A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Methods: Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Results: Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Conclusion: Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

All-optical neuromorphic XOR and XNOR operation utilizing a photonic spiking neuron based on a passive add-drop microring resonator.

We propose a concise hardware architecture supporting efficient exclusive OR (XOR) and exclusive NOR (XNOR) operations, by employing a single photonic spiking neuron based on a passive add-drop microring resonator (ADMRR). The threshold mechanism and inhibitory dynamics of the ADMRR-based spiking neuron are numerically discussed on the basis of the coupled mode theory. It is shown that a precise XOR operation in the ADMRR-based spiking neuron can be implemented by adjusting temporal differences within the inhibitory window. Additionally, within the same framework, the XNOR function can also be carried out by accumulating the input power over time to trigger an excitatory behavior. This work presents a novel, to the best of our knowledge, and pragmatic technique for optical neuromorphic computing and information processing utilizing passive devices.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale and modular design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs composed of simple molecular structures. This strategy is based on a universal core unit with signal selection capability, and a timeadjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal crosscatalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for largescale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Understanding Macrophage-Tumor Interactions: Insights from Single-Cell Behavior Monitoring in a Sessile Microdroplet System.

Interaction between tumor-associated macrophages and tumor cells is crucial for tumor development, metastasis, and the related immune process. However, the macrophages are highly heterogeneous spanning from anti-tumorigenic to pro-tumorigenic, which needs to be understood at the single-cell level. Herein, a sessile microdroplet system designed for monitoring cellular behavior and analyzing intercellular interaction, demonstrated with macrophage-tumor cell pairs is presented. An automatic procedure based on the inkjet printing method is utilized for the precise pairing and co-encapsulation of heterotypic cells within picoliter droplets. The sessile nature of microdroplets ensures controlled fusion and provides stable environments conducive to adherent cell culture. The nitric oxide generation and morphological changes over incubation are explored to reveal the complicated interactions from a single-cell perspective. The immune response of macrophages under distinct cellular microenvironments is recorded. The results demonstrate that the tumor microenvironment displays a modulating role in polarizing macrophages from anti-tumorigenic into pro-tumorigenic phenotype. The approach provides a versatile and compatible platform to investigate intercellular interaction at the single-cell level, showing promising potential for advancing single-cell behavior studies.

Silver Copper Chalcogenide Thermoelectrics: Advance, Controversy, and Perspective.

Thermoelectric technology, which enables a direct and pollution-free conversion of heat into electricity, provides a promising path to address the current global energy crisis. Among the broad range of thermoelectric materials, silver copper chalcogenides (AgCuQ, Q = S, Se, Te) have garnered significant attention in thermoelectric community in light of inherently ultra-low lattice thermal conductivity, controllable electronic transport properties, excellent thermoelectric performance across various temperature ranges, and a degree of ductility. This review epitomizes the recent progress in AgCuQ-based thermoelectric materials, from the optimization of thermoelectric performance to the rational design of devices, encompassing the fundamental understanding of crystal structures, electronic band structures, mechanical properties, and quasi-liquid behaviors. The correlation between chemical composition, mechanical properties, and thermoelectric performance in this material system is also highlighted. Finally, several key issues and prospects are proposed targeting further optimizing AgCuQ-based thermoelectric materials. This article is protected by copyright. All rights reserved.

Evaluation of aroma characteristics of Penaeus vannamei with different drying methods using HS-SPME-GC-MS, MMSE-GC-MS, and sensory evaluation.

The effects of microwave drying (MD), hot air drying (HAD), vacuum hot air drying (VD), and vacuum freeze drying (VFD) on the volatile profiles of Penaeus vannamei were investigated. A total of 89 and 94 volatile compounds were identified by headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and monolithic material sorptive extraction gas chromatography-mass spectrometry (MMSE-GC-MS), respectively. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and variable influence on projection (VIP) models were utilized to select characteristic volatiles and key marker compounds (e.g., octanal, 1-octen-3-ol, 2-methyl-butanal, 2-ethyl-furan, and trimethyl-pyrazine) to discriminate among four drying methods. Based on synthesis of odor descriptions and sensory evaluation, it was found that P. vannamei via MD, HAD, and VD greatly reduced the fishy and generated roasted, fatty, and smoked odors. This study systematically analyzed the aroma characteristics of four traditional dried P. vannamei products, which may provide theoretical guidance for industrial production.

Uridine diphosphate glucosyltransferase is vital for fenpropathrin resistance in Bombyx mori (Lepidoptera).

The silkworm (Bombyx mori) is an important model lepidopteran insect and can be used to identify pesticide resistance-related genes of great significance for biological control of pests. Uridine diphosphate glucosyltransferases (UGTs), found in all organisms, are the main secondary enzymes involved in the metabolism of heterologous substances. However, it remains uncertain if silkworm resistance to fenpropathrin involves UGT. This study observes significant variations in BmUGT expression among B. mori strains with variable fenpropathrin resistance post-feeding, indicating BmUGT's role in fenpropathrin detoxification. Knockdown of BmUGT with RNA interference and overexpression of BmUGT significantly decreased and increased BmN cell activity, respectively, indicating that BmUGT plays an important role in the resistance of silkworms to fenpropathrin. In addition, fenpropathrin residues were significantly reduced after incubation for 12 h with different concentrations of a recombinant BmUGT fusion protein. Finally, we verified the conservation of UGT to detoxify fenpropathrin in Spodoptera exigua: Its resistance to fenpropathrin decreased significantly after knocking down SeUGT. In a word, UGT plays an important role in silkworm resistance to fenpropathrin by directly degrading the compound, a function seen across other insects. The results of this study are of great significance for breeding silkworm varieties with high resistance and for biological control of pests.

Effects of the In Situ Growth of CNTs on Ti-Coated Diamond Surfaces on the Mechanical Properties of Diamond/Aluminum Composites.

Diamond/aluminum composites have attracted significant attention as novel thermal management materials, with their interfacial bonding state and configuration playing a crucial role in determining their thermal conductivity and mechanical properties. The present work aims to evaluate the bending strength and thermal conductivity of CNT-modified Ti-coated diamond/aluminum composites with multi-scale structures. The Fe catalyst was encapsulated on the surface of Ti-coated diamond particles using the solution impregnation method, and CNTs were grown in situ on the surface of Ti-coated diamond particles using the plasma-enhanced chemical vapor deposition (PECVD) method. We investigated the influence of interface structure on the thermal conductivity and mechanical properties of diamond/aluminum composites. The results show that the CNT-modified Ti-coated diamond/aluminum composite exhibits excellent bending strength, reaching up to 281 MPa, compared to uncoated diamond/aluminum composites and Ti-coated diamond/aluminum composites. The selective bonding between diamond and aluminum was improved by the interfacial reaction between Ti and diamond particles, as well as between CNT and Al. This led to the enhanced mechanical properties of Ti-coated diamond/aluminum composites while maintaining acceptable thermal conductivity. This work provides insights into the interface's configuration design and the performance optimization of diamond/metal composites for thermal management.

Halloysite-derived mesoporous silica with high ionic conductivity improves Li-S battery performance.

The slow Li+ transport rate in the thick sulfur cathode of the Li-S battery affects its capacity and cycling performance. Herein, Fe-doped highly ordered mesoporous silica material (Fe-HSBA-15) as a sulfur carrier of the Li-S battery shows high ion conductivity (1.10 mS cm-1) and Li+ transference number (0.77). The Fe-HSBA-15/S cell has an initial capacity of up to 1216.7 mA h g-1 at 0.2C and good stability. Impressively, at a high sulfur load of 4.34 mg cm-2, the Fe-HSBA-15/S cell still maintains an area specific capacity of 4.47 mA h cm-2 after 100 cycles. This is because Fe-HSBA-15 improves the Li+ diffusion behavior through the ordered mesoporous structure. Theoretical calculations also confirmed that the doping of iron enhances the adsorption of polysulfides, reduces the band gap and makes the catalytic activity stronger.

Mulberry branch fiber improved lipid metabolism and egg yolk fatty acid composition of laying hens via the enterohepatic axis.

BACKGROUND: The utilization of mulberry branch fiber (MF), the largest by-product of the sericulture industry, is an important issue. Supplementation with MF as a dietary fiber for poultry may serve as a useful application. However, little is known about the effects of MF on liver lipid metabolism and egg yolk fatty acid composition of laying hens and their underlying mechanisms. In this study, we performed a multi-omics investigation to explore the variations in liver lipid metabolism, egg yolk fatty acid composition, gut microbiota, and the associations among them induced by dietary MF in laying hens. RESULTS: Dietary MF had no harmful effects on the laying performance or egg quality in laying hens. The enzyme activities associated with lipid metabolism in the liver were altered by the addition of 5% MF, resulting in reduced liver fat accumulation. Furthermore, dietary 5% MF induced the variation in the fatty acid profiles of egg yolk, and increased the polyunsaturated fatty acid (PUFA) content. We observed a significant reduction in the diversity of both gut bacteria and changes in their compositions after the addition of MF. Dietary MF significantly increased the abundance of genes involved in fatty acid biodegradation, and short-chain fatty acids biosynthesis in the gut microbiota of laying hens. The significant correlations were observed between the liver lipid metabolism enzyme activities of hepatic lipase, lipoprotein lipase, and total esterase with gut microbiota, including negative correlations with gut microbiota diversity, and multiple correlations with gut bacteria and viruses. Moreover, various correlations between the contents of PUFAs and monounsaturated fatty acids in egg yolk with the gut microbiota were obtained. Based on partial-least-squares path modeling integrated with the multi-omics datasets, we deduced the direct effects of liver enzyme activities and gut bacterial compositions on liver fat content and the roles of liver enzyme activities and gut bacterial diversity on egg yolk fatty acid composition. CONCLUSIONS: The results indicate that dietary MF is beneficial to laying hens as it reduces the liver fat and improves egg yolk fatty acid composition through the enterohepatic axis. Video Abstract.

From gut to brain: understanding the role of microbiota in inflammatory bowel disease.

With the proposal of the "biological-psychological-social" model, clinical decision-makers and researchers have paid more attention to the bidirectional interactive effects between psychological factors and diseases. The brain-gut-microbiota axis, as an important pathway for communication between the brain and the gut, plays an important role in the occurrence and development of inflammatory bowel disease. This article reviews the mechanism by which psychological disorders mediate inflammatory bowel disease by affecting the brain-gut-microbiota axis. Research progress on inflammatory bowel disease causing "comorbidities of mind and body" through the microbiota-gut-brain axis is also described. In addition, to meet the needs of individualized treatment, this article describes some nontraditional and easily overlooked treatment strategies that have led to new ideas for "psychosomatic treatment".

Nano-formulated delivery of active ingredients from traditional Chinese herbal medicines for cancer immunotherapy.

Cancer immunotherapy has garnered promise in tumor progression, invasion, and metastasis through establishing durable and memorable immunological activity. However, low response rates, adverse side effects, and high costs compromise the additional benefits for patients treated with current chemical and biological agents. Chinese herbal medicines (CHMs) are a potential treasure trove of natural medicines and are gaining momentum in cancer immunomodulation with multi-component, multi-target, and multi-pathway characteristics. The active ingredient extracted from CHMs benefit generalized patients through modulating immune response mechanisms. Additionally, the introduction of nanotechnology has greatly improved the pharmacological qualities of active ingredients through increasing the hydrophilicity, stability, permeability, and targeting characteristics, further enhancing anti-cancer immunity. In this review, we summarize the mechanism of active ingredients for cancer immunomodulation, highlight nano-formulated deliveries of active ingredients for cancer immunotherapy, and provide insights into the future applications in the emerging field of nano-formulated active ingredients of CHMs.

Trichoderma harzianum prevents red kidney bean root rot by increasing plant antioxidant enzyme activity and regulating the rhizosphere microbial community.

Root rot is one of the main reasons for yield losses of red kidney bean (Phaseolus vulgaris) production. Pre-inoculation with Trichoderma harzianum can effectively lower the incidence of red kidney bean root rot. In this study, four treatments including CK (control), Fu13 (Fusarium oxysporum), T891 (T. harzianum) and T891 + Fu13 (T. harzianum + F. oxysporum) were arranged in a pot experiment to investigate how T891 affected the incidence and severity of root rot, plant growth, and changes of defense enzyme activity in red kidney bean plants. Community composition and diversity of the rhizosphere microbiota was evaluated through high-throughput sequencing, and co-occurrence network was analyzed. The results showed that when compared to the Fu13 treatment, pre-inoculation with T891 reduced the incidence and severity of red kidney bean root rot by 40.62 and 68.03% (p < 0.05), increased the root length, shoot length, total dry biomass by 48.63, 97.72, 122.17%. Upregulated activity of super-oxide dismutase (SOD), peroxidase (POD), catalase (CAT) by 7.32, 38.48, 98.31% (p < 0.05), and reduced malondialdehyde (MDA) by 23.70% (p < 0.05), respectively. Microbiological analyses also showed that F. oxysporum reduced alpha diversity resulting in alteration the composition of the rhizosphere microbial community in red kidney bean. T891 significantly reduced abundance of F. oxysporum, allowing the enrichment of potentially beneficial bacteria Porphyrobacter (ASV 46), Lysobacter (ASV 85), Microbacteriaceae (ASV 105), and Gemmatimonas (ASV 107), resulting in a more stable structure of the microbial network. The results of random forest analysis further revealed that ASV 46 (Porphyrobacter) was the primary influencing factor for the incidence of root rot after inoculation with T891, while ASV 85 (Lysobacter) was the primary influencing factor for the biomass of red kidney bean. In conclusion, T. harzianum promotes the growth of red kidney bean and inhibits root rot by improving plant antioxidant enzyme activity and regulating the rhizosphere microbial community.

A microporous bismuth-based MOF for efficient separation of acetylene from carbon dioxide.

The separation of acetylene from carbon dioxide is challenging due to their almost identical molecular sizes and volatilities. Metal-organic frameworks (MOFs) in general are strong candidates for the separation of gas mixtures owing to the presence of functional pore surfaces that can selectively capture specific target molecules. Herein, we report a stable and easily synthesized bismuth-based MOF, Bi-BTC, which can achieve the separation of acetylene and carbon dioxide. We performed a detailed analysis of the sorption properties of the Bi-MOF. Bi-BTC shows good adsorption capacities for C2H2 with a capacity of 53.8 cm3 g-1 at 298 K and 1.0 bar, and C2H2/CO2 selectivity of 5.14/7.69 at 298 K and 1.0/0.1 bar. IAST selectivity calculations indicate that Bi-BTC possesses good separation capacity, and dynamic breakthrough experiments were performed to prove the separation of C2H2 and CO2. Bi-MOFs as a group of relatively less studied types of MOFs have interesting adsorption characteristics, and this study on Bi-based MOF will enrich three-dimensional Bi-MOF adsorbents for gas adsorption and separation applications.

Minimizing Voltage Losses via Synergistically Reducing Hetero-Interface Energy Offset for High Efficiency Perovskite Solar Cells.

The open circuit voltage (VOC) losses at multiple interfaces within perovskite solar cells (PSCs) limit the improvements in power conversion efficiency (PCE). Herein, a tailored strategy is proposed to reduce the energy offset at both hetero-interfaces within PSCs to decrease the VOC losses. For the interface of perovskite and electron transport layer where exists a mass of defects, it uses the pyromellitic acid to serve as a molecular bridge, which reduces non-radiative recombination and energy level offset. For the interface of perovskite and hole transport layer, which includes a passivator of PEAI, the detrimental effect (negative shift of work function) of PEAI passivation and optimizing the interface energy level alignment are neutralized by incorporating (2-(4-(bis(4-methoxyphenyl)amino)phenyl)-1-cyanovinyl)phosphonic acid. Owing to synergistically reduced hetero-interface energy offset, the PSCs achieve a PCE of 25.13%, and the VOC is increased from 1.134 to 1.174 V. In addition, the resulting PSCs possess enhanced stability, the unencapsulated PSCs can maintain ≈96% and ≈97% of their initial PCE after 2000 h of aging under ambient conditions and 210 h under operation conditions.

Screening, identification, and mechanism analysis of starch-degrading bacteria during curing process in tobacco leaf.

Tobacco, a vital economic crop, had its quality post-curing significantly influenced by starch content. Nonetheless, the existing process parameters during curing were inadequate to satisfy the starch degradation requirements. Microorganisms exhibit inherent advantages in starch degradation, offering significant potential in the tobacco curing process. Our study concentrated on the microbial populations on the surface of tobacco leaves and in the rhizosphere soil. A strain capable of starch degradation, designated as BS3, was successfully isolated and identified as Bacillus subtilis by phylogenetic tree analysis based on 16SrDNA sequence. The application of BS3 on tobacco significantly enhanced enzyme activity and accelerated starch degradation during the curing process. Furthermore, analyses of the metagenome, transcriptome, and metabolome indicated that the BS3 strain facilitated starch degradation by regulating surface microbiota composition and affecting genes related to starch hydrolyzed protein and key metabolites in tobacco leaves. This study offered new strategies for efficiently improving the quality of tobacco leaves.

A Thermostable Cas12b-Powered Bioassay Coupled with Loop-Mediated Isothermal Amplification in a Customized "One-Pot" Vessel for Visual, Rapid, Sensitive, and On-Site Detection of Genetically Modified Crops.

Genetically modified crops (GMCs) have been discussed due to unknown safety, and thus, it is imperative to develop an effective detection technology. CRISPR/Cas is deemed a burgeoning technology for nucleic acid detection. Herein, we developed a novel detection method for the first time, which combined thermostable Cas12b with loop-mediated isothermal amplification (LAMP), to detect genetically modified (GM) soybeans in a customized one-pot vessel. In our method, LAMP-specific primers were used to amplify the cauliflower mosaic virus 35S promoter (CaMV35S) of the GM soybean samples. The corresponding amplicons activated the trans-cleavage activity of Cas12b, which resulted in the change of fluorescence intensity. The proposed bioassay was capable of detecting synthetic plasmid DNA samples down to 10 copies/µL, and as few as 0.05% transgenic contents could be detected in less than 40 min. This work presented an original detection method for GMCs, which performed rapid, on-site, and deployable detection.

GPCR-mediated regulation of beige adipocyte formation: Implications for obesity and metabolic health.

Obesity and its associated complications pose a significant burden on health. The non-shivering thermogenesis (NST) and metabolic capacity properties of brown adipose tissue (BAT), which are distinct from those of white adipose tissue (WAT), in combating obesity and its related metabolic diseases has been well documented. However, beige adipose tissue, the third and relatively novel type of adipose tissue, which emerges in extensive presence of WAT and shares similar favorable metabolic properties with BAT, has garnered considerable attention in recent years. In this review, we focused on the role of G protein-coupled receptors (GPCRs), the largest receptor family and the most successful class of drug targets in humans, in the induction of beige adipocytes. More importantly, we highlight researchers' clinical treatment attempts to ameliorate obesity and other related metabolic diseases through the formation and activation of beige adipose tissue. In summary, this review provides valuable insights into the formation of beige adipose tissue and the involvement of GPCRs, based on the latest advancements in scientific research.

Mitochondria-associated membranes contribution to exercise-mediated alleviation of hepatic insulin resistance: Contrasting high-intensity interval training with moderate-intensity continuous training in a high-fat diet mouse model.

OBJECTIVE: Mitochondria-associated membranes (MAMs) serve pivotal functions in hepatic insulin resistance (IR). Our aim was to explore the potential role of MAMs in mitigating hepatic IR through exercise and to compare the effects of different intensities of exercise on hepatic MAMs formation in high-fat diet (HFD) mice. METHODS: Male C57BL/6J mice were fed an HFD and randomly assigned to undergo supervised high-intensity interval training (HIIT) or moderate-intensity continuous training (MICT). IR was evaluated using the serum triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-C), glucose tolerance test (GTT), and insulin tolerance test (ITT). Hepatic steatosis was observed using hematoxylin-eosin (H&E) and oil red O staining. The phosphatidylinositol 3-kinase/protein kinase B/glycogen synthase kinase 3 beta (PI3K-AKT-GSK3ß) signaling pathway was assessed to determine hepatic IR. MAMs were evaluated through immunofluorescence (colocalization of voltage-dependent anion-selective channel 1 [VDAC1] and inositol 1,4,5-triphosphate receptor [IP3R]). RESULTS: After 8 weeks on an HFD, there was notable inhibition of the hepatic PI3K/Akt/GSK3ß signaling pathway, accompanied by a marked reduction in hepatic IP3R-VDAC1 colocalization levels. Both 8-week HIIT and MICT significantly enhanced the hepatic PI3K/Akt/GSK3ß signaling and colocalization levels of IP3R-VDAC1 in HFD mice, with MICT exhibiting a stronger effect on hepatic MAMs formation. Furthermore, the colocalization of hepatic IP3R-VDAC1 positively correlated with the expression levels of phosphorylation of protein kinase B (p-AKT) and phosphorylation of glycogen synthase kinase 3 beta (p-GSK3ß), while displaying a negative correlation with serum triglyceride/high-density lipoprotein cholesterol levels. CONCLUSION: The reduction in hepatic MAMs formation induced by HFD correlates with the development of hepatic IR. Both HIIT and MICT effectively bolster hepatic MAMs formation in HFD mice, with MICT demonstrating superior efficacy. Thus, MAMs might wield a pivotal role in exercise-induced alleviation of hepatic IR.

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding.

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.