The field of biomedicine is experiencing a surge in the utilization of peptide-based therapeutics, which are characterized by their high degree of specificity and low toxicity profile. Despite the option of incorporating non-canonical residues, employing the twenty natural amino acids alone limits the chemical space, enabling a thorough in silico evaluation. With this purpose in mind, a dataset of all possible di-, tri-, and tetra-peptide combinations using the standard amino acid building blocks was previously published. Despite the prior limitations, the enhancement of computational capabilities now facilitates the thorough evaluation of all pentapeptides, comparable to the exhaustive scrutiny of cyclic peptides containing four or five amino acid units. We offer both the complete and pre-filtered sets of di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids, encompassing their homodetic cyclic counterparts (N-to-C-terminal). Screening against protein targets is readily facilitated by the use of FASTA sequence data, SMILES descriptors, and 3D structural libraries in SDF files. We, additionally, offer a basic technique and device for performing identity-based filtration. Improved access to this dataset will stimulate a more rapid adoption and utilization of small peptide screening procedures in drug discovery initiatives. As a demonstrative example, the developed library was screened against the SARS-CoV-2 main protease to pinpoint potential small peptide inhibitors.Enzyme Commission (EC) numbers are vital for accurate enzyme function and cellular metabolic understanding, linking protein sequences to the biochemical reactions they catalyze. Starting from fundamental principles, many computational approaches were introduced for the purpose of predicting enzyme commission numbers from input protein sequences. The prediction metrics, encompassing accuracy, recall, and precision, coupled with the practical application and operational efficiency of current methodologies, underwent a severe decline when applied to newly identified proteins, leaving significant room for improvement. Employing novel deep learning techniques, this paper introduces HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting enzyme commission (EC) numbers. The embedding core and learning core constitute HDMLF; the former leverages the cutting-edge protein language model for sequence embedding, while the latter performs EC number prediction. Employing a gated recurrent unit framework, HDMLF is developed for the multi-objective, multitasking prediction of EC numbers. Furthermore, an attention layer was incorporated to enhance the precision of EC predictions, and a greedy strategy was subsequently used to integrate and refine the final model. When compared to four representative methodologies, HDMLF maintains a consistent edge in performance. This results in a considerable 60% increase in accuracy and a 40% enhancement in F1 score over the existing state-of-the-art. A subsequent examination of tyrB, anticipated to counteract the deficiency of aspartate aminotransferase aspC, as outlined in a prior experimental study, showcases our model's capability to uncover instances of enzyme promiscuity. Ultimately, a web platform, ECRECer (https://ecrecer.biodesign.ac.cn), was developed, leveraging a fully serverless, cloud-based architecture, and offering an offline package for enhanced usability.Neural progenitor cell (NPC) based cell replacement therapy has demonstrated efficacy in treating ischemic stroke. Unfortunately, the therapeutic results are less than desired due to the homeostasis imbalance in the local microenvironment following ischemia. Acid-sensing ion channels 1a (ASIC1a), a subunit of proton-gated cation channels, can be activated by the common homeostatic imbalance of acidosis, found in the penumbra following ischemic stroke. However, the contribution of ASIC1a to the behavior of NPCs after an episode of ischemia is still unknown. ASIC1a, functional within NPCs' channels, was activated by extracellular acidification, as demonstrated by our results. bcl2 signaling Additional findings highlighted ASIC1a activation's inhibitory impact on NPC migration and neurogenesis, stemming from RhoA-dependent filopodia remodeling. Pharmacological or genetic interference with ASIC1a effectively reversed this effect. Live animal experiments indicated that the gene-silencing of ASIC1a spurred the migration of neural progenitor cells and neurogenesis in the penumbra, which translated into improved behavioral recovery from stroke. Afterwards, ASIC1a's gain of function partially canceled out this result. Furthermore, the administration of ASIC1a antagonist drugs, such as amiloride or Psalmotoxin 1, facilitated functional restoration by boosting NPC migration and neurogenesis. These findings together show targeting ASIC1a as a novel therapeutic strategy that promotes NPC migration toward the penumbra, facilitating lesion repair after ischemic stroke and potentially for other neurological disorders exhibiting niche acidosis.The importance of temperature sensing is undeniable in the areas of wearable healthcare, robotics/prosthetic devices, and noncontact physiological monitoring systems. The usual mechanical deformation processes, encompassing pressing, bending, and stretching, often lead to unwanted modifications in the feature size of temperature sensors' internal conductive network distribution, which critically impacts accuracy. Motivated by the transient receptor potential mechanism, which underlies biological thermoreceptors' precision in diverse skin conformations, we propose an MXene/Clay/poly(N-isopropylacrylamide) (PNIPAM) (MCP) hydrogel exhibiting high stretchability, a rapid response to stimuli, and insensitivity to deformation. Upon water discharge at the threshold temperature, a transformation of the inner conductive network from three dimensions to two triggers the dynamic spike response. Precise threshold temperature sensing (32°C) of MCP hydrogels is achieved through the thermosensitivity of PNIPAM, which is unaffected by mechanical deformation, allowing for consistent water discharge under varied conditions, such as pressing and 15% stretching. The applications of plant electronics for real-time surface temperature monitoring and skin electronics for human-machine communication were demonstrated as a proof of concept. The study of temperature-threshold perception within the complicated interplay of surface and mechanical factors is facilitated by our research.During liver resection and transplantation, hepatic ischemia-reperfusion (IR) injury frequently arises as a substantial clinical problem. Recent advancements in the understanding of hepatic IR injury's pathophysiology have not yet translated into effective interventions and treatments. Our analysis assessed the involvement of transient receptor potential melastatin 2 (TRPM2), a calcium-permeable, non-specific cation channel, in the pathogenesis of liver ischemia-reperfusion injury. Experimental results show that a decrease in TRPM2 expression ameliorated the consequences of IR exposure on the liver, reducing inflammation, dysfunction, and cell death in mice. Furthermore, RNA sequencing analysis revealed that TRPM2-induced IR injury arises through ferroptosis-associated pathways. The ferroptosis inducer, (1S,3R)-RSL3, consistently induced mitochondrial dysfunction in hepatocytes, an outcome that was reversed by a TRPM2 inhibitor. Interestingly, the TRPM2-driven calcium influx precipitated mitochondrial calcium accumulation through the mitochondrial Ca2+-selective uniporter. This upregulated arachidonate 12-lipoxygenase (ALOX12) expression, consequently resulting in mitochondrial lipid peroxidation during hepatic ischemia-reperfusion injury. Heavily influenced by the IR-related ferroptosis of the liver, its effect was markedly reduced using a TRPM2 inhibitor or calcium depletion strategies, both in test-tube and live-animal studies. The combined results underscore the pivotal contribution of TRPM2-mediated ferroptosis in hepatic ischemia-reperfusion (IR) injury, which is characterized by elevated Ca2+-triggered ALOX12 expression. This emphasizes the potential of TRPM2 inhibition as a therapeutic strategy for diseases arising from hepatic IR injury, including those that may occur during liver procedures like resection and transplantation.A person's voice production can be impaired during the early phase of laryngeal cancer. The study examined the interplay between Voice Handicap Index (VHI) and Voice-Related Quality of Life (V-RQOL) parameters in individuals with early-stage laryngeal cancer.This study included 27 patients from Amir-Alam hospital who had early-stage laryngeal cancer (T1, T2) and whose average age was 5,935,777 years. Following a laryngeal cancer diagnosis in its early stages, patients completed Persian translations of the VHI and V-RQOL questionnaires, administered by a laryngologist.The results for patients with early laryngeal cancer showed that the average total score for the VHI was 65,941,421, and the average score for the V-RQOL was 4,864,975%. These results highlight a rise in the VHI's total and subscale scores, accompanied by a decline in the VRQOL scores. The VHI and V-RQOL total scores displayed a Pearson correlation of -90, reflecting a strong inverse relationship. Consistently, a strong negative correlation was identified between the total and subscales of VH and VRQOL (r.The temperature scale descends from negative forty-six degrees to negative ninety degrees, illustrating a substantial drop in warmth.005).Voice-related quality of life was found to diminish in patients with laryngeal cancer exhibiting early tumor growth (stages T1 and T2), as per our findings. In patients with early laryngeal cancer, the highly negative correlation between VHI and V-RQOL scores suggests that either questionnaire can be substituted for the other, potentially streamlining voice clinic procedures and saving valuable time.