Solid dispersion-based pellet for colon delivery of tacrolimus through time- and pH-dependent layer coating: preparation, in vitro and in vivo studies

The intent of the present investigation is to develop and evaluate colon-specific coated tacrolimus solid dispersion pellet (SDP) that retards drug release in the stomach and small intestine but progressively releases in the colon. Tacrolimus-SDP was prepared by extrusion-spheronization technology and optimized by the micromeritic properties including flowability, friability, yields and dissolution rate. Subsequently, the pH-dependent layer (Eudragit L30D55) and time-dependent layer (Eudragit NE30D and L30D55) were coated on the SDP to form tacrolimus colon-specific pellets (CSP) using a fluidized bed coater. Under in vitro gradient pH environment, tacrolimus only released from CSP after changing pH to 6.8 and then quickly released in the phosphate buffer solution of pH 7.2. The Cmax of CSP was 195.68 ± 3.14 ng/mL at Tmax 4.5 ± 0.24 h where as in case of SDP, the Cmax was 646.16 ± 8.15 ng/mL at Tmax 0.5 ± 0.03 h, indicating the ability of CSP targeted to colon. The highest area under the curve was achieved 2479.58 ± 183.33 ng·h/mL for SDP, which was 2.27-fold higher than tacrolimus suspension. However, the best biodistribution performance was achieved from CSP. In conclusion, SDP combining of pH- and time-dependent approaches was suitable for targeted delivery of tacrolimus to colon

Learning to screen Glaucoma like the ophthalmologists

GAMMA Challenge is organized to encourage the AI models to screen the glaucoma from a combination of 2D fundus image and 3D optical coherence tomography volume, like the ophthalmologists

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Thanks to its impressive capabilities in all-round segmentation tasks and its prompt-based interface, SAM has sparked intensive discussion within the community. It is even said by many prestigious experts that image segmentation task has been "finished" by SAM. However, medical image segmentation, although an important branch of the image segmentation family, seems not to be included in the scope of Segmenting "Anything". Many individual experiments and recent studies have shown that SAM performs subpar in medical image segmentation. A natural question is how to find the missing piece of the puzzle to extend the strong segmentation capability of SAM to medical image segmentation. In this paper, instead of fine-tuning the SAM model, we propose Med SAM Adapter, which integrates the medical specific domain knowledge to the segmentation model, by a simple yet effective adaptation technique. Although this work is still one of a few to transfer the popular NLP technique Adapter to computer vision cases, this simple implementation shows surprisingly good performance on medical image segmentation. A medical image adapted SAM, which we have dubbed Medical SAM Adapter (MSA), shows superior performance on 19 medical image segmentation tasks with various image modalities including CT, MRI, ultrasound image, fundus image, and dermoscopic images. MSA outperforms a wide range of state-of-the-art (SOTA) medical image segmentation methods, such as nnUNet, TransUNet, UNetr, MedSegDiff, and also outperforms the fully fine-turned MedSAM with a considerable performance gap. Code will be released at: https://github.com/WuJunde/Medical-SAM-Adapter

Bactericidal synergism between phage endolysin Ply2660 and cathelicidin LL-37 against vancomycin-resistant Enterococcus faecalis biofilms

Antibiotic resistance and the ability to form biofilms of Enterococcus faecalis have compromised the choice of therapeutic options, which triggered the search for new therapeutic strategies, such as the use of phage endolysins and antimicrobial peptides. However, few studies have addressed the synergistic relationship between these two promising options. Here, we investigated the combination of the phage endolysin Ply2660 and the antimicrobial peptide LL-37 to target drug-resistant biofilm-producing E. faecalis. In vitro bactericidal assays were used to demonstrate the efficacy of the Ply2660–LL-37 combination against E. faecalis. Larger reductions in viable cell counts were observed when Ply2660 and LL-37 were applied together than after individual treatment with either substance. Transmission electron microscopy revealed that the Ply2660–LL-37 combination could lead to severe cell lysis of E. faecalis. The mode of action of the Ply2660–LL-37 combination against E. faecalis was that Ply2660 degrades cell wall peptidoglycan, and subsequently, LL-37 destroys the cytoplasmic membrane. Furthermore, Ply2660 and LL-37 act synergistically to inhibit the biofilm formation of E. faecalis. The Ply2660–LL-37 combination also showed a synergistic effect for the treatment of established biofilm, as biofilm killing with this combination was superior to each substance alone. In a murine peritoneal septicemia model, the Ply2660–LL-37 combination distinctly suppressed the dissemination of E. faecalis isolates and attenuated organ injury, being more effective than each treatment alone. Altogether, our findings indicate that the combination of a phage endolysin and an antimicrobial peptide may be a potential antimicrobial strategy for combating E. faecalis

Abalone Muscle Texture Evaluation and Prediction Based on TPA Experiment

The effects of different heat treatments on abalones’ texture properties and sensory characteristics were studied. Thermal processing of abalone muscle was analyzed to determine the optimal heat treatment condition based on fuzzy evaluation. The results showed that heat treatment at 85°C for 1 hour had certain desirable effects on the properties of the abalone meat. Specifically, a back propagation (BP) neural network was introduced to predict the equations of statistically significant sensory hardness, springiness, and smell using the texture data gained through TPA (texture profile analysis) experiments as input and sensory evaluation data as the desired output. The final outcome was that the predictability was proved to be satisfactory, with an average error of 6.93%

Assessment of fragility curve for steel frame construction under different categories of earthquakes

Cilj je ovog istraživanja ispitati učinak dviju kategorija potresnih događaja na krivulju oštetljivosti čelične okvirne konstrukcije zgrade (građevine s različitim brojem katova) razmatranjem relativnog bočnog pomaka kao kriterija oštećenosti. Kategorije za opisivanje promjene relativnog bočnog položaja bile su blaga, umjerena, značajna i potpuna. Kako se povećava seizmički zahtjev, veća je vjerojatnost prekoračenja. Drugim riječima, ako maksimalno ubrzanje potresa raste, povećava se vjerojatnost prekoračenja (eng. probability of exceeding - PoE). Povećanja vršnog ubrzanja tla (PGA) povećat će PoE trokatne konstrukcije pri značajnom oštećenju. Krivulje oštetljivosti druge kategorije pokazuju pomak iz stanja mirovanja (postupni porast) u stanje stajanja (nagli porast) u usporedbi s potresima prve klase kod modela s pet katova. Sve veći broj katova utječe na povećanje PoE pri značajnom i teškom oštećenju i povećava vjerojatnost prekoračenja. Model sa sedam katova u značajnom načinu ima 10 i 15,5 % PoE veći od modela s pet i tri kata. Međutim, u stanju potpune oštećenosti, PoE u modelu s pet katova je 6 i 7 puta veća nego u modelu sa sedam i tri kata. Stoga se može zaključiti da je s povećanjem broja katova porasala PoE, što se može bolje uočiti pri značajnom stupnju oštenja.The aim of this study is to investigate the effect of two categories of earthquake events on the fragility curves of steel building construction (structures with different number of stories) by considering relative lateral displacement as a damage criterion. The categories used to describe the change in the relative lateral position were chosen to be slight, moderate, extensive, and complete. Increased seismic demand increases the probability of exceeding. In other words, the greater the maximum earthquake acceleration, the higher is the probability of exceeding (PoE). In a 3-story structure, the increase in PGA increases the PoE of the structure at extensive failure levels. The fragility curves for the 2nd category earthquakes show a shift from the sleeping mode (gradual increase) to the standing state (rapid increase) compared to the 1st-class earthquakes in the 5-story model. Increasing the number of stories increases the PoE of extensive and large failures. The PoE of the extensive mode in the 7-story model was 10 and 15.5 % higher than that in the 5- and 3-story models, respectively. However, for the complete damage state, the PoE in the 5-story model was 6 and 7 % more than that in the 7- and 3-story models, respectively. Therefore, it can be concluded that increasing the number of stories increases the PoE, but this increase is more evident for the extensive failure level

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated $11\times$, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances (https://www.science.org/doi/10.1126/sciadv.adi1480

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes