Biotech

Biohacking, the self-directed application of biotechnology, digital tools, and lifestyle interventions, has rapidly converged with gut microbiome science to create adaptive, individualized, and minimally invasive precision-health paradigms. This narrative review integrates current evidence on diet-based modulation, microbial therapeutics (probiotics, prebiotics, postbiotics, and fecal microbiota transplantation), and synthetic-biology approaches (engineered strains and phage or synthetic consort

Molecular property prediction constitutes a cornerstone of drug discovery and materials science, necessitating models capable of disentangling complex structure-property relationships across diverse molecular modalities. Existing approaches frequently exhibit entangled representations--conflating structural, chemical, and functional factors--thereby limiting interpretability and transferability. Furthermore, conventional methods inadequately exploit complementary information from graphs, sequenc

Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and patient tumors. Rather than aiming to improve absolute in vitro prediction accuracy, this work examines whether explicitly separating representation learning from task supervision enables more sample-efficient adaptation of drug-response models to patient data under strong biological domain shift. We propose a staged tran

The dominant narrative of artificial intelligence development assumes that progress is continuous and that capability scales monotonically with model size. We challenge both assumptions. Drawing on punctuated equilibrium theory from evolutionary biology, we show that AI development proceeds not through smooth advancement but through extended periods of stasis interrupted by rapid phase transitions that reorganize the competitive landscape. We identify five such eras since 1943 and four epochs wi

Large language models (LLMs) suffer from proactive interference (PI): outdated information in the context window disrupts retrieval of current values. This interference degrades retrieval accuracy log-linearly as stale associations accumulate, a bottleneck that persists regardless of context length and resists prompt-engineering mitigations. Biological brains resolve an analogous challenge through sleep-dependent memory consolidation: synaptic downscaling, selective replay, and targeted forgetti

Traditional discriminative computer vision relies predominantly on static projections, mapping input features to outputs in a single computational step. Although efficient, this paradigm lacks the iterative refinement and robustness inherent in biological vision and modern generative modelling. In this paper, we propose Discriminative Flow Matching, a framework that reformulates classification and object detection as a conditional transport process. By learning a vector field that continuously t

Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT-based semantic retrieval and LLM-mediated interpretation for interactive s

Deep learning models can predict protein properties with unprecedented accuracy but rarely offer mechanistic insight or actionable guidance for engineering improved variants. When a model flags an antibody as unstable, the protein engineer is left without recourse: which mutations would rescue stability while preserving function? We introduce Manifold-Constrained Counterfactual Optimization for Proteins (MCCOP), a framework that computes minimal, biologically plausible sequence edits that flip a

The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either in physical fidelity without transparent reasoning, or in flexible reasoning without guarantees of chemical validity. This imbalance limits the reliability of artificial intelligence systems in real scientific design workflo

The alignment problem refers to concerns regarding powerful intelligences, ensuring compatibility with human preferences and values as capabilities increase. Current large language models (LLMs) show misaligned behaviors, such as strategic deception, manipulation, and reward-seeking, that can arise despite safety training. Gaining a mechanistic understanding of these failures requires empirical approaches that can isolate behavioral patterns in controlled settings. We propose that biological mis

Protein sequence design must balance designability, defined as the ability to recover a target backbone, with multiple, often competing, developability properties such as solubility, thermostability, and expression. Existing approaches address these properties through post hoc mutation, inference-time biasing, or retraining on property-specific subsets, yet they are target dependent and demand substantial domain expertise or careful hyperparameter tuning. In this paper, we introduce ProtAlign, a

Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into consideration. Traditional methods treat sequence and structure separately, limiting the ability to exploit the alignment between the structure and protein sequence embeddings. In this paper, we introduce a sequence structure contrastive alignment framework, which learns a shared embedding space where proteins are repres

High-Content Screening routinely generates massive volumes of cell painting images for phenotypic profiling. However, technical variations across experimental executions inevitably induce biological batch (bio-batch) effects. These cause covariate shifts and degrade the generalization of deep learning models on unseen data. Existing batch correction methods typically rely on additional prior knowledge (e.g., treatment or cell culture information) or struggle to generalize to unseen bio-batches.

Natural products (NPs) have long served as a cornerstone of drug discovery, yielding landmark therapeutics such as paclitaxel and artemisinin and providing sustained access to biologically relevant chemical space. Despite this legacy, NP-based discovery has gradually declined with the rise of synthetic chemistry and high-throughput screening, even as many contemporary "synthetic" drugs remain structurally inspired by natural scaffolds. Classical NP workflows-centered on phenotypic screening and

Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Bo

Bridging biological and artificial systems, intelligent interfaces drive the demand for flexible electronics that emulate the skin’s multifunctionality. However, achieving such multifunctionality in a compact, self-sustained form remains challenging, as multimodal sensors often rely on rigid materials, discrete components, and external power sources. Herein, this study presents a single-component poly(vinyl alcohol) hydrogel e-skin integrating thermogalvanic, piezoionic, and diffusion mechanisms

Background/Objectives: Periodontitis is a chronic inflammatory disease with marked inter-individual heterogeneity and well-established links to cardiometabolic and other systemic conditions. Conventional clinical diagnostics remain indispensable. However, they provide limited real-time insight into molecular activity and host-response biology. This review aimed to synthesize recent advances in point-of-care diagnostics and emerging molecular biomarkers relevant to periodontal disease and its sys

CRISPR-Cas genome-editing technologies have emerged as powerful tools for precise DNA and RNA modulation, offering promising therapeutic strategies for neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). This review critically evaluates current CRISPR/Cas applications in neurodegeneration, with emphasis on mechanistic insights, therapeutic outcomes, and translational feasibility. Preclinical a

Background: Contemporary stroke rehabilitation protocols traditionally emphasise session frequency and treatment duration over intervention intensity-yet emerging evidence suggests we may be preparing patients for therapeutic marathons when their brains demand neuroplastic sprints. Across neuroscientific, behavioural, and clinical domains, convergent data indicate that repetition density, metabolic load, engagement, and temporal compression-not cumulative minutes-constitute the biologically mean

Despite decades of study, large parts of the mammalian metabolome remain unexplored1. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains challenging2–4. Biochemical language models have transformed the interpretation of DNA, RNA and protein sequences, but have not yet had a comparable impact on unde

High-throughput proteomics is critical for understanding biological processes, enabling large-scale studies such as biomarker discovery and systems biology. However, current mass spectrometry technologies face limitations in speed, sensitivity, and scalability for analyzing large sample cohorts. The Thermo Scientific Orbitrap Astral Zoom mass spectrometer (MS) was developed to address these limitations by improving acquisition speed, ion utilization, and spectral processing, which are all essent

The severe shortage of donor organs and limitations of current disease models highlight the urgent need for transformative strategies in tissue engineering (TE) and regenerative medicine (RM). Bioprinting has emerged as a powerful approach for creating functional tissues and organs, yet current workflows remain labor-intensive, variable, and challenging to scale. The convergence of artificial intelligence (AI), advanced bioprinting technologies, robotics, biosensing, and cutting-edge biological

Precision healthcare is increasingly oriented toward the development of therapeutic strategies that are as individualized as the patients receiving them. Central to this paradigm shift is artificial intelligence (AI)-enabled multi-modal data integration, which consolidates heterogeneous data streams-including genomic, transcriptomic, proteomic, imaging, environmental, and electronic health record (EHR) data into a unified analytical framework. This integrative approach enhances early disease det

DNA methylation is a central epigenetic mechanism that mediates the interaction between nutritional exposures and gene regulation. Emerging evidence demonstrates that diet, bioactive compounds, genetic background, and lifestyle factors collectively shape the human methylome, influencing metabolic function, disease susceptibility, and biological aging. This review synthesizes current knowledge on the molecular and biochemical mechanisms of DNA methylation, the role of nutrients and dietary patter

Bacteriophage therapy, which employs bacterial viruses to selectively eliminate pathogenic bacteria, has re-emerged as a promising strategy in the face of increasing antimicrobial resistance. However, its widespread clinical implementation is constrained by concerns regarding safety, standardisation, and predictable efficacy. In this review, we examine the key role of genomics in transforming phage therapy from an empirical practice into a standardised and personalised modality of contemporary m

This paper posits that the most significant long-term existential risk to human civilization is not an acute technological or environmental catastrophe, but a chronic, systemic decay driven by the psychological and demographic consequences of a biologically capped lifespan. The entrenched expectation of mortality before 120 years fosters a condition of "temporal myopia," which cultivates cultural short-termism, consumerist nihilism, and demographic apathy. A critical and compounding aspect of th

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemed

Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future direct

The development of data-driven and targeted drug delivery systems is essential for advancing precision therapeutics. Despite substantial progress in nanocarrier development, conventional platforms continue to face major challenges in clinical translation due to biological complexity, off-target accumulation, and limited adaptability to dynamic physiological environments. The integration of nanoarchitectonics and artificial intelligence (AI) offers an advanced strategy for engineering delivery sy

Extracellular vesicle (EV)-based therapies have attracted considerable attention as a novel class of biologics with broad clinical potential. However, their clinical translation is impeded by the fragmented and rapidly evolving regulatory landscape, with significant disparities between the United States, European Union, and key Asian jurisdictions. In this review, we systematically analyze regional guidelines and strategic frameworks governing EV therapeutics, emphasizing critical hurdles in qua

ABSTRACT The progression of differentiated thyroid carcinoma (DTC) poses significant clinical challenges, especially in determining the optimal time for intervention. To capture early signals of disease progression, we employed an optimized dynamic network biomarker (DNB) method—a systems biology approach that detects abrupt molecular changes indicating a critical transition signal. This analysis revealed that Stage II marks a critical transition in the disease trajectory. We further developed a

Clinical pathogen diagnostics detect targets by qPCR (but with low sensitivity) or blood culturing (but time-consuming). Here we leverage a dual-stem-loop DNA amplifier to enhance non-specific collateral enzymatic cleavage of an oligonucleotide linker between a fluophore and its quencher by CRISPR-CasΦ, achieving ultrasensitive target detection. Specifically, the target pathogens are lysed to release DNA, which binds its complementary gRNA in CRISPR-CasΦ to activate the collateral DNA-cleavage c

Engineering biology applies synthetic biology to address global environmental challenges like bioremediation, biosequestration, pollutant monitoring, and resource recovery. This perspective outlines innovations in engineering biology, its integration with other technologies (e.g., nanotechnology, IoT, AI), and commercial ventures leveraging these advancements. We also discuss commercialisation and scaling challenges, biosafety and biosecurity considerations including biocontainment strategies, s

Cancer is a major global health concern. Therefore, new treatment options are needed. The phytochemicals have different chemical structures. It also exhibits several other biological activities. Therefore, these compounds are promising anticancer agents. This review aims to identify and assess new candidates for anticancer therapy. Researchers have identified these compounds among the well-studied plant chemicals and their actions. Thus, these compounds can be used in anticancer therapies. The p

Rates of cognitive decline in Alzheimer’s disease (AD) are extremely heterogeneous. Although biomarkers for amyloid-beta (Aβ) and tau proteins, the hallmark AD pathologies, have improved pathology-based diagnosis, they explain only 20–40% of the variance in AD-related cognitive impairment (CI). To discover novel biomarkers of CI in AD, we performed cerebrospinal fluid (CSF) proteomics on 3,397 individuals from six major prospective AD case–control cohorts. Synapse proteins emerged as the stronge

Phytochemicals are bioactive compounds found in plants that play a key role in promoting health and preventing diseases. Present in fruits, vegetables, grains, and seed oils, these compounds are considered safe for consumption due to the co-evolution and adaptation between mammals and plants. Due to their wide-ranging biological effects, they have attracted considerable research interest. This comprehensive review explores the mechanisms of action, health benefits, and applications of dietary ph

The demand for sensitive, rapid, and affordable diagnostic techniques has surged, particularly following the COVID-19 pandemic, driving the development of CRISPR-based diagnostic tools that utilize Cas effector proteins (such as Cas9, Cas12, and Cas13) as viable alternatives to traditional nucleic acid-based detection methods. These CRISPR systems, often integrated with biosensing and amplification technologies, provide precise, rapid, and portable diagnostics, making on-site testing without the

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules. This review explores AI applications in diagnostics, therapy, and drug dis

Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients acr