Search

The COVID-19 global pandemic exposed healthcare system vulnerabilities, further endangering patient safety. This article explores perspectives on resilience and patient safety among healthcare workers using data from the Taiwan Patient Safety Culture Survey (TPSCS) and implementing Patient Safety Leadership Walkrounds (PSLWs) at E-Da Hospital. In 2021 and 2022, 1340 and 1114 staff members from clinical departments completed TPSCS questionnaires, respectively. 89 leaders from clinical departments participated in PSLWs in 2022. Among the four job categories, scores of Safety Attitude Questionnaire (SAQ) were lower among nurses and medical technicians, while perceptions of resilience and work-life balance were lowest among nurses. Between the two-year surveys, nurses exhibited significant decreases in SAQ in 2022 compared to 2021, while perceptions of work condition and work-life balance significantly declined among pharmacists. Resilience perception significantly decreased among all job categories except physicians during the pandemic. Mediation analyses showed teamwork climate, job satisfaction, management, work condition, and work-life balance were directly associated with safety climate, while resilience acts as a mediator, indirectly potentiating these relationships. Through PSLWs, we identified concerns about patient safety, including workforce, systems, processes, equipment, and work environment. Among these, workforce shortages and unsatisfactory pay emerged as the most pressing challenges. Strong leadership was recognized as a crucial factor in enhancing resilience and patient safety. This study suggests that TPSCS and PSLWs are worth regularly promoting among hospital institutions. Additionally, our findings highlight the urgency of healthcare organizations and governmental agencies to undertake policy reforms to improve healthcare workers’ well-being.

Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://pcnas.life.nthu.edu.tw/SeqCP/.

Abstract Background Human protein kinases, the key players in phosphoryl signal transduction, have been actively investigated as drug targets for complex diseases such as cancer, immune disorders, and Alzheimer’s disease, with more than 60 successful drugs developed in the past 30 years. However, many of these single-kinase inhibitors show low efficacy and drug resistance has become an issue. Owing to the occurrence of highly conserved catalytic sites and shared signaling pathways within a kinase family, multi-target kinase inhibitors have attracted attention. Results To design and identify such pan-kinase family inhibitors (PKFIs), we proposed PKFI sets for eight families using 200,000 experimental bioactivity data points and applied a graph convolutional network (GCN) to build classification models. Furthermore, we identified and extracted family-sensitive (only present in a family) pre-moieties (parts of complete moieties) by utilizing a visualized explanation (i.e., where the model focuses on each input) method for deep learning, gradient-weighted class activation mapping (Grad-CAM). Conclusions This study is the first to propose the PKFI sets, and our results point out and validate the power of GCN models in understanding the pre-moieties of PKFIs within and across different kinase families. Moreover, we highlight the discoverability of family-sensitive pre-moieties in PKFI identification and drug design.

Abstract Background While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. Results In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. Conclusion With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.

Abstract Linezolid has been reported to be associated with thrombocytopenia. However, limited information is available on susceptibility to thrombocytopenia after linezolid usage. We aimed to investigate the risk factors for linezolid-associated thrombocytopenia (LAT). We conducted a retrospective cohort study of patients aged ≥ 18 years who received linezolid for ≥ 5 d during hospitalization in 2019. Information was extracted from electronic medical records. Thrombocytopenia was defined as a platelet count of

Abstract Background Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). Results In this study, we used “merged moiety-based interpretable features (MMIFs),” which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. Conclusions In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.

In this study, an advanced 2.3D solution was proposed to integrate RDL interposer and FR-4 substrate using the technique of hybrid soldering, which applies printing of epoxy solder paste to form the hybrid joint. The simulation was performed to evaluate warpage and von Mises stress of different joint structures, indicating that the hybrid joint structure is promising for the reliability of 2.3D assembly. In addition, hybrid soldering 2.3D assembly was successfully demonstrated, followed by SEM analyses, electrical measurements, warpage measurements, and thermal cycling tests for 5000 cycles. The experimental results validate that the packaging can achieve high reliability without the needs of underfill. Thus, this solution paves the way for large area 2.3D integration with low cost, high throughput, and high reliability.

Abstract Background This work aims to help develop new protein engineering techniques based on a structural rearrangement phenomenon called circular permutation (CP), equivalent to connecting the native termini of a protein followed by creating new termini at another site. Although CP has been applied in many fields, its implementation is still costly because of inevitable trials and errors. Results Here we present CirPred, a structure modeling and termini linker design method for circularly permuted proteins. Compared with state-of-the-art protein structure modeling methods, CirPred is the only one fully capable of both circularly-permuted modeling and traditional co-linear modeling. CirPred performs well when the permutant shares low sequence identity with the native protein and even when the permutant adopts a different conformation from the native protein because of three-dimensional (3D) domain swapping. Linker redesign experiments demonstrated that the linker design algorithm of CirPred achieved subangstrom accuracy. Conclusions The CirPred system is capable of (1) predicting the structure of circular permutants, (2) designing termini linkers, (3) performing traditional co-linear protein structure modeling, and (4) identifying the CP-induced occurrence of 3D domain swapping. This method is supposed helpful for broadening the application of CP, and its web server is available at http://10.life.nctu.edu.tw/CirPred/ and http://lo.life.nctu.edu.tw/CirPred/ .

BackgroundDrug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease.MethodsHere, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 μM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms.ResultsOur in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals.ConclusionsWe demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Background: Fibromyalgia (FM) is characterized by complex pain symptoms lacking impersonal considerations in diagnosis and treatment evaluation, which often happens in women. Chronic and persistent widespread pain is the key symptom disturbing patients with FM, leading to depression, obesity, and sleep disturbances. Toll-like receptor 4 (TLR4) activation produces a harmful sensory input involved in central pain; this is the focus of this study. Electroacupuncture (EA) has beneficial effects in reducing FM pain, but its connection with TLR4 signaling is still unknown. Methods: Intermittent cold stress significantly induced mechanical and thermal pain. EA, but not sham EA, reliably attenuated mechanical and thermal hyperalgesia. The increased inflammatory mediators in FM mice were reduced in the EA group, but not in the sham group. Results: All TLR4 and related molecule levels increased in the FM mice’s hypothalamus, periaqueductal gray (PAG), and cerebellum. These increases could be attenuated by EA but not sham stimulation. Activation of TLR4 by lipopolysaccharide (LPS) significantly induced FM and can be further reversed by a TLR4 antagonist. Conclusions: These mechanisms provide evidence that the analgesic effect of EA is related to the TLR4 pathway. In addition, we showed that inflammation can activate the TLR4 pathway and provided new possible therapeutic targets for FM pain.

Against the background of the widespread application of various electronic devices and communication technologies, there is great concern regarding the problem of excessive radiation of electromagnetic waves with regard to electromagnetic interference, environmental pollution, and human health. Microwave-absorbing materials (MAMs) can transform electromagnetic energy into heat or dissipate electromagnetic waves via interference. Numerous theoretical and experimental studies have focused on the prevention of electromagnetic pollution and other related problems. Magnetite (Fe3O4) is considered one of the most promising MAMs because of its excellent properties, such as high saturation magnetization, high Curie temperature, and low cost. However, the single Fe3O4 has the disadvantages of weak dielectric loss and easy oxidation, thereby limiting its application in the field of microwave absorption. Fabrication of Fe3O4-based nanocomposites is an effective solution for these problems. In this study, a new type of Fe3O4@SnO2 composite similar to myrica rubra (Chinese bayberry) was synthesized by the Stöber method and hydrothermal method using magnetic Fe3O4 microspheres as template. The phase structure, surface elements, micromorphology, magnetic properties, and microwave absorption properties of the samples were characterized by means of X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and transmission electron microscopy and by observations based on a vibrating-sample magnetometer and vector network analyzer. The results show that the diameter of the myrica rubra-like Fe3O4@SnO2 sphere is about 500 nm, without obvious agglomeration, and that it has good morphological uniformity. The SnO2 layer is composed of nano-SnO2 particles, which are loosely stacked. The layer possesses many porous structures and is about 40 nm thick. The myrica rubra-like Fe3O4@SnO2 has strong dielectric loss capacity, is conducive to improving impedance matching performance, and exhibits good electromagnetic wave absorption capacity. When the thickness is 1.4–2.8 mm, RL(min) exceeds −20 dB. The optimum thickness is 1.7 mm, RL(min) is −29 dB, and the effective bandwidth is 4.9 GHz (13.1–18 GHz). It is a potential-absorbing material.

In this paper we generalize the quantum Brownian motion (QBM) to include the momentum-dependent system-environment couplings. The resulting Hamiltonian in the particle number representation is given by H_{t}ot=ℏω_{S}a^{†}a+∑_{k}ℏω_{k}b_{k}^{†}b_{k}+∑_{k}ℏ(V_{k}a^{†}b_{k}+V_{k}^{*}b_{k}^{†}a)+∑_{k}ℏ(W_{k}a^{†}b_{k}^{†}+W_{k}^{*}b_{k}a). The conventional QBM model corresponds to the special case W_{k}=V_{k}. The generalized QBM can capture more physical applications because the single-particle transition and the two-particle pair production between the system and the environment represent two very different physical processes that usually do not have the same coupling strengths. We discuss the physical realizations of the generalized QBM in different physical systems, and derive its exact master equation for both the initial decoupled states and initial correlated states. The Hu-Paz-Zhang master equation of the conventional QBM model is reproduced as a special case. We find that the renormalized system Hamiltonian after tracing out all the environmental states induced naturally a momentum-dependent potential, which shows the consistency of including the momentum-dependent coupling in the QBM Hamiltonian. In the Hu-Paz-Zhang master equation, such a renormalized potential is misplaced so that the correct renormalization Hamiltonian has not been found. With the exact master equation for both the initial decoupled and initial correlated states, the issues about the initial jolt are also reexamined. We find that the so-called “initial jolt,” which has been thought to be an artificial effect due to the use of the initial decoupled system-environment states, has nothing to do with the initial decoupled state. The exact master equation for the generalized QBM also has potential applications to photonics quantum computing.

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing

Abstract Background Inflammation has been found to be associated with many neurodegenerative diseases, including Parkinson’s and dementia. Attenuation of microglia-induced inflammation is a strategy that impedes the progression of neurodegenerative diseases. Methods We used lipopolysaccharide (LPS) to simulate murine microglia cells (BV2 cells) as an experimental model to mimic the inflammatory environment in the brain. In addition, we examined the anti-inflammatory ability of corylin, a main compound isolated from Psoralea corylifolia L. that is commonly used in Chinese herbal medicine. The production of nitric oxide (NO) by LPS-activated BV2 cells was measured using Griess reaction. The secretion of proinflammatory cytokines including tumor necrosis factor (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) by LPS-activated BV2 cells was analyzed using enzyme-linked immunosorbent assay (ELISA). The expression of inducible NO synthase (iNOS), cyclooxygenase-2 (COX-2), nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3), apoptosis-associated speck-like protein containing a caspase-activation and recruitment domain (ASC), caspase-1, IL-1β and mitogen-activated protein kinases (MAPKs) in LPS-activated BV2 cells was examined by Western blot. Results Our experimental results demonstrated that corylin suppressed the production of NO and proinflammatory cytokines by LPS-activated BV2 cells. In addition, corylin inhibited the expression of iNOS and COX-2, attenuated the phosphorylation of ERK, JNK and p38, decreased the expression of NLRP3 and ASC, and repressed the activation of caspase-1 and IL-1β by LPS-activated BV2 cells. Conclusion Our results indicate the anti-inflammatory effects of corylin acted through attenuating LPS-induced inflammation and inhibiting the activation of NLRP3 inflammasome in LPS-activated BV2 cells. These results suggest that corylin might have potential in treating brain inflammation and attenuating the progression of neurodegeneration diseases.

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81–86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4–5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84–87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

The development of fan-out packaging technology for fine-pitch and high-pin-count applications is a hot topic in semiconductor research. To reduce the package footprint and improve system performance, many applications have adopted packaging-on-packaging (PoP) architecture. Given its inherent characteristics, glass is a good material for high-speed transmission applications. Therefore, this study proposes a fan-out wafer-level packaging (FO-WLP) with glass substrate-type PoP. The reliability life of the proposed FO-WLP was evaluated under thermal cycling conditions through finite element simulations and empirical calculations. Considering the simulation processing time and consistency with the experimentally obtained mean time to failure (MTTF) of the packaging, both two- and three-dimensional finite element models were developed with appropriate mechanical theories, and were verified to have similar MTTFs. Next, the FO-WLP structure was optimized by simulating various design parameters. The coefficient of thermal expansion of the glass substrate exerted the strongest effect on the reliability life under thermal cycling loading. In addition, the upper and lower pad thicknesses and the buffer layer thickness significantly affected the reliability life of both the FO-WLP and the FO-WLP-type PoP.

Introduction and aim. Oral manifestation of the disorder is the leading cause of common initial features of most autoimmune diseases. Therefore, this study aimed to present different oral manifestations of selected autoimmune diseases. Material and methods. We systematically reviewed the etiology, signs and symptoms, oral manifestations, epidemiology, diagnosis, treatment plan, and prognosis. We searched the articles on PubMed, Google Scholar and Web of Science for the following search term: Behcet's disease, lichen planus, mucous membrane pemphigoid and bullous pemphigoid, pemphigus vulgarisms and paraneoplastic pemphigus, rheumatoid arthritis, Sjögren's syndrome, IgG4-related disease, systemic lupus erythematous, and granulomatosis with polyangitis. Analysis of the literature. We conducted that the disorder's oral manifestation causes most autoimmune illnesses' earliest symptoms. Conclusion. Clinical-pathological is a piece of requisite knowledge for the dentist to recognize and diagnose in the early phase of the symptoms. [ABSTRACT FROM AUTHOR]