Phani Paladugu

Muscle weakness after immobilization often exceeds that explained by loss of muscle mass alone, suggesting a role for neuromuscular synaptic changes. To quantify these adaptations, we developed a composite transcriptomic Neuromuscular Junction (NMJ) Remodeling Score and evaluated its behavior relative to classical atrophy pathways during short-term unloading. We analyzed vastus lateralis RNA sequencing data from adults undergoing 10 days of unilateral lower-limb suspension followed by a 21-day recovery, generating NMJ and atrophy scores for 15 and 10 genes, respectively. Transcriptome-wide testing across more than twenty thousand genes identified a broad pattern of metabolic suppression. The NMJ score showed a large effect increase during unloading and partial normalization with recovery, while the atrophy score rose more strongly and reversed during recovery. The two scores demonstrated weak correlation, consistent with distinct biological processes. Individual NMJ-related genes displayed coordinated regulation, including marked upregulation of several acetylcholine receptor subunits and modest downregulation of muscle signaling kinase (MuSK), reflecting a denervation-like transcriptional pattern. Directional replication in a 60-day bed rest cohort supported generalizability across disuse conditions. Together, these findings indicate that limb unloading elicits measurable transcriptomic remodeling at the NMJ that is only partially aligned with atrophy signaling, providing a framework for investigating neural contributions to immobilization-induced weakness.

AI and ML in spine surgery has both transformative possibilities and notable challenges related to regulatory oversight, algorithmic bias, and clinical responsibility. We propose a governance model to tackle these important issues, ensuring the responsible use of AI tools. This framework introduces the SAIGE-R Index, a tool designed to measure AI system risks based on Clinical Volatility, System Integration Risk, and Data Integrity Confidence. This index supports a tiered oversight system, ranging from minimal checks for low-risk systems to thorough FDA reviews for high-risk applications. In addition, SAIGE sets specific validation standards focused on spine surgery outcomes. These include important differences in patient-reported measures and accuracy in pedicle screw placement, along with quarterly fairness checks to reduce demographic bias. The framework also describes a strong governance structure that focuses on ongoing clinician training, involvement from multiple stakeholders, and strict data security measures. It suggests a liability model that matches responsibility with the evaluated risk level of AI tools. By addressing validation, ethics, and accountability, the SAIGE Framework provides a foundation for safely and effectively incorporating AI into complex surgical settings. This approach encourages innovation while maintaining patient safety and clinical integrity.

Hypertension and osteoporosis are prevalent chronic conditions that frequently coexist, particularly in aging populations. Increasing evidence indicates that they share interconnected pathophysiological pathways involving calcium metabolism, chronic inflammation, oxidative stress, and hormonal regulation. Diet is a critical adjustable factor that influences the onset and progression of both conditions. This review evaluates the impact of dietary modification, including adherence to the Dietary Approaches to Stop Hypertension (DASH) diet, the Mediterranean diet, sodium restriction, and targeted nutrient intake (e.g., calcium, vitamin D, potassium, and magnesium), on bone mineral density (BMD) in individuals with treated and untreated hypertension. While the DASH diet effectively reduces blood pressure, its potential to lower BMD in the absence of adequate calcium intake warrants caution. In contrast, the Mediterranean diet provides broader nutritional support for both vascular and skeletal health through its anti-inflammatory and antioxidant profile. Diets rich in dairy products, minerals, and bioactive compounds are associated with improved BMD and vascular function. This review combines current evidence, explains mechanistic links between diet, bone, and cardiovascular physiology, and offers evidence-based dietary recommendations to address the coexisting conditions of hypertension and osteoporosis.

Spinal health depends on the dynamic interplay between mechanical forces, biochemical signaling, and cellular behavior. This review explores how key molecular pathways, including integrin, yeas-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), Piezo, and Wingless/Integrated (Wnt) with β-catenin, actively shape the structural and functional integrity of spinal tissues. These signaling mechanisms respond to physical cues and interact with inflammatory mediators such as interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), driving changes that lead to disc degeneration, vertebral fractures, spinal cord injury, and ligament failure. New research is emerging that shows scaffold designs that can directly harness these pathways. Further, new stem cell-based therapies have been shown to promote disc regeneration through targeted differentiation and paracrine signaling. Interestingly, many novel bone and ligament scaffolds are modulating anti-inflammatory signals to enhance tissue repair and integration, as well as prevent scaffold degradation. Neural scaffolds are also arising. These mimic spinal biomechanics and activate Piezo signaling to guide axonal growth and restore motor function. Scientists have begun combining these biological platforms with brain–computer interface technology to restore movement and sensory feedback in patients with severe spinal damage. Although this technology is not fully clinically ready, this field is advancing rapidly. As implantable technology can now mimic physiological processes, molecular signaling, biomechanical design, and neurotechnology opens new possibilities for restoring spinal function and improving the quality of life for individuals with spinal disorders.

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases.

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care.

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl-sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann-Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review positions CSF cfDNA epigenetic analysis as a transformative liquid biopsy modality with the potential to enable earlier, more precise, and personalized diagnosis and disease monitoring in neurometabolic conditions.

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide.

Advancements in load-bearing tissue repair increasingly demand biomaterials that not only support structural integrity but also interact dynamically with the physiological environment. This review examines the latest progress in smart biomaterials designed for skeletal reconstruction, with emphasis on mechanoresponsive scaffolds, bioactive composites, and integrated microsensors for real-time monitoring. We explore material formulations that enhance osseointegration, resist micromotion-induced loosening, and modulate inflammatory responses at the bone–implant interface. Additionally, we assess novel fabrication methods—such as additive manufacturing and gradient-based material deposition—for tailoring stiffness, porosity, and degradation profiles to match host biomechanics. Special attention is given to sensor-augmented platforms capable of detecting mechanical strain, biofilm formation, and early-stage implant failure. Together, these technologies promise a new class of bioresponsive, diagnostic-capable constructs that extend beyond static support to become active agents in regenerative healing and post-operative monitoring. This multidisciplinary review integrates insights from materials science, mechanobiology, and device engineering to inform the future of implantable systems in skeletal tissue repair.

Spine-focused biomedical informatics has emerged as a critical frontier for applying artificial intelligence (AI) to improve diagnosis, prognosis, and intervention in complex musculoskeletal and neurodegenerative conditions. This review synthesizes recent developments in the integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) within spine health ecosystems. We highlight convolutional neural networks for vertebral segmentation and disc classification, radiomic pipelines for quantitative MRI/CT biomarker extraction, and multimodal ML models for outcome prediction across degenerative, neoplastic, and traumatic pathologies. We examine spine-specific clinical decision support systems (CDSSs) that fuse structured EHR data with NLP-parsed reports, wearable biomechanics, and genomic signals to generate real-time, risk-adaptive treatment guidance. Emphasis is placed on explainable AI (XAI) frameworks—e.g., SHAP, LIME, Grad-CAM—that support interpretability and ethical accountability in AI-augmented decision-making. We analyze challenges in generalizability, privacy-preserving federated learning, and regulatory heterogeneity, with attention to evolving U.S. FDA guidance, EU AI Act obligations, and liability frameworks. Integrating epistemic transparency with computational scalability, this review offers a roadmap for translating AI-enhanced spine informatics into safe, equitable, and clinically trustworthy systems—advancing the goals of biomedical informatics by unifying predictive analytics with ethical and patient-centered care.