Thiophene π-Bridge Manipulation of NIR-II AIEgens for Multimodal Tumor Phototheranostics.

The fabrication of a multimodal phototheranostic platform on the basis of single-component theranostic agent to afford both imaging and therapy simultaneously, is attractive yet full of challenges. The emergence of aggregation-induced emission luminogens (AIEgens), particularly those emit fluorescence in the second near-infrared window (NIR-II), provides a powerful tool for cancer treatment by virtue of adjustable pathway for radiative/non-radiative energy consumption, deeper penetration depth and aggregation-enhanced theranostic performance. Although bulky thiophene π-bridges such as ortho-alkylated thiophene, 3,4-ethoxylene dioxythiophene and benzo[c]thiophene are commonly adopted to construct NIR-II AIEgens, the subtle differentiation on their theranostic behaviours has yet to be comprehensively investigated. In this work, systematical investigations discovered that AIEgen BT-NS bearing benzo[c]thiophene possesses acceptable NIR-II fluorescence emission intensity, efficient reactive oxygen species generation, and high photothermal conversion efficiency. Eventually, by using of BT-NS nanoparticles, unprecedented performance on NIR-II fluorescence/photoacoustic/photothermal imaging-guided synergistic photodynamic/photothermal elimination of tumors was demonstrated. This study thus offers useful insights into developing versatile phototheranostic systems for clinical trials.

Ferulic Acid Induces Autophagy and Apoptosis in Colon Cancer CT26 Cells via the MAPK Pathway.

Ferulic acid (FA) is a bioactive compound found in traditional Chinese herbal medicine; for example, it is present in Xinjiang Ferula, but also in strong-flavor Chinese baijiu. FA has been shown to play a crucial role in treating oxidative stress, skin whitening, and eye diseases. In this study, the potential role of FA as a means of inducing apoptosis and inhibiting colon cancer induced by the transplantation of CT26 cells was investigated. The results show that FA adjuvant treatment caused an upregulation in the expression of genes related to autophagy while simultaneously suppressing the expression of inflammatory response elements and improving the bodyweight, glutamic pyruvic transaminase (ALT), and glutamic oxaloacetic transaminase (AST) in vivo. Furthermore, FA inhibited the proliferation of CT26 cells and induced apoptosis, specifically by activating the phosphorylation of ERK and JNK to enhance the essential proteins BCL-2 and BAX in the apoptosis pathway. These results suggest that FA could be a promising auxiliary therapeutic agent for the treatment of colon cancer. Further research is needed to better understand the mechanisms underlying the beneficial effects of FA and its synergistic effects with other compounds.

A NIR-II emissive polymer AIEgen for imaging-guided photothermal elimination of bacterial infection.

The development of antibiotics resistance has made multidrug-resistant (MDR) bacterial infection one of the most serious global health issues. Photothermal therapy (PTT) is an emerging therapeutic mode which can be applied to bacterial infection without inducing resistance. Moreover, enhanced therapeutic efficacy and less tissue damage can be realized with NIR-II fluorescence imaging (FLI) guided PTT. Herein, a polymeric luminogen with aggregation-induced emission (AIEgens) characteristics, poly(dithieno[3,2-b:2',3'-d]pyrrole-benzo[1,2-c:4,5-c']bis([1,2,5]thiadiazole)) (PDTPTBT), was synthesized and used as a photothermal agent for PTT of bacterial infections. PDTPTBT was encapsulated into liposomes (L-PDTPTBT) for improved water dispersibility. Upon 808 nm NIR irradiation, L-PDTPTBT can eliminate multiple bacteria including the Gram-positive methicillin-resistant Staphylococcus aureus and Enterococcus faecalis, the Gram-negative Escherichia coli and Pseudomonas aeruginosa. Serious damage of bacterial membrane and leakage of cytoplasm is observed after photothermal treatment using L-PDTPTBT. The potential of the formulation has been demonstrated in two infected animal models: (i) a subcutaneous abscess model and (ii) a diabetic skin infection model. In the diabetic skin infection model, the death of mice is largely suppressed and the wounds can heal more quickly with treatment of L-PDTPTBT under NIR irradiation. The excellent photothermal bactericidal ability and low cytotoxicity make L-PDTPTBT potential candidate for treating MDR bacterial infections in the future.

Synchronously boosting type-I photodynamic and photothermal efficacies via molecular manipulation for pancreatic cancer theranostics in the NIR-II window.

In view of the fact that pancreatic cancer, called as the king of cancer, is one of the most lethal malignancies, exploring effective technologies for pancreatic cancer diagnosis and therapy remains an appealing yet significantly challenging task. Phototheranostics has recently received considerable attention by virtue of its various distinctive advantages. However, the limited penetration depth, strong oxygen-dependence and high heat shock protein-inhibition of conventional phototheranostic materials severely hamper their overall theranostic efficacy, especially for deep-seated hypoxia tumors, such as pancreatic tumor. In this study, an aggregation-induced emission (AIE)-featured photosensitizer, namely DCTBT, synchronously sharing NIR-II fluorescence imaging (FLI), diminished oxygen-dependent type-I photodynamic therapy (PDT) and high-efficiency photothermal therapy (PTT) functions was subtly constructed by molecular engineering. With the aid of an EGFR-targeting-peptide-modified amphiphilic polymer, the as-prepared DCTBT-loaded liposomes is capable of effectively accumulating at and visualizing pancreatic tumor, as well as significantly suppressing the tumor growth on both subcutaneous and orthotopic PANC-1 tumor mice models. This study thus brings useful insights into designing the next generation of cancer theranostic agents for potential clinical applications.

Polymers and protein-associated vesicles for the microencapsulation of anthocyanins from grape skins used for food applications.

BACKGROUND: Anthocyanins were extracted from grape skins by a combination of ethanolic-ultrasonic assisted methods and were then encapsulated by freeze-drying in soy phosphatidylcholine vesicles with the addition of different polymers, such as pectin, acacia gum, and whey protein isolate. The goal of this research was to microencapsulate anthocyanin compounds extracted from grape skins, to characterize the stability and behavior of the vesicles and then to use them to obtain a new light formulated mayonnaise. RESULTS: The particle size ranged from 900 nm in the control condition to 250 nm in vesicles loaded with whey proteins. The powders showed higher encapsulation efficiency for all variants, ranging from 81 to 96%. Vibrational spectroscopy revealed better inclusion of anthocyanins in polysaccharide-based coacervates, whereas in protein-based coacervates a possible interaction of anthocyanins with amine groups was observed. The vesicles were tested for in vitro release, and the results confirmed the gradual release of the anthocyanins in both stages of digestion, with a residual content of about 50% in the vesicles. The powders displayed high stability during storage in the dark at 4 °C. The panelists appreciated the new light formulated mayonnaises enriched with 10% dried vesicles compared with the control sample, in particular samples with acacia gum. CONCLUSION: The study revealed that polymer-loaded vesicles presented stability in simulated gastrointestinal fluids and have proved successful in obtaining new light enriched mayonnaises. © 2020 Society of Chemical Industry.

An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker.

Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise. A machine learning algorithm is introduced here to address this listening situation. A deep neural network was trained to estimate the ideal ratio mask for a male target talker in the presence of a female competing talker. The monaural algorithm was found to produce sentence-intelligibility increases for hearing-impaired (HI) and normal-hearing (NH) listeners at various signal-to-noise ratios (SNRs). This benefit was largest for the HI listeners and averaged 59%-points at the least-favorable SNR, with a maximum of 87%-points. The mean intelligibility achieved by the HI listeners using the algorithm was equivalent to that of young NH listeners without processing, under conditions of identical interference. Possible reasons for the limited ability of HI listeners to perceptually segregate concurrent voices are reviewed as are possible implementation considerations for algorithms like the current one.

Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises.

Supervised speech segregation has been recently shown to improve human speech intelligibility in noise, when trained and tested on similar noises. However, a major challenge involves the ability to generalize to entirely novel noises. Such generalization would enable hearing aid and cochlear implant users to improve speech intelligibility in unknown noisy environments. This challenge is addressed in the current study through large-scale training. Specifically, a deep neural network (DNN) was trained on 10 000 noises to estimate the ideal ratio mask, and then employed to separate sentences from completely new noises (cafeteria and babble) at several signal-to-noise ratios (SNRs). Although the DNN was trained at the fixed SNR of - 2 dB, testing using hearing-impaired listeners demonstrated that speech intelligibility increased substantially following speech segregation using the novel noises and unmatched SNR conditions of 0 dB and 5 dB. Sentence intelligibility benefit was also observed for normal-hearing listeners in most noisy conditions. The results indicate that DNN-based supervised speech segregation with large-scale training is a very promising approach for generalization to new acoustic environments.

Reconstruction techniques for improving the perceptual quality of binary masked speech.

This study proposes an approach to improve the perceptual quality of speech separated by binary masking through the use of reconstruction in the time-frequency domain. Non-negative matrix factorization and sparse reconstruction approaches are investigated, both using a linear combination of basis vectors to represent a signal. In this approach, the short-time Fourier transform (STFT) of separated speech is represented as a linear combination of STFTs from a clean speech dictionary. Binary masking for separation is performed using deep neural networks or Bayesian classifiers. The perceptual evaluation of speech quality, which is a standard objective speech quality measure, is used to evaluate the performance of the proposed approach. The results show that the proposed techniques improve the perceptual quality of binary masked speech, and outperform traditional time-frequency reconstruction approaches.

An algorithm to improve speech recognition in noise for hearing-impaired listeners.

Despite considerable effort, monaural (single-microphone) algorithms capable of increasing the intelligibility of speech in noise have remained elusive. Successful development of such an algorithm is especially important for hearing-impaired (HI) listeners, given their particular difficulty in noisy backgrounds. In the current study, an algorithm based on binary masking was developed to separate speech from noise. Unlike the ideal binary mask, which requires prior knowledge of the premixed signals, the masks used to segregate speech from noise in the current study were estimated by training the algorithm on speech not used during testing. Sentences were mixed with speech-shaped noise and with babble at various signal-to-noise ratios (SNRs). Testing using normal-hearing and HI listeners indicated that intelligibility increased following processing in all conditions. These increases were larger for HI listeners, for the modulated background, and for the least-favorable SNRs. They were also often substantial, allowing several HI listeners to improve intelligibility from scores near zero to values above 70%.

Role of mask pattern in intelligibility of ideal binary-masked noisy speech.

Intelligibility of ideal binary masked noisy speech was measured on a group of normal hearing individuals across mixture signal to noise ratio (SNR) levels, masker types, and local criteria for forming the binary mask. The binary mask is computed from time-frequency decompositions of target and masker signals using two different schemes: an ideal binary mask computed by thresholding the local SNR within time-frequency units and a target binary mask computed by comparing the local target energy against the long-term average speech spectrum. By depicting intelligibility scores as a function of the difference between mixture SNR and local SNR threshold, alignment of the performance curves is obtained for a large range of mixture SNR levels. Large intelligibility benefits are obtained for both sparse and dense binary masks. When an ideal mask is dense with many ones, the effect of changing mixture SNR level while fixing the mask is significant, whereas for more sparse masks the effect is small or insignificant.

Multitalker speech perception with ideal time-frequency segregation: effects of voice characteristics and number of talkers.

When a target voice is masked by an increasingly similar masker voice, increases in energetic masking are likely to occur due to increased spectro-temporal overlap in the competing speech waveforms. However, the impact of this increase may be obscured by informational masking effects related to the increased confusability of the target and masking utterances. In this study, the effects of target-masker similarity and the number of competing talkers on the energetic component of speech-on-speech masking were measured with an ideal time-frequency segregation (ITFS) technique that retained all the target-dominated time-frequency regions of a multitalker mixture but eliminated all the time-frequency regions dominated by the maskers. The results show that target-masker similarity has a small but systematic impact on energetic masking, with roughly a 1 dB release from masking for same-sex maskers versus same-talker maskers and roughly an additional 1 dB release from masking for different-sex masking voices. The results of a second experiment measuring ITFS performance with up to 18 interfering talkers indicate that energetic masking increased systematically with the number of competing talkers. These results suggest that energetic masking differences related to target-masker similarity have a much smaller impact on multitalker listening performance than energetic masking effects related to the number of competing talkers in the stimulus and non-energetic masking effects related to the confusability of the target and masking voices.

Speech perception of noise with binary gains.

For a given mixture of speech and noise, an ideal binary time-frequency mask is constructed by comparing speech energy and noise energy within local time-frequency units. It is observed that listeners achieve nearly perfect speech recognition from gated noise with binary gains prescribed by the ideal binary mask. Only 16 filter channels and a frame rate of 100 Hz are sufficient for high intelligibility. The results show that, despite a dramatic reduction of speech information, a pattern of binary gains provides an adequate basis for speech perception.