cross section values. The obtained mass spectrometry data were then analyzed by targeted metabolomics, including principal component analysis, partial least squares-discriminant analysis and hierarchical clustering analysis, and importing the samples in the established model, the confidence values can reach 0.61 and 0.76. All in all, this method can provide a useful tool for the detection of aristolochic acids and deoxyribonucleic acid adducts. In conclusion, this method was successfully used for the detection and identification of aristolochic acids and their DNA adducts.

Application of Phosphorylated Tau for Predicting Outcomes Among Sudden Cardiac Arrest Survivors.

BACKGROUND: Phosphorylated Tau (p-Tau), an early biomarker of neuronal damage, has emerged as a promising candidate for predicting neurological outcomes in cardiac arrest (CA) survivors. Despite its potential, the correlation of p-Tau with other clinical indicators remains underexplored. This study assesses the predictive capability of p-Tau and its effectiveness when used in conjunction with other predictors. METHODS: In this single-center retrospective study, 230 CA survivors had plasma and brain computed tomography scans collected within 24 h after the return of spontaneous circulation (ROSC) from January 2016 to June 2023. The patients with prearrest Cerebral Performance Category scores ≥ 3 were excluded (n = 33). The neurological outcomes at discharge with Cerebral Performance Category scores 1-2 indicated favorable outcomes. Plasma p-Tau levels were measured using an enzyme-linked immunosorbent assay, diastolic blood pressure (DBP) was recorded after ROSC, and the gray-to-white matter ratio (GWR) was calculated from brain computed tomography scans within 24 h after ROSC. RESULTS: Of 197 patients enrolled in the study, 54 (27.4%) had favorable outcomes. Regression analysis showed that higher p-Tau levels correlated with unfavorable neurological outcomes. The levels of p-Tau were significantly correlated with DBP and GWR. For p-Tau to differentiate between neurological outcomes, an optimal cutoff of 456 pg/mL yielded an area under the receiver operating characteristic curve of 0.71. Combining p-Tau, GWR, and DBP improved predictive accuracy (area under the receiver operating characteristic curve = 0.80 vs. 0.71, p = 0.008). CONCLUSIONS: Plasma p-Tau levels measured within 24 h following ROSC, particularly when combined with GWR and DBP, may serve as a promising biomarker of neurological outcomes in CA survivors, with higher levels predicting unfavorable outcomes.

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results. METHODS: A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. RESULTS: The K-nearest neighbor model (R2=0.779) and the ridge regression model (R2=0.729) outperformed stepwise regression (R2=0.617), while the decision tree model (R2=0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. CONCLUSIONS: Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Nontargeted metabolomics and enzyme inhibitory and antioxidant activities for chemical and biological characterization of jujube (Ziziphus jujuba) extracts.

The chemical and biologically active characterization of jujube samples (fruits, cores, and leaves) were carried out by the integrated nontargeted metabolomics and bioassay. Firstly, collision cross-section values of active compounds in jujubes were determined by ultrahigh-performance liquid chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry. Then, a multidimensional statistical analysis that contained principal component analysis, partial least squares-discriminant analysis and hierarchical clustering analysis was employed to effectively cluster different tissues and types of jujubes, making identification more scientific. Furthermore, angiotensin-converting enzyme (ACE) and 2, 2-diphenyl-1-picrylhydrazyl (DPPH) were used to evaluate the quality of jujubes from a double activity dimension. The analytical results obtained by using ACE and DPPH to evaluate the quality of jujube were different from multivariate statistics, providing a reference for the application of jujube. Therefore, integrating chemical and biological perspectives to evaluate the quality of jujube provided a more comprehensive evaluation and effective reference for clinical needs.

Identification of selective dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) inhibitors and their effects on tau and microtubule.

The overexpression of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A), commonly observed in neurodegenerative diseases like Alzheimer's disease (AD) and Down syndrome (DS), can induce the formation of neurofibrillary tangles (NFTs) and amyloid plaques. Hence, designing a selective DYRK1A inhibitor would result in a promising small molecule for treating neurodegenerative diseases. Developing selective inhibitors for DYRK1A has been a difficult challenge due to the highly preserved ATP-binding site of protein kinases. In this study, we employed a structure-based virtual screening (SBVS) campaign targeting DYRK1A from a database containing 1.6 million compounds. Enzymatic assays were utilized to verify inhibitory properties, confirming that Y020-3945 and Y020-3957 showed inhibitory activity towards DYRK1A. In particular, the compounds exhibited high selectivity for DYRK1A over a panel of 120 kinases, reduced the phosphorylation of tau, and reversed the tubulin polymerization for microtubule stability. Additionally, treatment with the compounds significantly reduced the secretion of inflammatory cytokines IL-6 and TNF-α activated by DYRK1A-assisted NFTs and Aß oligomers. These identified inhibitors possess promising therapeutic potential for conditions associated with DYRK1A in neurodegenerative diseases. The results showed that Y020-3945 and Y020-3957 demonstrated structural novelty compared to known DYRK1A inhibitors, making them a valuable addition to developing potential treatments for neurodegenerative diseases.

Synthesis and evaluation of potent (iso)ellipticine-based inhibitors of MYLK4 accessed via expeditious synthesis from isoquinolin-5-ol.

The K2S2O8-mediated generation of p-iminoquinone contributed to the regioselective substitution of isoquinolin-5,8-dione. This hydroxyl group-guided substitution was also applied to selected heterocycles and addressed the regioselectivity issue of quinones. This study has provided an expeditious pathway from isoquinolin-5-ol (5) to ellipticine (1) and isoellipticine (2), which benefits the comprehensive comparison of their activity. Compounds 1 and 2 displayed marked MYLK4 inhibitory activity with IC50 values of 7.1 and 6.1 nM, respectively. In the cellular activity of AML cells (MV-4-11 and MOLM-13), compound 1 showed better AML activity than compound 2.

Intestinal epithelial dopamine receptor signaling drives sex-specific disease exacerbation in a mouse model of multiple sclerosis.

Although the gut microbiota can influence central nervous system (CNS) autoimmune diseases, the contribution of the intestinal epithelium to CNS autoimmunity is less clear. Here, we showed that intestinal epithelial dopamine D2 receptors (IEC DRD2) promoted sex-specific disease progression in an animal model of multiple sclerosis. Female mice lacking Drd2 selectively in intestinal epithelial cells showed a blunted inflammatory response in the CNS and reduced disease progression. In contrast, overexpression or activation of IEC DRD2 by phenylethylamine administration exacerbated disease severity. This was accompanied by altered lysozyme expression and gut microbiota composition, including reduced abundance of Lactobacillus species. Furthermore, treatment with N2-acetyl-L-lysine, a metabolite derived from Lactobacillus, suppressed microglial activation and neurodegeneration. Taken together, our study indicates that IEC DRD2 hyperactivity impacts gut microbial abundances and increases susceptibility to CNS autoimmune diseases in a female-biased manner, opening up future avenues for sex-specific interventions of CNS autoimmune diseases.

Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer.

Purpose: To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). Methods: This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. Results: The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580-0.757] in the training cohort and 0.62 (95% CI, 0.467-0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727-0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662-0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). Conclusions: The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.

[Superstitious Beliefs Among Healthcare Providers: A Concept Analysis].

Evidence-based practice is a problem-solving approach to healthcare delivery that reflects the best current scientific evidence. When healthcare providers face unexpected changes in a patient's condition or uncontrollable situations during care delivery, they may have less confidence or feel fearful / anxious about the care process and result. As people, healthcare providers may hold beliefs regarding the effect of external, supernatural forces on events, which may lead to superstitious beliefs and behaviors. Also, superstitious beliefs may be adopted by healthcare providers as a mechanism to cope with stress, anxiety, and uncertainty in situations where standard medical practices offer no ready solution. Although superstitious beliefs may help ease anxiety and feelings of failure in healthcare providers, this issue and the effects of these beliefs on medical staff behavior have not been adequately studied. The concept analysis strategy of Walker and Avant (2019) was applied in this study to define this concept and to examine (1) healthcare providers' loss of environment control and domination of irrationality in decision making, (2) the lack of objective evidence to explain cause-and-effect relationships in health-related situations, and (3) how unverified true or false claims become a compliance criterion among healthcare providers. Typical, borderline, and contrary cases were used to explain the concept of superstition in medical staff. The antecedents and possible consequences of healthcare providers holding superstitious beliefs were identified and the empirically addressed measurement tools were evaluated. This analysis may be used to improve the understanding of healthcare workers regarding superstitious beliefs. The results are expected to benefit clinical practice, facilitate further research, and enhance healthcare quality.

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis.

BACKGROUND: Development of a radiomics model for predicting lymph node metastasis status in rectal cancer patients based on 3-dimensional endoanal rectal ultrasound images. METHODS: This study retrospectively included 79 patients (41 with lymph node metastasis positive and 38 with lymph node metastasis negative) diagnosed with rectal cancer in our hospital from January 2018 to February 2022. The tumor's region of interest is first delineated by radiologists, from which radiomics features are extracted. Radiomics features were then selected by independent samples t-test, correlation coefficient analysis between features, and least absolute shrinkage and regression with selection operator. Finally, a multilayer neural network model is developed using the selected radiomics features, and nested cross-validation is performed on it. These models were validated by assessing their diagnostic performance and comparing the areas under the curve and recall rate curve in the test set. RESULTS: The areas under the curve of radiologist was 0.662 and the F1 score was 0.632. Thirty-four radiomics features were significantly associated with lymph node metastasis (P < .05), and 10 features were finally selected for developing multilayer neural network models. The areas under the curve of the multilayer neural network models were 0.787, 0.761, 0.853, and the mean areas under the curve was 0.800. The F1 scores of the multilayer neural network models were 0.738, 0.740, and 0.818, and the mean F1 score was 0.771. CONCLUSIONS: Radiomics models based on 3-dimensional endoanal rectal ultrasound can be used to identify lymph node metastasis status in rectal cancer patient with good diagnostic performance.

Development of Furanopyrimidine-Based Orally Active Third-Generation EGFR Inhibitors for the Treatment of Non-Small Cell Lung Cancer.

The development of orally bioavailable, furanopyrimidine-based double-mutant (L858R/T790M) EGFR inhibitors is described. First, selectivity for mutant EGFR was accomplished by replacing the (S)-2-phenylglycinol moiety of 12 with either an ethanol or an alkyl substituent. Then, the cellular potency and physicochemical properties were optimized through insights from molecular modeling studies by implanting various solubilizing groups in phenyl rings A and B. Optimized lead 52 shows 8-fold selective inhibition of H1975 (EGFRL858R/T790M overexpressing) cancer cells over A431 (EGFRWT overexpressing) cancer cells; western blot analysis further confirmed EGFR mutant-selective target modulation inside the cancer cells by 52. Notably, 52 displayed in vivo antitumor effects in two different mouse xenograft models (BaF3 transfected with mutant EGFR and H1975 tumors) with TGI = 74.9 and 97.5% after oral administration (F = 27%), respectively. With an extraordinary kinome selectivity (S(10) score of 0.017), 52 undergoes detailed preclinical development.

Combined cardiomegaly and aortic arch calcification predict mortality in hemodialysis patients.

INTRODUCTION: This study aimed to investigate the relationship between cardiomegaly and aortic arch calcification (AAC) and overall/cardiovascular mortality in hemodialysis patients. METHODS: We conducted a retrospective cohort study and enrolled patients who underwent initial hemodialysis. Cardiomegaly and AAC were determined by chest radiography and classified into four groups according to cross-classification of cardiothoracic ratio (CTR) of 0.5 and lower/higher grade AAC (LGAAC/HGAAC). The relationship between these groups and mortality was then analyzed by Cox proportional hazards model. RESULTS: In multivariate Cox regression analysis, those in CTR ≤ 0.5 and HGAAC [hazard ratio (95% confidence interval): 2.07 (1.14-3.77)], CTR > 0.5 & LGAAC [3.60 (2.07-6.25)] and CTR > 0.5 & HGAAC [3.42 (2.03-5.77)] were significantly associated with overall mortality; while those in CTR > 0.5 & LGAAC [2.81 (1.28-6.19)] and CTR > 0.5 & HGAAC [2.32 (1.09-4.95)] were significantly related to cardiovascular mortality. CONCLUSION: Combined cardiomegaly and AAC predicted overall and cardiovascular mortality in hemodialysis patients.

Modeling Heme Peroxidase: Heme Saddling Facilitates Reactions with Hyperperoxides To Form High-Valent FeIV -Oxo Species.

Saddle-shaped hemes have been discovered in the structures of most peroxidases. How such a macrocycle deformation affects the reaction of FeIII hemes with hydrogen peroxide (H2 O2 ) to form high-valent Fe-oxo species remains uncertain. Through examination of the ESI-MS spectra, absorption changes and 1 H NMR chemical shifts, we investigated the reactions of two FeIII porphyrins with different degrees of saddling deformation, namely FeIII (OETPP)ClO4 (1OE ) and FeIII (OMTPP)ClO4 (1OM ), with tert-butyl hydroperoxide (tBuOOH) in CH2 Cl2 at -40 °C, which quickly resulted in O-O bond homolysis from a highly unstable FeIII -alkylperoxo intermediate, FeIII -O(H)OR (2) into FeIV -oxo porphyrins (3). Insight into the reaction mechanism was obtained from [tBuOOH]-dependent kinetics. At -40 °C, the reaction of 1OE with tBuOOH exhibited an equilibrium constant (Ka =362.3 M-1 ) and rate constant (k=1.87×10-2  sM->1 ) for the homolytic cleavage of the 2 O-O bond that were 2.1 and 1.4 times higher, respectively, than those exhibited by 1OM (Ka =171.8 M-1 and k=1.36×10-2 s-1 ). DFT calculations indicated that an FeIII porphyrin with greater saddling deformation can achieve a higher HOMO ([Fe(d z 2 ,d x 2 - y 2 )-porphyrin(a2u )]) to strengthen the orbital interaction with the LUMO (O-O bond σ*) to facilitate O-O cleavage.

O-methylated flavonol as a multi-kinase inhibitor of leukemogenic kinases exhibits a potential treatment for acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous disease with poor overall survival characterized by various genetic changes. The continuous activation of oncogenic pathways leads to the development of drug resistance and limits current therapeutic efficacy. Therefore, a multi-targeting inhibitor may overcome drug resistance observed in AML treatment. Recently, groups of flavonoids, such as flavones and flavonols, have been shown to inhibit a variety of kinase activities, which provides potential opportunities for further anticancer applications. PURPOSE: In this study, we evaluated the anticancer effects of flavonoid compounds collected from our in-house library and investigated their potential anticancer mechanisms by targeting multiple kinases for inhibition in AML cells. METHODS: The cytotoxic effect of the compounds was detected by cell viability assays. The kinase inhibitory activity of the selected compound was detected by kinase-based and cell-based assays. The binding conformation and interactions were investigated by molecular docking analysis. Flow cytometry was used to evaluate the cell cycle distribution and cell apoptosis. The protein and gene expression were estimated by western blotting and qPCR, respectively. RESULTS: In this study, an O-methylated flavonol (compound 11) was found to possess remarkable cytotoxic activity against AML cells compared to treatment in other cancer cell lines. The compound was demonstrated to act against multiple kinases, which play critical roles in survival signaling in AML, including FLT3, MNK2, RSK, DYRK2 and JAK2 with IC50 values of 1 - 2 µM. Compared to our previous flavonoid compounds, which only showed inhibitions against MNKs or FLT3, compound 11 exhibited multiple kinase inhibitory abilities. Moreover, compound 11 showed effectiveness in inhibiting internal tandem duplications of FLT3 (FLT3-ITDs), which accounts for 25% of AML cases. The interactions between compound 11 and targeted kinases were investigated by molecular docking analysis. Mechanically, compound 11 caused dose-dependent accumulation of leukemic cells at the G0/G1 phase and followed by the cells undergoing apoptosis. CONCLUSION: O-methylated flavonol, compound 11, can target multiple kinases, which may provide potential opportunities for the development of novel therapeutics for drug-resistant AMLs. This work provides a good starting point for further compound optimization.

Effects of Onabotulinum Toxin A on Gait in Parkinson's Disease Patients with Foot Dystonia.

We investigated the effects of botulinum toxin on gait in Parkinson's disease (PD) patients with foot dystonia. Six patients underwent onabotulinum toxin A injection and were assessed by Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS), visual analog scale (VAS) of pain, Timed Up and Go (TUG), Berg Balance Test (BBT), and 3D gait analysis at baseline, 1 month, and 3 months. BFMDRS (p = 0.002), VAS (p = 0.024), TUG (p = 0.028), and BBT (p = 0.034) were improved. Foot pressures at Toe 1 (p = 0.028) and Midfoot (p = 0.018) were reduced, indicating botulinum toxin's effects in alleviating the dystonia severity and pain and improving foot pressures during walking in PD.