Effect of embedding a sieving phase into the current plastic recycling process to capture microplastics

This study proposes a systematic change to the current plastic recycling process by introducing a sieving stage in between the shredding and washing units to capture the microplastics being unintentionally generated and released. The benefit of adding the sieving stage to minimise microplastics release to wash water was highlighted by comparing the findings with the case where microplastics are released to wash water and a conventional coagulation process is used to remove microplastics from water. Two coagulants, aluminium sulphate (Al2(SO4)3.18H2O) and aluminium chloride (AlCl3.6H2O), were used to remove polyethylene terephthalate (PET) and polycarbonate (PC) from water. The size of the microplastic particles played a significant role on the removal efficiency. The maximum removal efficiency of PET by AlCl3.6H2O was 99.2 % for the particles in 1.18–5 mm range, whereas the average removal efficiency over the whole tested size range of 0.15–5.00 mm was 76.1 % for the same plastic-coagulant combination. By contrast, the addition of a 5 mm sieve between the shredding and the washing units was found to capture 96–97 % of the microplastics generated. The findings of this innovative experiment demonstrate the beneficial impact that this strategy has on capturing microplastics prior to entering water matrix

Effect of glucose, lactate and pyruvate concentrations on in vitro growth of goat granulosa cell

Carbohydrates are among the most influential of the numerous components of culture medium that affect metabolism and developmental potential. Glucose, lactate and pyruvate are required for the growth of oocytes and other follicular cells in vitro. The aim of this study was to determine the effects of different concentrations of glucose, lactate and pyruvate on promoting DNA synthesis of granulosa cells in a serum-free medium. Effects of glucose (0.75, 1.5 or 3 mM), pyruvate (0.1 or 0.33 mM) and Llactate (3, 6 or 12 mM) concentrations in the maturation medium on the relative granulosa cell growth during metaphase II (MII) were examined in a 3 × 2 × 3 factorial design. The greatest relative granulosa cell growth response (p<0.05) was observed in the presence of 1.5 mM glucose and 0.33 mM pyruvate or in 6 mM lactate and 0.33 mM pyruvate. Increasing pyruvate concentrations from 0.1 to 0.33 mM resulted in an increase in DNA synthesis in granulosa cells. In conclusion, the results of this study showed that increasing glucose and pyruvate concentrations in the maturation medium increased the growth of goat granulosa cells.Key word: Energy substrate, granulosa cell growth, methyl-3H-thymidine, goat

Silicon-Mediated Resilience: Unveiling the Protective Role against Combined Cypermethrin and Hymexazol Phytotoxicity in Tomato Seedlings

Insecticides and fungicides present potential threats to non-target crops, yet our comprehension of their combined phytotoxicity to plants is limited. Silicon (Si) has been acknowledged for its ability to induce crop tolerance to xenobiotic stresses. However, the specific role of Si in alleviating the cypermethrin (CYP) and hymexazol (HML) combined stress has not been thoroughly explored. This study aims to assess the effectiveness of Si in alleviating phytotoxic effects and elucidating the associated mechanisms of CYP and/or HML in tomato seedlings. The findings demonstrated that, compared to exposure to CYP or HML alone, the simultaneous exposure of CYP and HML significantly impeded seedling growth, resulting in more pronounced phytotoxic effects in tomato seedlings. Additionally, CYP and/or HML exposures diminished the content of photosynthetic pigments and induced oxidative stress in tomato seedlings. Pesticide exposure heightened the activity of both antioxidant and detoxification enzymes, increased proline and phenolic accumulation, and reduced thiols and ascorbate content in tomato seedlings. Applying Si (1 mM) to CYP- and/or HML-stressed seedlings alleviated pigment inhibition and oxidative damage by enhancing the activity of the pesticide metabolism system and secondary metabolism enzymes. Furthermore, Si stimulated the phenylpropanoid pathway by boosting phenylalanine ammonia-lyase activity, as confirmed by the increased total phenolic content. Interestingly, the application of Si enhanced the thiols profile, emphasizing its crucial role in pesticide detoxification in plants. In conclusion, these results suggest that externally applying Si significantly alleviates the physio-biochemical level in tomato seedlings exposed to a combination of pesticides, introducing innovative strategies for fostering a sustainable agroecosystem

Tracheal Replacement Therapy with a Stem Cell-Seeded Graft: Lessons from Compassionate Use Application of a GMP-Compliant Tissue-Engineered Medicine

Tracheal replacement for the treatment of end-stage airway disease remains an elusive goal. The use of tissue-engineered tracheae in compassionate use cases suggests that such an approach is a viable option. Here, a stem cell-seeded, decellularized tissue-engineered tracheal graft was used on a compassionate basis for a girl with critical tracheal stenosis after conventional reconstructive techniques failed. The graft represents the first cell-seeded tracheal graft manufactured to full good manufacturing practice (GMP) standards. We report important preclinical and clinical data from the case, which ended in the death of the recipient. Early results were encouraging, but an acute event, hypothesized to be an intrathoracic bleed, caused sudden airway obstruction 3 weeks post-transplantation, resulting in her death. We detail the clinical events and identify areas of priority to improve future grafts. In particular, we advocate the use of stents during the first few months post-implantation. The negative outcome of this case highlights the inherent difficulties in clinical translation where preclinical in vivo models cannot replicate complex clinical scenarios that are encountered. The practical difficulties in delivering GMP grafts underscore the need to refine protocols for phase I clinical trials

Doppler ultrasound scoring to predict chemotherapeutic response in advanced breast cancer

<p>Abstract</p> <p>Background</p> <p>Doppler ultrasonography (US) is increasingly being utilized as an imaging modality in breast cancer. It is used to study the vascular characteristics of the tumor. Neoadjuvant chemotherapy is the standard modality of treatment in locally advanced breast cancer. Histological examination remains the gold standard to assess the chemotherapy response. However, based on the color Doppler findings, a new scoring system that could predict histological response following chemotherapy is proposed.</p> <p>Methods</p> <p>Fifty cases of locally advanced infiltrating duct carcinoma of the breast were studied. The mean age of the patients was 44.5 years. All patients underwent clinical, Doppler and histopathological assessment followed by three cycles of CAF (Cyclophosphamide, Adriamycin and 5-Fluorouracil) chemotherapy, repeat clinical and Doppler examination and surgery. The resected specimens were examined histopathologically and histological response was correlated with Doppler findings. The Doppler characteristics of the tumor were graded as 1–4 for <25%, 25–50%, >50% and complete disappearance of flow signals respectively. A cumulative score was calculated and compared with histopathological response. Results were analyzed using Chi square test, sensitivity, specificity, positive and negative predictive values.</p> <p>Results</p> <p>The maximum Doppler score according to the proposed scoring system was twelve and minimum three. Higher scores corresponded with a more favorable histopathological response. Twenty four patients had complete response to chemotherapy. Sixteen of these 24 patients (66.7%) had a cumulative Doppler score more than nine. The sensitivity of cumulative score >5 was 91.7% and specificity was 38.5%. The area under the ROC curve of the cumulative score >9 was 0.72.</p> <p>Conclusion</p> <p>Doppler scoring can be accurately used to objectively predict the response to chemotherapy in patients with locally advanced breast cancer and it correlates well with histopathological response.</p

Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature

<p>Abstract</p> <p>Background</p> <p>Antigen-antibody interactions are key events in immune system, which provide important clues to the immune processes and responses. In Antigen-antibody interactions, the specific sites on the antigens that are directly bound by the B-cell produced antibodies are well known as B-cell epitopes. The identification of epitopes is a hot topic in bioinformatics because of their potential use in the epitope-based drug design. Although most B-cell epitopes are discontinuous (or conformational), insufficient effort has been put into the conformational epitope prediction, and the performance of existing methods is far from satisfaction.</p> <p>Results</p> <p>In order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including the impact of interior residues, different contributions of adjacent residues, and the imbalanced data which contain much more non-epitope residues than epitope residues. In order to address above issues, we take following strategies. Firstly, a concept of 'thick surface patch' instead of 'surface patch' is introduced to describe the local spatial context of each surface residue, which considers the impact of interior residue. The comparison between the thick surface patch and the surface patch shows that interior residues contribute to the recognition of epitopes. Secondly, statistical significance of the distance distribution difference between non-epitope patches and epitope patches is observed, thus an adjacent residue distance feature is presented, which reflects the unequal contributions of adjacent residues to the location of binding sites. Thirdly, a bootstrapping and voting procedure is adopted to deal with the imbalanced dataset. Based on the above ideas, we propose a new method to identify the B-cell conformational epitopes from 3D structures by combining conventional features and the proposed feature, and the random forest (RF) algorithm is used as the classification engine. The experiments show that our method can predict conformational B-cell epitopes with high accuracy. Evaluated by leave-one-out cross validation (LOOCV), our method achieves the mean AUC value of 0.633 for the benchmark bound dataset, and the mean AUC value of 0.654 for the benchmark unbound dataset. When compared with the state-of-the-art prediction models in the independent test, our method demonstrates comparable or better performance.</p> <p>Conclusions</p> <p>Our method is demonstrated to be effective for the prediction of conformational epitopes. Based on the study, we develop a tool to predict the conformational epitopes from 3D structures, available at <url>http://code.google.com/p/my-project-bpredictor/downloads/list</url>.</p