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Pathogenic bacteria have become a huge threat to social health and economy for their frighteningly infectious and lethal capacity. It is quite important to make a diagnosis in advance to prevent infection or allow a rapid treatment after infection. Noble metal nanoparticles, due to their unique physicochemical properties, especially optical properties, have drawn a great attention during the past decades and have been widely applied into all kinds of fields related to human health. By utilizing these noble metal nanoparticles, optical diagnosis platforms towards pathogenic bacteria have emerged continually, providing highly sensitive, selective, and particularly facile detection tools for clinic or point-of-care diagnosis. This review summarizes the recent development in this field. It begins with a brief introduction of pathogenic bacteria and noble metal nanoparticles. And then, optical detection methods are systematically discussed in three distinct aspects. In addition to these proof-of-concept methods, corresponding algorithms and point-of-care detection devices are also described. Finally, the review ends up with subjective views on present limitations and some appropriate advice for future research directions. Graphical abstract

Plasmonic core-satellite nanostructures assembled from simple building blocks have attracted extensive attention since they were reported by the way of DNA-directed assembly in 1998, because of their unique enhanced and synergistic optical properties and widespread potential applications in biosensing, imaging, drug delivery, and diagnostics. In this review, we introduce the synthetic methods of core-satellite nanostructures, emphazising the bottom-up synthesis method, including DNA, molecular, protein, peptide, amino acids, metal ion–assisted assembly, electrostatic adsorption assembly, clicked-to-assembly, and in situ deposition. Than we review and discuss their morphology classification, and summarize influencing factors of morphology. This is followed by overviews on optical properties, including localized surface plasmon resonance, surface-enhanced Raman scattering, surface-enhanced fluorescence and quenching, and applications in the biomedical field. Finally, the challenges and prospects of these kinds of nanostructures are discussed. Graphical Abstract

Treatment modalities for breast cancer, the leading cause of cancer-related deaths in women worldwide, include surgery, radiotherapy, adjuvant chemotherapy, targeted therapy, and hormonal therapy. The advancement in medical technology has facilitated substantial reduction in breast cancer mortality. However, patients may experience cognitive impairment after chemotherapy. This phenomenon called chemotherapy-induced cognitive impairment (i.e., \"chemobrain\") is common among breast cancer survivors. However, cognitive function deficits may exist before chemotherapy initiation. This study examined the functional network alterations in breast survivors by using resting-state functional magnetic resonance imaging (fMRI). We recruited 172 female participants and separated them into three groups: C+ (57 breast cancer survivors who had finished 3-12-month-long chemotherapy), C- (45 breast cancer survivors who had not undergone chemotherapy), and HC (70 participants with no breast cancer history). We analyzed mean fractional amplitudes of low-frequency fluctuation and graph theoretical topologies from resting-state fMRI and applied network-based analysis to portray functional changes among the three groups. Among the three groups, the C- group demonstrated hyperactivity in the prefrontal cortex, bilateral middle temporal gyrus, right inferior temporal gyrus and right angular gyrus. Only the left caudate demonstrated significantly more hypoactivity in the C- group than in the C+ group. Graph theoretical analysis demonstrated that the brains of the C+ group became inclined toward regular networks and the brains of the C- group became inclined toward random networks. Subtle alterations were noted in the brain activity and networks of our cancer survivors. Moreover, functional network disruptions occurred regardless of chemotherapeutic agent administration.

Magnetic Resonance Imaging (MRI) has been widely used to acquire structural and functional information about the brain. In a group- or voxel-wise analysis, it is essential to correct the bias field of the radiofrequency coil and to extract the brain for accurate registration to the brain template. Although automatic methods have been developed, manual editing is still required, particularly for echo-planar imaging (EPI) due to its lower spatial resolution and larger geometric distortion. The needs of user interventions slow down data processing and lead to variable results between operators. Deep learning networks have been successfully used for automatic postprocessing. However, most networks are only designed for a specific processing and/or single image contrast (e.g., spin-echo or gradient-echo). This limitation markedly restricts the application and generalization of deep learning tools. To address these limitations, we developed a deep learning network based on the generative adversarial net (GAN) to automatically correct coil inhomogeneity and extract the brain from both spin- and gradient-echo EPI without user intervention. Using various quantitative indices, we show that this method achieved high similarity to the reference target and performed consistently across datasets acquired from rodents. These results highlight the potential of deep networks to integrate different postprocessing methods and adapt to different image contrasts. The use of the same network to process multimodality data would be a critical step toward a fully automatic postprocessing pipeline that could facilitate the analysis of large datasets with high consistency.

This review (with 106 references) summarizes the latest progress in the synthesis, properties and biomedical applications of gold nanotubes (AuNTs). Following an introduction into the field, a first large section covers two popular AuNTs synthesis methods. The hard template method introduces anodic alumina oxide template (AAO) and track-etched membranes (TeMs), while the sacrificial template method based on galvanic replacement introduces bimetallic, trimetallic AuNTs and AuNT-semiconductor hybrid materials. Then, the factors affecting the morphology of AuNTs are discussed. The next section covers their unique surface plasmon resonance (SPR), surface-enhanced Raman scattering (SERS) and their catalytic properties. This is followed by overviews on the applications of AuNTs in biosensors, protein transportation, photothermal therapy and imaging. Several tables are presented that give an overview on the wealth of synthetic methods, morphology factors and biological application. A concluding section summarizes the current status, addresses current challenges and gives an outlook on potential applications of AuNTs in biochemical detection and drug delivery. Graphical abstract

Purpose To evaluate the prognostic factors and outcome for acute lymphoblastic leukemia (ALL) in children with MLL rearrangement (MLL-r). Methods A total of 124 pediatric patients who were diagnosed with ALL were classified into two groups based on the MLL-r status by using a retrospective case-control study method from June 2008 to June 2020. Results The prevalence of MLL-r positive in the whole cohort was 4.9%. The complete remission (CR) rate on Day 33 in the MLL-r positive group was not statistically different from the negative group (96.8% vs 97.8%, P  = 0.736). Multivariate analysis showed that T-cell, white blood cell counts (WBC) ≥ 50 × 10 9 /L, MLL-AF4, and D15 minimal residual disease (MRD) positive were independent risk factors affecting the prognosis of MLL-r positive children. Stem cell transplantation (SCT) was a favorable independent prognostic factor affecting event-free survival (EFS) in MLL-r positive patients ( P  = 0.027), and there was a trend toward an independent prognostic effect on overall survival (OS) ( P  = 0.065). The 10-year predicted EFS for patients with MLL-AF4, MLL-PTD, MLL-ENL, other MLL partner genes, and MLL-r negative cases were 46.67 ± 28.61%, 85.71 ± 22.37%, 75 ± 32.41%, 75 ± 32.41%, and 77.33 ± 10.81%, respectively ( P  = 0.048). The 10-year predicted OS were 46.67 ± 28.61%, 85.71 ± 22.37%, 75 ± 32.41%, 75 ± 32.41%, and 85.2 ± 9.77%, respectively ( P  = 0.049). The 124 patients with ALL were followed up and eventually 5 (4%) cases relapsed, with a median relapse time of 3.9 years. Conclusion Patients with MLL-r positive ALL have moderate remission rates, but are prone to relapse with low overall survival. The outcome of MLL-r positive ALL was closely related to the partner genes, and clinical attention should be paid to screening for MLL partner genes and combining them with other prognostic factors for accurate risk stratification.

Background In this study, we examined the differential associations of various proinflammatory and anti-inflammatory cytokines with depression severity from the development of breast cancer to subsequent chemotherapy treatment. Methods A cross-sectional study was conducted on a sample of 116 women: 29 controls without cancer, 55 patients with breast cancer who were not receiving chemotherapy, and 32 patients with breast cancer who were receiving chemotherapy. Blood samples were assayed to evaluate serum levels of the following cytokines: interferon-γ, interleukin (IL)-12 (p70), IL-1β, IL-2, tumor necrosis factor (TNF)-α, IL-4, IL-5, IL-10, IL-13, IL-6, and IL-17A. Depression severity was assessed using the Patient Health Questionnaire. Results After adjustment for sociodemographics, consistent patterns of the association between cytokine and depression were noted in the different groups. No significant associations were observed in the controls. Inverse associations were observed between cytokines levels and depression severity in patients with breast cancer who were not receiving chemotherapy, whereas positive associations were noted in patients with breast cancer who were receiving chemotherapy. Specific differential relationships between IL-5 levels and depression severity were found between patients with breast cancer who were receiving and not receiving chemotherapy. Conclusions Our study revealed differential relationships between cytokine levels and depression severity with the development of cancer. Immunostimulation and immunosuppression in breast cancer and cancer treatment may account for the differential responses with the development of breast cancer.

The improvement of bone ingrowth into prosthesis and enhancement of the combination of the range between the bone and prosthesis are important for long-term stability of artificial joints. They are the focus of research on uncemented artificial joints. Porous materials can be of potential use to solve these problems. This research aims to observe the characteristics of the new porous Ti-25Nb alloy and its biocompatibility in vitro, and to provide basic experimental evidence for the development of new porous prostheses or bone implants for bone tissue regeneration. The Ti-25Nb alloys with different porosities were fabricated using powder metallurgy. The alloys were then evaluated based on several characteristics, such as mechanical properties, purity, pore size, and porosity. To evaluate biocompatibility, the specimens were subjected to methylthiazol tetrazolium (MTT) colorimetric assay, cell adhesion and proliferation assay using acridine staining, scanning electron microscopy, and detection of inflammation factor interleukin-6 (IL-6). The porous Ti-25Nb alloy with interconnected pores had a pore size of 200 µm to 500 µm, which was favorable for bone ingrowth. The compressive strength of the alloy was similar to that of cortical bone, while with the elastic modulus closer to cancellous bone. MTT assay showed that the alloy had no adverse reaction to rabbit bone marrow mesenchymal stem cells, with a toxicity level of 0 to 1. Cell adhesion and proliferation experiments showed excellent cell growth on the surface and inside the pores of the alloy. According to the IL-6 levels, the alloy did not cause any obvious inflammatory response. All porous Ti-25Nb alloys showed good biocompatibility regardless of the percentage of porosity. The basic requirement of clinical orthopedic implants was satisfied, which made the alloy a good prospect for biomedical application. The alloy with 70% porosity had the optimum mechanical properties, as well as suitable pore size and porosity, which allowed more bone ingrowth.

Tungsten-doped TiO2 thin films were prepared by sol–gel method on fluorine-doped tin oxide-coated substrates as working electrodes of dye-sensitized solar cells. The influences of different W doping (0, 2, 4, 6, and 8 at%) on the microstructure, optical, and photovoltaic properties of the W-TiO2 thin-film DSSCs were studied by the measurement of X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET) analysis, and electrochemical impedance spectroscopy (EIS). An optimal DSSCs performance was observed with a 6 at% W-doped TiO2 thin film, resulting in a Voc of 0.68 V, a Jsc of 20.2 mA/cm2, an FF of 68.6%, and an efficiency (η) of 9.42%. The efficiency of DSSCs with 6 at% W-doped TiO2 photoanode improved by 75%. This is because the 6 at% W-doped TiO2 thin film increases the specific surface area and electron transfer rate.

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88–100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.