J-Aggregation induced NIR-II fluorescence: an aza-BODIPY luminogen for efficient phototheranostics.

The development of organic dyes with emission peaks in the second near-infrared window (NIR-II 1000-1700 nm) is highly desirable for in vivo imaging and imaging-guided phototheranostics. However, the lack of appropriate molecular frameworks and the challenges associated with complex synthesis critically hinder the development of new candidate fluorophores. J-Aggregation is considered as a smart and straightforward way to construct such a therapeutic agent with NIR-II fluorescence imaging properties. Here, we present the design and synthesis of an aza-BODIPY probe (TA). Upon encapsulation within the amphiphilic polymer DSPEG-PEG2000-NH2, TA underwent self-assembly and formed J-aggregates (TAJ NPs), which showed emission at 1020 nm. High spatial resolution and adequate signal-to-noise ratio of the TAJ NPs are demonstrated for noninvasive bioimaging of the vasculature, lymph nodes and bones of mice in the NIR-II region. Moreover, the TAJ NPs exhibited good tumor enrichment efficiency with reduced liver accumulation and significant imaging-guided phototherapy performance against lung cancer cells. Taken together, this work not only introduces a new NIR-II imaging and phototheranostic agent based on J-aggregates, but also provides insight into the development of versatile organic dyes for future clinical implementation.

Exploiting Cancer Vulnerabilities by Blocking of the DHODH and GPX4 Pathways: A Multifunctional Bodipy/PROTAC Nanoplatform for the Efficient Synergistic Ferroptosis Therapy.

Ferroptosis is a form of programmed cell death and plays an important role in many diseases. Dihydroorotate dehydrogenase (DHODH) and glutathione peroxidase 4 (GPX4) play major roles in cell resistance to ferroptosis. Therefore, inactivation of these proteins provides an excellent opportunity for efficient ferroptosis-based synergistic cancer therapy. In this study, a multifunctional nanoagent (BPNpro ) containing a GPX4 targeting boron dipyrromethene (Bodipy) probe (BP) and a DHODH targeting proteolysis targeting chimera (PROTAC) is reported. BPNpro is prepared using a nanoprecipitation method in the presence of a thermoresponsive liposome, where BP is encapsulated inside and the cathepsin B (CatB)-cleavable PROTAC peptide (DPCP) is modified on the outer surface. In the presence of near-infrared (NIR) photoirradiation, BPNpro is melted and BP is released in tumor cells. Subsequently, BP inhibits the activity of GPX4 by covalently bonding with the selenocysteine at the enzyme active site. In addition, DPCP achieves sustained degradation of DHODH upon activation by CatB overexpressed in the tumor. The synergistic deactivation of GPX4 and DHODH induces extensive ferroptosis and subsequent cell death. In vivo and in vitro studies clearly show that the proposed ferroptosis therapy provides excellent antitumor effect.

Portable smartphone-assisted ratiometric fluorescence sensor for visual detection of glucose.

Fluorescence-based visual assays have sparked tremendous attention in on-site detection due to their obvious color gradient changes and high sensitivity. In this study, a novel emission wavelength shift-based visual sensing platform is constructed to detect glucose based on the oxidation of Rhodamine B (RhB). MnO2 nanosheets (MnO2 NS) with strong oxidizing properties were introduced to oxidize RhB, which resulted in a blue shift in the emission wavelength, and a visual color changed of the fluorescence from orange-red to green. The oxidation reaction could be inhibited via reducing and destroying MnO2 NS by H2O2, which was produced by the oxidizing procedure of glucose in the presence of glucose oxidase (GOx). A series of wavelength shifts and fluorescence color variations appeared with the addition of various amounts of glucose. A ratiometric fluorescence glucose sensor with a lowest recorded concentration of 0.25 µM was developed. Meanwhile, test paper-based assays integrated with the smartphone platform were established for the sensing of glucose by means of the significant fluorescence color changes, offering a reliable, sensitive, and portable on-site assay of glucose.

Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study.

Background: Gestational diabetes mellitus (GDM) is diabetes first diagnosed in pregnancy. GDM, together with its short- and long-term negative outcomes, is increasing in incidence all over the world. The current diagnostic method for GDM, the oral glucose tolerance test (OGTT), is dated and has been reported as inconvenient for women as well as poorly reproducible and reliable. Aims: We aimed at assessing the acceptability, feasibility, and accuracy of continuous glucose monitoring (CGM) as a diagnostic test for GDM and explore its correlation with the OGTT and risk factors for GDM. Methods: In this prospective cohort study, pregnant women due for or having completed OGTT underwent CGM for seven days, performing daily finger-prick blood glucose levels before completing an acceptability questionnaire. Data on GDM risk factors and CGM variability were analyzed and compared with OGTT results. Results: Seventy-three women completed CGM (40 GDM, 33 normal glucose tolerances); 34 concurrently underwent OGTT. CGM was acceptable and generally well-tolerated, with skin irritation/itchiness the only adverse event (11 mild, one severe). CGM and OGTT strongly correlated for fasting glucose values (r = 0.86, p < 0.05) only. Triangulating GDM risk factors, OGTT results and CGM variability parameters with the application of machine learning highlighted the possibility of unmasking false positive (11 showed low CGM variability and demographic risks but positive OGTT) and false-negative OGTT diagnoses (1 showed high CGM variability and demographic risks but negative OGTT). Conclusions: CGM was well-tolerated, showing poorer glycaemic control in GDM, and revealing potential misdiagnosis of the OGTT when combined with GDM risk factors. Future research is needed to determine cut-off values for CGM-defined and OGTT-independent screening criteria for GDM.

Black phosphorus nanosheets/poly(allylamine hydrochloride) based electrochemical immunosensor for the selective detection of human epididymis protein 4.

In this work, an electrochemical immunosensor based on black phosphorus nanosheets (BPNS)/poly(allylamine hydrochloride) (PAH) nanocomposite modified glassy carbon electrode was developed for the detection of ovarian cancer biomarker HE4. PAH has been applied to retain BPNS in its original honeycomb structure and to anchor biomolecules electrostatically on the transducer surface. The as synthesized nanocomposite was characterized by zeta potential analysis, scanning electron microscopy, x-ray photoelectron spectroscopy, transmission electron microscopy, high-resolution transmission electron microscopy. Subsequently, the performance of the electrochemical immunosensor was evaluated through cyclic voltammetry, differential pulse voltammetry and electrochemical impedance spectroscopy. Under the optimal condition, the developed electrochemical immunosensor permitted to detect HE4 with a linear range of 0.1-300 ng ml-1and a detection limit of 0.01 ng ml-1. The developed sensor exhibited good selectivity and specificity to HE4 with negligible interference effect from common biomolecules like bovine serum albumin, lysozyme, protamine, glucose, fructose, hemoglobin and fetal bovine serum. Further, practical application of developed electrochemical immunosensor was demonstrated in spiked human serum which showed satisfactory recovery percentages.

Engineering carbon nanotubes for sensitive viral detection.

Viral infections have been proven a severe threat to human beings, and the pandemic of Coronavirus Disease 2019 (COVID-19) has become a societal health concern, including mental distress and morbidity. Therefore, the early diagnosis and differentiation of viral infections are the prerequisite for curbing the local and global spread of viruses. To this end, carbon nanotubes (CNTs) based virus detection strategies are developed that provide feasible alternatives to conventional diagnostic techniques. Here in this review, an overview of the design and engineering of CNTs-based sensors for virus detection is summarized, followed by the nano-bio interactions used in developing biosensors. Then, we classify the viral sensors into covalently engineered CNTs, non-covalently engineered CNTs, and size-tunable CNTs arrays for viral detection, based on the type of CNTs-based nano-bio interfaces. Finally, the current challenges and prospects of CNTs-based sensors for virus detection are discussed.

Effect of post-diagnosis exercise on depression symptoms, physical functioning and mortality in breast cancer survivors: A systematic review and meta-analysis of randomized control trials.

BACKGROUND: Cancer is the second leading cause of death worldwide. Breast cancer, the most common cancer found in women, affects 2.1 million women annually and has the highest number of cancer related deaths. The objective of the current meta-analysis is to evaluate the effects of post-diagnosis exercises on depression, physical functioning, and mortality in breast cancer survivors. METHODS: The search for eligible articles was conducted through CINAHL, Medline/PubMed, Scopus, Cochrane, Emerald Insight and Web of Science, Embase database, MEDLINE In-Process, Elsevier, Google Scholar, PsycInfo, Cochrane Database of Systematic Reviews (CDSR), Cochrane Central Register of Controlled Trials (CENTRAL), Allied and Complementary Medicine (AMED), Biosis Previews, SPORTDiscus, PEDro scientific databases from 1974 to 2020. Following the exclusion procedure, 26 articles yielded for final analysis. The combined statistics for depression, physical functioning, and mortality in breast cancer survivors were calculated using standardized mean differences (SMD). Standard errors and 95% confidence intervals (CI) were converted to standard deviations as required. For mortality, combined statistics were calculated using hazard ratios (HR). The 95% CIs were converted to standard errors as required. The forest plots display point estimates and 95% CIs. RESULTS: Statistically significant improvements on levels of depression were identified following the exercise intervention, suggesting that post-diagnosis physical activity leads to a decrease in depression scores. Overall, post-diagnosis exercise led to a 37% reduction in the rate of breast cancer-specific mortality. The all-cause mortality rate was decreased by 39% with the inclusion of moderate physical activity as the part of daily routine. CONCLUSIONS: Future studies should look at how to improve the quality of life while incorporating physical activity as a daily routine after breast-cancer treatment.

Catalytic SrMoO4 nanoparticles and conducting polymer composite sensor for monitoring of K+-induced dopamine release from neuronal cells.

Octahedral SrMoO4 nanoparticles (NPs) with a high degree of crystallinity and controlled size (250-350 nm) were synthesized for the first time by employing a facile hydrothermal method. The prepared NPs were composited with a carboxyl group bearing conducting polymer (2,2:5,2-terthiophene-3-(p-benzoic acid, TBA)) to attain a stable sensor probe (pTBA/SrMoO4) which was analyzed using various surface analysis methods. The catalytic performance of the composite electrode was explored as an oxidation catalyst for biological molecules through anchoring on the conducting polymer layer, which functioned as a matrix to enhance the stability and selectivity of the sensor probe. The pTBA/SrMoO4 coated on glassy carbon displayed excellent electrocatalytic performance for the oxidation of some biologically important molecules, including dopamine (DA) in neuronal cells. The sensor immobilized with the catalyst showed an excellent response for DA with a dynamic range between 0.2 and 500 µM and a detection limit of 5 nM. The proposed sensor demonstrates the detection of trace DA released from PC12 cells under K+ stimulation, followed by inhibition of the release of exogenic DA by a Ca2+ channel blocker (nifedipine). The developed method provides an interesting way to monitor the effect of extracellular substances on living cells and the drug potency test.

Prediction of the Ultimate Strength of Notched and Unnotched IM7/977-3 Laminated Composites Using a Micromechanics Approach.

This paper proposes a multi-scale analysis technique based on the micromechanics of failure (MMF) to predict and investigate the damage progression and ultimate strength at failure of laminated composites. A lamina's representative volume element (RVE) is developed to predict and calculate constituent stresses. Damages that occurred in the constituents are calculated using separate failure criteria for both fiber and matrix. Subsequently, the volume-based damage homogenization technique is utilized to prevent the localization of damage throughout the total matrix zone. The proposed multiscale analysis procedure is then used to investigate the notched and unnotched behavior of three multi-directional composite layups, [30, 60, 90, -60, 30]2S, [0, 45, 90, -45]2S, and [60, 0, -60]3S, subjected to static tension and compression loading. The specimen is fabricated from unidirectionally reinforced composite (IM7/977-3). The prediction of ultimate strength at failure and equivalent stiffness are then benchmarked against the experimental test data. The comparative analysis with various failure models is also carried out to validate the proposed model. MMF demonstrated the capability to correctly predict the ultimate strength at failure for a range of multidirectional composites laminates under tensile and compressive load. The numerically predicted findings revealed a good agreement with the experimental test data. Out of the three investigated composite layups, the simulated results for the quasi-isotropic [0, 45, 90, -45]2S layup agreed extremely well with the experimental results with all the percentage errors within 10% of the measured failure loads.

CuO Hollow Cubic Caves Wrapped with Biogenic N-Rich Graphitic C for Simultaneous Monitoring of Uric Acid and Xanthine.

Herein, we synthesized hollow cubic caves of CuO (HC) and wrapped it with N-rich graphitic C (NC), derived from a novel biogenic mixture composed of dopamine (DA) and purine. The synthesized NC wrapped HC (NC@HC) sensor shows enhanced electrocatalytic efficacy compared to unwrapped CuO with shapes including HC, sponge (SP), cabbage (CB), and solid icy cubes (SC). The shape and composition of synthesized materials were confirmed through field-emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), whereas interfacial surface energy was calculated through contact angle measurement. The designed NC@HC sensor shows a remarkable response toward the simultaneous detection of uric acid (UA) and xanthine (Xn) with detection limits of 0.017 ± 0.001 (S/N of 3) and 0.004 ± 0.001 µM (S/N of 3), respectively. In addition, this platform was successfully applied to monitor UA from the gout patient serum. To the best of our knowledge, this is the first report on using such novel NC@HC materials for the simultaneous monitoring of UA and Xn.

Polymer matrix: A good substrate material for oxygen probes used in pressure sensitive paints.

Over the past few years, surface pressure measurement has fundamental importance in many areas, particularly, aerodynamic research. Conventional methods involve pressure taps, but due to the nature of these pressure taps, only pressure information of isolated points on model surface is available, which limit their applications in aerodynamics studies. Recently the newly developed approach, pressure sensitive paint (PSP) has revolutionized such pressure measurements and various PSP materials have been developed for aerodynamics research. Hence, the main focus of this review is to study the interactions of polymers with different oxygen probes and polymeric role as supporting material in the maturation of PSP. In this review, the selected PSP materials are categorically elucidated in terms of their advantages and limitations to give a fair insight about their applicability. Further, we have summarized and articulated such particular optical oxygen sensing materials either that have been used as PSP or have potential to be used as PSP materials.

Nano-biosensor for the in vitro lactate detection using bi-functionalized conducting polymer/N, S-doped carbon; the effect of αCHC inhibitor on lactate level in cancer cell lines.

A robust amperometric sensor was developed for the lactate detection in the extracellular matrix of cancer cells. The sensor was fabricated by separately immobilizing nicotinamide adenine dinucleotide (NAD+) onto a carboxylic acid group and lactate dehydrogenase (LDH) onto an amine group of bi-functionalized conducting polymer (poly 3-(((2,2':5',2″-terthiophen)-3'-yl)-5-aminobenzoic acid (pTTABA)) composited with N, S-doped porous carbon. Morphological features of the composite layer and sensor performance were investigated using FE-SEM, XPS, and electrochemical methods. The experimental parameters were optimized to get the best results. The calibration plot showed a linear dynamic range between 0.5 µM and 4.0 mM with the detection limit of 112 ± 0.02 nM. The proposed sensor was applied to detect lactate in a non-cancerous (Vero) and two cancer (MCF-7 and HeLa) cell lines. Among these cell lines, MCF-7 was mostly affected by the administration of lactate transport inhibitor, α-cyano-4-hydroxycinnamate (αCHC), followed by HeLa and Vero, respectively. Furthermore, the effect of αCHC concentration and treatment time on the lactate level in the cell lines were demonstrated. Finally, cytotoxicity studies were also performed to evaluate the effect of αCHC on cell viability.

A dynamic neural network model for predicting risk of Zika in real time.

BACKGROUND: In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. METHODS: In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. RESULTS: The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. CONCLUSIONS: Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.

Open-field PET: Simultaneous brain functional imaging and behavioural response measurements in freely moving small animals.

A comprehensive understanding of how the brain responds to a changing environment requires techniques capable of recording functional outputs at the whole-brain level in response to external stimuli. Positron emission tomography (PET) is an exquisitely sensitive technique for imaging brain function but the need for anaesthesia to avoid motion artefacts precludes concurrent behavioural response studies. Here, we report a technique that combines motion-compensated PET with a robotically-controlled animal enclosure to enable simultaneous brain imaging and behavioural recordings in unrestrained small animals. The technique was used to measure in vivo displacement of [11C]raclopride from dopamine D2 receptors (D2R) concurrently with changes in the behaviour of awake, freely moving rats following administration of unlabelled raclopride or amphetamine. The timing and magnitude of [11C]raclopride displacement from D2R were reliably estimated and, in the case of amphetamine, these changes coincided with a marked increase in stereotyped behaviours and hyper-locomotion. The technique, therefore, allows simultaneous measurement of changes in brain function and behavioural responses to external stimuli in conscious unrestrained animals, giving rise to important applications in behavioural neuroscience.

Effect of probiotic interventions on depressive symptoms: A narrative review evaluating systematic reviews.

Depression is one of the most prevalent mental illnesses and is often associated with various other medical disorders. Since the 1980s, the primary pharmacological treatment has been antidepressants, but due to the recent discovery of the association between the gut microbiome and mental health, probiotics have been proposed as an adjunctive or alternate treatment. In this narrative review, we aim to provide a holistic perspective by synthesizing and evaluating existing evidence, discussing key biological mechanisms, exploring the history of probiotic use, and appreciating the influence of modern diet on mental health. Five online databases were searched for relevant studies up to December 2017. Systematic reviews that included randomized controlled trials assessing the efficacy of probiotics in the treatment of depressive symptoms were included. Seven systematic reviews met the inclusion criteria. Three of these reviews conducted meta-analyses, out of which, two concluded that probiotics improved depressive symptoms in the sample population. Out of the four reviews that conducted qualitative analysis, three reviews concluded that probiotics have the potential to be used as a treatment. Due to the differences in clinical trials, a definitive effect of probiotics on depressive symptoms cannot be concluded. Nonetheless, probiotics seem to potentially produce a significant therapeutic effect for subjects with pre-existing depressive symptoms. Further studies are warranted for definitive conclusions.

Early detection of lung cancer biomarkers through biosensor technology: A review.

Lung cancer is undoubtedly one of the most serious health issues of the 21 st century. It is the second leading cause of cancer-related deaths in both men and women　worldwide, accounting for about 1.5 million deaths annually. Despite advances in the treatment of lung cancer with new pharmaceutical products and technological improvements, morbidity and mortality rates remains a significant challenge for the cancer biologists and oncologists. The vast majority of lung cancer patients present with advanced-stage of pathological process that ultimately leads to poor prognosis and a five-year survival rate less than 20%. Early and accurate screening and analysis using cost-effective means are urgently needed to effectively diagnose the disease, improve the survival rate or to reduce mortality and morbidity associated with lung cancer patients. Thus, the only hope for early recognition of risk factors and timely diagnosis and treatment of lung cancer is biosensors technology. Novel biosensing based diagnostics approaches for predicting metastatic risks are likely to have significant therapeutic and clinical impact in the near future. This article systematically provides a brief overview of various biosensing platforms for identification of lung cancer disease biomarkers, with a specific focus on recent advancements in electrochemical and optical biosensors, analytical performances of different biosensors, challenges and further research opportunities for routine clinical analysis.

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula.

Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.

New-onset diabetes after transplantation among renal transplant recipients at a new transplant center; King Fahad Specialist Hospital-Dammam, Saudi Arabia.

New-onset diabetes after transplant (NODAT) has been reported to occur in 4%-25% of renal transplant recipients. Its development has also been shown to be associated with an adverse impact on patient survival and an increased risk of graft rejection and graft loss, as well as an increased incidence of infectious complications. The study aims to describe the incidence of NODAT and its important risk factors in a single center. We conducted a retrospective analysis of data from all kidney transplant recipients in our center, transplanted between September 2008 and May 2013. Out of 311 patients, 77 had diabetes mellitus (DM) before transplantation and were excluded, leaving 234 patients as the study population. NODAT was diagnosed based on the WHO definition for DM: any two readings of fasting blood sugar >7 mmol/L or random blood sugar >11 or the use of hypoglycemic medications after 1st posttransplant month. The mean age of the study patients was 36 years ± 14 years; 55.5% were male, 69% had living-related transplant, 31% had deceased donor transplant, 98% were on tacrolimus-based immunosuppression regimen, 2% on cyclosporine and all patients were on the steroid-based regimen. The 1 and 5-year cumulative incidence of NODAT was 14.1% and 27.5%, respectively. The median duration to onset of NODAT was 2.5 months. The body mass index of >30 kg/m2 and age >60 years at the time of transplant were significantly associated with the occurrence of NODAT. Our finding of incidence was not different from what has been reported in the literature. Larger prospective and multicenter studies are needed.