Medical review (Berlin, Germany)最新文献

Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease with tremendous social and economic burden. Therefore, early and accurate diagnosis is imperative for effective treatment or prevention of the disease. Cerebrospinal fluid and blood biomarkers emerge as favorable diagnostic tools due to their relative accessibility and potential for widespread clinical use. This review focuses on the AT(N) biomarker system, which includes biomarkers reflecting AD core pathologies, amyloid deposition, and pathological tau, as well as neurodegeneration. Novel biomarkers associated with inflammation/immunity, synaptic dysfunction, vascular pathology, and α-synucleinopathy, which might contribute to either the pathogenesis or the clinical progression of AD, have also been discussed. Other emerging candidates including non-coding RNAs, metabolites, and extracellular vesicle-based markers have also enriched the biofluid biomarker landscape for AD. Moreover, the review discusses the current challenges of biofluid biomarkers in AD diagnosis and offers insights into the prospective future development.

Mechanical forces control a multitude of biological responses in various cells and tissues. The periodontal ligament, located between the tooth's root and alveolar bone, is a major tissue compartment that is incessantly subjected to such mechanical stimulation through either normal or abnormal oral functionality. It is now known that mechanical stimulation activates periodontal ligament stem cells (PDLSCs) to modulate periodontal immunity and regulate inflammation - a basic feature of periodontal disease that affects virtually every human during their lifetime. For instance, shear stress induces the expression of immunomodulatory-related gene, indoleamine 2,3-dioxygenase (IDO). IDO cleaves l-tryptophan, resulting in increased l-kynurenine levels that, in turn, further promote regulatory T-cell differentiation and inhibit T cell proliferation. These and other related data reinforce the notion that mechanical stimulation plays a crucial role in controlling inflammation and immunomodulation of periodontal tissues. Further investigations, however, are warranted to evaluate the immunomodulatory features of PDLSCs so as to understand the pathological basis of periodontal disease and translate these into clinical interventions.

Stroke is a prevalent, severe, and disabling health-care issue on a global scale, inevitably leading to motor and cognitive deficits. It has become one of the most significant challenges in China, resulting in substantial social and economic burdens. In addition to the medication and surgical interventions during the acute phase, rehabilitation treatment plays a crucial role in stroke care. Robotic technology takes distinct advantages over traditional physical therapy, occupational therapy, and speech therapy, and is increasingly gaining popularity in post-stroke rehabilitation. The use of rehabilitation robots not only alleviates the workload of healthcare professionals but also enhances the prognosis for specific stroke patients. This review presents a concise overview of the application of therapeutic robots in post-stroke rehabilitation, with particular emphasis on the recovery of motor and cognitive function.

Cardiovascular research has heavily relied on studies using patient samples and animal models. However, patient studies often miss the data from the crucial early stage of cardiovascular diseases, as obtaining primary tissues at this stage is impracticable. Transgenic animal models can offer some insights into disease mechanisms, although they usually do not fully recapitulate the phenotype of cardiovascular diseases and their progression. In recent years, a promising breakthrough has emerged in the form of in vitro three-dimensional (3D) cardiovascular models utilizing human pluripotent stem cells. These innovative models recreate the intricate 3D structure of the human heart and vessels within a controlled environment. This advancement is pivotal as it addresses the existing gaps in cardiovascular research, allowing scientists to study different stages of cardiovascular diseases and specific drug responses using human-origin models. In this review, we first outline various approaches employed to generate these models. We then comprehensively discuss their applications in studying cardiovascular diseases by providing insights into molecular and cellular changes associated with cardiovascular conditions. Moreover, we highlight the potential of these 3D models serving as a platform for drug testing to assess drug efficacy and safety. Despite their immense potential, challenges persist, particularly in maintaining the complex structure of 3D heart and vessel models and ensuring their function is comparable to real organs. However, overcoming these challenges could revolutionize cardiovascular research. It has the potential to offer comprehensive mechanistic insights into human-specific disease processes, ultimately expediting the development of personalized therapies.

Cancer nanomedicine is defined as the application of nanotechnology and nanomaterials for the formulation of cancer therapeutics that can overcome the impediments and restrictions of traditional chemotherapeutics. Multidrug resistance (MDR) in cancer cells can be defined as a decrease or abrogation in the efficacy of anticancer drugs that have different molecular structures and mechanisms of action and is one of the primary causes of therapeutic failure. There have been successes in the development of cancer nanomedicine to overcome MDR; however, relatively few of these formulations have been approved by the United States Food and Drug Administration for the treatment of cancer. This is primarily due to the paucity of knowledge about nanotechnology and the fundamental biology of cancer cells. Here, we discuss the advances, types of nanomedicines, and the challenges regarding the translation of in vitro to in vivo results and their relevance to effective therapies.

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.