Design and fabrication of wearable electronic textiles using twisted fiber-based threads.

Mono-dimensional fiber-based electronics can effectively address the growing demand for improved wearable electronic devices because of their exceptional flexibility and stretchability. For practical applications, functional fiber electronic devices need to be integrated into more powerful and versatile systems to execute complex tasks that cannot be completed by single-fiber devices. Existing techniques, such as printing and sintering, reduce the flexibility and cause low connection strength of fiber-based electronic devices because of the high curvature of the fiber. Here, we outline a twisting fabrication process for fiber electrodes, which can be woven into functional threads and integrated within textiles. The design of the twisted thread structure for fiber devices ensures stable interfacing and good flexibility, while the textile structure features easily accessible, interlaced points for efficient circuit connections. Electronic textiles can be customized to act as displays, health monitors and power sources. We detail three main fabrication sections, including the fabrication of the fiber electrodes, their twisting into electronic threads and their assembly into functional textile-based devices. The procedures require ~10 d and are easily reproducible by researchers with expertise in fabricating energy and electronic devices.

The management of uterine tumor resembling an ovarian sex cord tumor (UTROSCT): case series and literature review.

AIMS: To present a case series of 11 rare uterine tumors resembling ovarian sex cord tumors (UTROSCTs), and review the literature on this topic to offer up-to-date treatment management for UTROSCTs. METHOD: Eight cases from Fujian Cancer Hospital between January 2017 and May 2023 and three patients from Fujian Union Hospital between October 2012 and October 2020 were retrospectively reviewed. All cases were pathologically confirmed as UTROSCTs by two senior and experienced pathologists. Clinical behaviors, medical data, histopathological features, therapy approaches, and survival outcomes were discussed. RESULTS: The median age at initial diagnosis was 53 years (29-70 years). 3 (27.3%) patients were under 40. Seven cases presented with abnormal vaginal bleeding, one with menstrual disorder, one with abnormal vaginal secretion, and two patients were accidentally found by physical examination without any symptoms. Three patients were initially misdiagnosed with endometrial cancer by MRI. Curettage was performed in all cases. Nine of them were well diagnosed by routine curettage, except for two samples, which were identified after surgery. Immunohistochemical biomarkers, such as CD99, Desmin, WT-1, CK, Vimentin, SMA, α-Inhibin, Ki67, CD56, ER, PR, and CR, tend to be positive in UTRO SCs patients. Six patients underwent hysterectomy with bilateral salpingo-oophorectomy. Two cases received a radical hysterectomy with bilateral salpingo-oophorectomy, retroperitoneal lymph node dissection, and omentum dissection. Three UTROSCTs were under observation after mass resection. The median PFS was 24 months (range 1-125 months). CONCLUSION: UTROSCT is a rare mesenchymal tumor with low malignant potential. Treatment modalities should be carefully considered to balance the therapy outcomes and patient needs. Surgery conservative management might be suitable for young women with fertility desires.

An artificial nickel chlorinase based on the biotin-streptavidin technology.

Herein, we report on an artificial nickel chlorinase (ANCase) resulting from anchoring a biotinylated nickel-based cofactor within streptavidin (Sav). The resulting ANCase was efficient for the chlorination of diverse C(sp3)-H bonds. Guided by the X-ray analysis of the ANCase, the activity of the artificial chlorinase could be significantly improved. This approach opens interesting perspectives for late-stage functionalization of organic intermediates as it complements biocatalytic chlorination strategies.

Opto-thermal deformation fitting method based on a neural network and a transfer learning.

The thermal deformation fitting result of an optical surface is an important factor that affects the reliability of optical-mechanical-thermal integrated analysis. The traditional numerical methods are challenging to balance fitting accuracy and efficiency, especially the insufficient ability to deal with high-order Zernike polynomials. In this Letter, we innovatively proposed an opto-thermal deformation fitting method based on a neural network and a transfer learning to overcome shortcomings of numerical methods. The one-dimensional convolutional neural network (1D-CNN) model, which can represent deformation of the optical surface, is trained with Zernike polynomials as the input and the optical surface sag change as the output, and the corresponding Zernike coefficients are predicted by the identity matrix. Meanwhile, the trained 1D-CNN is further combined with the transfer learning to efficiently fit all thermal deformations of the same optical surface at different temperature conditions and avoids repeated training of the network. We performed thermal analysis on the main mirror of an aerial camera to verify the proposed method. The regression analysis of 1D-CNN training results showed that the determination coefficient is greater than 99.9%. The distributions of Zernike coefficients predicted by 1D-CNN and transfer learning are consistent. We conducted an error analysis on the fitting results, and the average values of the peak-valley, root mean square, and mean relative errors of the proposed method are 51.56%, 60.51, and 45.14% of the least square method, respectively. The results indicate that the proposed method significantly improves the fitting accuracy and efficiency of thermal deformations, making the optical-mechanical-thermal integrated analysis more reliable.

Enhanced expression of RAGE/NADPH oxidase signaling pathway and increased level of oxidative stress in brains of rats with chronic fluorosis and the protective effects of blockers.

This investigation was designed to examine the potential involvement of RAGE/NADPH oxidase signaling in the damage to the brain caused by chronic fluorosis. Sprague-Dawley rats were divided randomly into 9 groups each containing 20 animals, Controls (C); rats receiving low (i.e., 10 ppm) (LF) or high does ( i.e., 50 ppm) (HF) of fluoride in their drinking water; and these same groups injected with FPS-ZM1, an inhibitor of RAGE, (CF, LFF and HFF, respectively) or administered EGb761, an active ingredient of Ginkgo biloba extract, intragastrically (CE, LFE, and HFE). Following 3 and 6 months of such treatment, the spatial learning and memory of the animals were assessed with the Morris water maze test; the levels of malondialdehyde (MDA), hydrogen peroxide (H2O2) and superoxide dismutase (SOD) assayed by biochemical methods; and the levels of proteins related to the RAGE/NADPH pathway determined by Western blot and of the corresponding mRNAs by qPCR. After 6 months, the spatial learning and memory of the LF and HF groups had declined; their brain contents of MDA and H2O2 increased and SOD activity decreased; and the levels of the RAGE, gp91, P47, phospho-P47phox and P22 proteins and corresponding mRNAs in their brains were all elevated. Interestingly, all of these pathological changes caused by fluorosis could be attenuated by both FPS-ZM1 and EGb761. These findings indicate that the brain damage induced by fluorosis may be caused, at least in part, by enhanced RAGE/NADPH oxidase signaling and that FPS-ZM1 or EGb761 might be of clinical value in connection with the treatment of this condition.

A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions.

Design of experiments (DoE) plays an important role in optimizing the catalytic performance of chemical reactions. The most commonly used DoE relies on the response surface methodology (RSM) to model the variable space of experimental conditions with the fewest number of experiments. However, the RSM leads to an exponential increase in the number of required experiments as the number of variables increases. Herein we describe a Bayesian optimization algorithm (BOA) to optimize the continuous parameters (e.g., temperature, reaction time, reactant and enzyme concentrations, etc.) of enzyme-catalyzed reactions with the aim of maximizing performance. Compared to existing Bayesian optimization methods, we propose an improved algorithm that leads to better results under limited resources and time for experiments. To validate the versatility of the BOA, we benchmarked its performance with biocatalytic C-C bond formation and amination for the optimization of the turnover number. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs previous Bayesian optimization algorithms were obtained. Importantly, this strategy enabled simultaneous optimization of both the enzyme's activity and selectivity for cross-benzoin condensation.

Simultaneous quantitative chiral analysis of four isomers by ultraviolet photodissociation mass spectrometry and artificial neural network.

Although mass spectrometry (MS) has its unique advantages in speed, specificity and sensitivity, its application in quantitative chiral analysis aimed to determine the proportions of multiple chiral isomers is still a challenge. Herein, we present an artificial neural network (ANN) based approach for quantitatively analyzing multiple chiral isomers from their ultraviolet photodissociation mass spectra. Tripeptide of GYG and iodo-L-tyrosine have been applied as chiral references to fulfill the relative quantitative analysis of four chiral isomers of two dipeptides of L/D His L/D Ala and L/D Asp L/D Phe, respectively. The results show that the network can be well-trained with limited sets, and have a good performance in testing sets. This study shows the potential of the new method in rapid quantitative chiral analysis aimed at practical applications, with much room for improvement in the near future, including selecting better chiral references and improving machine learning methods.

A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining.

BACKGROUND: Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications. METHODS: We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes. RESULTS: We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC1-year = 0.714, ROC3-year = 0.750, ROC5-year = 0.767). Furthermore, GSEA unveiled that the cell cycle, certain malignant tumors, and cell metabolism were the main biological functions enriched in this identified model. We found the five metabolism-related genes signature was associated with the immune infiltrating cells and immune functions. Most importantly, it was linked with specific immune checkpoints (PD-1, CTLA4, and CD40) that could predict immunotherapy's clinical response. CONCLUSION: The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.

Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm.

The Aquila Optimizer (AO) is a new bio-inspired meta-heuristic algorithm inspired by Aquila's hunting behavior. Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm (NCAAO) is proposed to address the problem that although the Aquila Optimizer (AO) has a strong global exploration capability, it has an insufficient local exploitation capability and a slow convergence rate. First, to improve the diversity of populations in the algorithm and the uniformity of distribution in the search space, DLCS chaotic mapping is used to generate the initial populations so that the algorithm is in a better exploration state. Then, to improve the search accuracy of the algorithm, an adaptive adjustment strategy of de-searching preferences is proposed. The exploration and development phases of the NCAAO algorithm are effectively balanced by changing the search threshold and introducing the position weight parameter to adaptively adjust the search process. Finally, the idea of small habitats is effectively used to promote the exchange of information between groups and accelerate the rapid convergence of groups to the optimal solution. To verify the optimization performance of the NCAAO algorithm, the improved algorithm was tested on 15 standard benchmark functions, the Wilcoxon rank sum test, and engineering optimization problems to test the optimization-seeking ability of the improved algorithm. The experimental results show that the NCAAO algorithm has better search performance and faster convergence speed compared with other intelligent algorithms.

Double-Barreled Micropipette Enables Neuron-Compatible In Vivo Analysis.

The sensors based on ion transport in a confined nano-/microchannel (i.e., iontronic sensors) have brought new opportunities for in vivo neurochemical assay, especially for electroinactive molecules. However, the interference on spontaneous neuronal activity induced by the electric field around the sensors has not been addressed. Here, the electric field distribution with a double-barreled micropipette was shrunk and quantified by finite element simulation, which can explain and minimize the influence on spontaneous neuronal activity. The parameters affecting the electric field distribution, including the pore size, applied voltage, and angle degree, were studied to balance the sensitivity and interference on spontaneous neuronal activity. The double-barreled micropipette, as a pH sensor with high selectivity and sensitivity, has been successfully applied to real-time pH sensing in rat brain. This study offers a new way for in vivo monitoring neurochemical dynamics with neuron-compatibility.

Expression of expanded GGC repeats within NOTCH2NLC causes behavioral deficits and neurodegeneration in a mouse model of neuronal intranuclear inclusion disease.

GGC repeat expansions within NOTCH2NLC have been identified as the genetic cause of neuronal intranuclear inclusion disease (NIID). To understand the molecular pathogenesis of NIID, here, we established both a transgenic mouse model and a human neural progenitor cells (hNPCs) model. Expression of the NOTCH2NLC with expanded GGC repeats produced widespread intranuclear and perinuclear polyglycine (polyG), polyalanine (polyA), and polyarginine (polyR) inclusions, leading to behavioral deficits and severe neurodegeneration, which faithfully mimicked the clinical and pathological features associated with NIID. Furthermore, conserved alternative splicing events were identified between the NIID mouse and hNPC models, among which was the enrichment of the binding motifs of hnRNPM, an RNA binding protein known as alternative splicing regulator. Expanded NOTCH2NLC-polyG and NOTCH2NLC-polyA could interact with and sequester hnRNPM, while overexpression of hnRNPM could ameliorate the cellular toxicity. These results together suggested that dysfunction of hnRNPM could play an important role in the molecular pathogenesis of NIID.

Genetic Analysis of Six Transmembrane Protein Family Genes in Parkinson's Disease in a Large Chinese Cohort.

Objectives: Parkinson's disease (PD) is a neurodegenerative disorder with the manifestation of motor symptoms and non-motor symptoms. Previous studies have indicated the role of several transmembrane (TMEM) protein family genes in PD pathogenesis. Materials and Methods: In order to better investigate the genetic role of PD-related TMEM protein family genes in PD, including TMEM230, TMEM59, TMEM108, TMEM163, TMEM175, and TMEM229B, 1,917 sporadic early onset PD (sEOPD) or familial PD (FPD) patients and 1,652 healthy controls were analyzed by whole-exome sequencing (WES) while 1,962 sporadic late-onset PD (sLOPD) and 1,279 healthy controls were analyzed by whole-genome sequencing (WGS). Rare and common variants for each gene were included in the analysis. Results: One hundred rare damaging or loss of function variants of six genes were found at the threshold of MAF < 0.1%. Three rare Dmis variants of TMEM230 were specifically identified in PD. Rare missense variants of TMEM59 were statistically significantly associated with PD in the WES cohort, indicating the role of TMEM59 in FPD and sEOPD. Rare missense variants of TMEM108 were suggestively associated with PD in the WGS cohort, indicating the potential role of TMEM108 in sLOPD. The rare variant of the other three genes and common variants of six genes were not significantly associated with PD. Conclusion: We performed a large case-control study to systematically investigate the role of several PD-related TMEM protein family genes in PD. We identified three PD-specific variants in TMEM230, the significant association of TMEM59 with FPD, and sEOPD and the suggestive association of TMEM108 with sLOPD.

Genetic Analysis of HSP40/DNAJ Family Genes in Parkinson's Disease: a Large Case-Control Study.

Molecular chaperones were reported to play an important role in PD pathogenesis. Recent studies revealed the association of several HSP40/DNAJ family genes with PD, but no genetic analysis of all the DNAJ family genes in PD has been conducted. To systematically analyze the genetic impact of all the DNAJ family genes in PD, we performed genetic analysis for these genes in a large case-control study. We analyzed the rare variants in 49 DNAJ family genes from 3879 PD patients and 2931 healthy controls by whole-exome sequencing and whole-genome sequencing. All rare missense variants and the subgroups of rare damaging missense (Dmis) and loss-of-function (LoF) variants were gathered to test the accumulated association of these variants in each gene with PD. In total, 1617 rare nonsynonymous variants of DNAJ family genes with minor allele frequency less than 1% were identified in our cohort. We identified 82 rare missense variants for DNAJC26 in sporadic early-onset PD (sEOPD) or familial PD (FPD), and 17 Dmis and one LoF variant were detected among them. Gene-based burden analysis showed that the rare Dmis variants alone or Dmis plus LoF variants together of DNAJC26 were significantly enriched in PD patients. We also found suggestive associations of DNAJB2 and DNAJC18 with PD in sEOPD or FPD and DNAJC2, DNAJC10, DNAJC22, DNAJC24, DNAJC27, DNAJC28, and DNAJC29 with PD in sporadic late-onset PD. In conclusion, rare missense variants of DNAJC26 were significantly enriched in FPD or sEOPD. Moreover, DNAJB2, DNAJC2, DNAJC10, DNAJC18, DNAJC22, DNAJC24, DNAJC27, DNAJC28, and DNAJC29 were suggestively associated with PD.

Shipborne variable-FOV, dual-wavelength, polarized ocean lidar: design and measurements in the Western Pacific.

For the requirement of high-precision vertical profile of the polarization and optical properties of natural seawater, a ship-borne variable-FOV, dual-wavelength, polarized ocean lidar system is designed to obtain the volume linear depolarization ratio (VDR), color ratio and optical parameter profiles of seawater. With the high signal-to-noise ratio, which benefits from the high power (355 nm with 120 mJ, 532 nm with 200 mJ) solid-state laser and a photon counting recorder with a sampling rate of 1 GHz, the attenuated backscattered signal of seawater in the western Pacific campaign reaches to the depth of 50 m, where a plankton layer presents. The receiver of lidar is capable of switching to wide and narrow field of view (FOV), respectively, to obtain the lidar attenuation coefficient Klidar, which is in good agreement with the beam attenuation coefficient of seawater c with a narrow FOV and diffuse attenuation coefficient Kd with a wide FOV. Besides, the Klidar, and the VDR, at two wavelengths of 355 nm and 532 nm are compared to explore the possibility of multi-wavelength of laser application in the ocean lidar. The VDR and the color ratio profiles have a desirable correlation with the in-situ measurement of chlorophyll a (Chla) and chromophoric dissolved organic matter (CDOM) profiles, respectively. With the combination of the Klidar, the VDR and the color ratio profiles, measured in different regions and time periods during the campaign, the multi-wavelength and polarization lidar shows its potential to explore various ocean compositions, such as the ocean particles size shape, the species and vertical migration characteristics of planktons, and the profile distribution of the ocean compositions.

High-Precision Measurement of Sea Surface Temperature with Integrated Infrared Thermometer.

The sea surface temperature (SST) is a crucial parameter system in climate monitoring. Satellite remote sensing is currently the most common approach for measuring long-term and large-area sea surface temperatures. The SST data measured by the satellite radiometer include the sea surface skin temperature (SSTskin) at a depth of approximately 10 µm. Satellite remote sensing measurement data must be compared and validated with on-site measured data. There are various solutions for on-site measuring instruments; the essential components are usually infrared radiation sensors with radiation output. This paper uses an ordinary integrated infrared thermometer without a radiation output function to remotely measure the sea surface temperature to achieve a high-precision measurement. The scheme of integrating infrared thermometers to measure the sea surface temperature is investigated in this paper. Based on Planck's formula, the bidirectional conversion relationship between temperature and radiation in a certain band is established. The experimental system introduced in this paper uses an integrated infrared thermometer to measure the small blackbody and the target in a cyclic measurement system. We combine it with the sea surface emissivity characteristics and eliminate the influence of sky background radiation on the sea surface to obtain the actual amount of radiation on the sea surface, from which we obtain the actual radiation amount on the sea surface. Accurate SST can be calculated from the actual amount of radiation at the sea surface. The temperature measurement accuracy can reach 0.1 K, allowing it to meet on-site temperature measurement requirements, as well as the comparison measurement requirements confirmed by satellite remote sensing on-site data. There are relatively few products available for sensors with a temperature measurement accuracy of 0.1 K on the market, and temperature measurement equipment with a temperature measurement accuracy of 0.1 K is relatively expensive. Cost is one of the important factors to consider when using in bulk, especially as global warming increases the need for ocean monitoring. The scheme proposed in this paper is beneficial to reduce the volume and weight of measuring instruments, reduce the cost, and promote the large-scale combined application of sea surface temperature change monitoring.

Differentiation of disaccharide isomers via a combination of IR and UV photodissociation mass spectrometry.

RATIONALE: The challenge of glycan identification due to their structural complexity and diversity has profited enormously from recent developments in mass spectrometry (MS)-related methods. For photodissociation MS, infrared (IR) and ultraviolet (UV) lasers can generate complementary fragment ions, so an effective combination of the two methods may provide rich and valuable fragmentation patterns for glycan analysis. METHODS: A 7.0 T Fourier transform ion cyclotron resonance (FTICR) mass spectrometer equipped with a double-beam laser system was applied for the experiments. 3,5-Diiodo-L-tyrosine was selected as the assistant molecule to form complex ions with ten isomeric disaccharides through electrospray ionization. The complex ions were further isolated and irradiated by IR and UV lasers separately or continuously in the FTICR cell. RESULTS: By combining the two complementary fragment spectra generated from the IR and UV lasers, a clear identification of all the ten isomers was achieved using their binary codes based on their fragmentation patterns. The double-beam method simplifies the experiment by introducing the two lasers sequentially in one experiment, providing richer fragmentation patterns and making the full discrimination easier. CONCLUSIONS: This study demonstrates the capabilities of the combination of IR and UV photodissociation MS in the identification of diverse glycan isomers. The double-beam photodissociation method described here distinguished compositional, configurational and connectivity disaccharide isomers successfully. Compared with the data accumulation method based on separate IR and UV experiments, this method is simpler, faster, more flexible and also characterized by richer fragmentation patterns.

Efficacy and safety of secukinumab in patients with psoriatic arthritis: A meta-analysis of different dosing regimens.

The appropriate dosing regimens of secukinumab for psoriatic arthritis (PsA) are not well defined. We performed a meta-analysis to evaluate the efficacy and safety of different dosing regimens of secukinumab in the treatment of PsA. A systematic search was conducted using major electronic databases to identify relevant randomized controlled trials (RCTs) comparing secukinumab 300 mg versus secukinumab 150 mg in patients with PsA. Meta-analysis was performed using Review Manager software (version 5.3). Six studies with a total of 1141 patients were included. At week 24, secukinumab 300 mg was associated with a higher American College of Rheumatology 20% response (ACR 20), ACR 50, PASI 75 response rate, and dactylitis resolution rate than secukinumab 150 mg, especially in the anti-TNF-IR subgroup. At week 52, secukinumab 300 mg was associated with a higher psoriasis area and severity index (PASI) 75 and PASI 90 response rate than secukinumab 150 mg. There was no significant difference between secukinumab 300 mg and secukinumab 150 mg in the risk of any adverse events (AEs) and serious AEs at either week 24 or week 52. Secukinumab 300 mg was significantly more effective than 150 mg, especially for patients with PsA who have failed TNF therapy, and it was well tolerated.