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Following a humiliating defeat in the Crimean War, the Russian Empire found herself exposed due to major deficiencies in her infrastructure. To gain from European scientific, technical and educational advancements, the Russian Government began to permit studies abroad and relaxed censorship, which brought a new flood of literature into the country. These measures enormously facilitated the growth of Russian science, medicine and education in the late nineteenth century, taking the Empire into a fascinating era of laboratory research, a new cultural and intellectual tradition. The Imperial Laboratory tells the story of the lives and studies of the leading Russian and German clinician-experimenters who played critical roles in the integration of physics and chemistry into physiology and clinical medicine. A principal theme is the major transformations undergone in military medicine and education. Using a wide range of Russian and German primary sources, this book offers a unique English-language insight into Russian physiology and medicine that will be of interest to both historians and doctors, as well as anyone interested in Russian science and culture.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65–75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice. A deep learning model trained on real-world digital pathology data achieves clinical performance in cancer diagnosis.

\"A literary courtroom thriller about a mother accused of murdering her eight-year-old autistic son\"-- Provided by publisher.

Alzheimer's disease and related dementias (ADRD) are among the top contributors to disability and mortality in later life. As with many chronic conditions, aging is the single most influential factor in the development of ADRD. Even among older adults who remain free of dementia throughout their lives, cognitive decline and neurodegenerative changes are appreciable with advancing age, suggesting shared pathophysiological mechanisms. In this Review, we provide an overview of changes in cognition, brain morphology, and neuropathological protein accumulation across the lifespan in humans, with complementary and mechanistic evidence from animal models. Next, we highlight selected aging processes that are differentially regulated in neurodegenerative disease, including aberrant autophagy, mitochondrial dysfunction, cellular senescence, epigenetic changes, cerebrovascular dysfunction, inflammation, and lipid dysregulation. We summarize research across clinical and translational studies to link biological aging processes to underlying ADRD pathogenesis. Targeting fundamental processes underlying biological aging may represent a yet relatively unexplored avenue to attenuate both age-related cognitive decline and ADRD. Collaboration across the fields of geroscience and neuroscience, coupled with the development of new translational animal models that more closely align with human disease processes, is necessary to advance novel therapeutic discovery in this realm.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for coronavirus 2019 (COVID-19) pneumonia. Little is known about the kinetics, tissue distribution, cross-reactivity, and neutralization antibody response in patients with COVID-19. Two groups of patients with RT-PCR-confirmed COVID-19 were enrolled in this study: 12 severely ill patients in intensive care units who needed mechanical ventilation and 11 mildly ill patients in isolation wards. Serial clinical samples were collected for laboratory detection. Results showed that most of the severely ill patients had viral shedding in a variety of tissues for 20-40 days after onset of disease (8/12, 66.7%), while the majority of mildly ill patients had viral shedding restricted to the respiratory tract and had no detectable virus RNA 10 days after onset (9/11, 81.8%). Mildly ill patients showed significantly lower IgM response compared with that of the severe group. IgG responses were detected in most patients in both the severe and mild groups at 9 days after onset, and remained at a high level throughout the study. Antibodies cross-reactive to SARS-CoV and SARS-CoV-2 were detected in patients with COVID-19 but not in patients with MERS. High levels of neutralizing antibodies were induced after about 10 days after onset in both severely and mildly ill patients which were higher in the severe group. SARS-CoV-2 pseudotype neutralization test and focus reduction neutralization test with authentic virus showed consistent results. Sera from patients with COVID-19 inhibited SARS-CoV-2 entry. Sera from convalescent patients with SARS or Middle East respiratory syndrome (MERS) did not. Anti-SARS-CoV-2 S and N IgG levels exhibited a moderate correlation with neutralization titers in patients' plasma. This study improves our understanding of immune response in humans after SARS-CoV-2 infection.

Beyond their substantial protection of individual vaccinees, coronavirus disease 2019 (COVID-19) vaccines might reduce viral load in breakthrough infection and thereby further suppress onward transmission. In this analysis of a real-world dataset of positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results after inoculation with the BNT162b2 messenger RNA vaccine, we found that the viral load was substantially reduced for infections occurring 12–37 d after the first dose of vaccine. These reduced viral loads hint at a potentially lower infectiousness, further contributing to vaccine effect on virus spread. Breakthrough infections of SARS-CoV-2 occurring 12 or more days after the first dose of the BNT162b2 mRNA vaccine were associated with lower viral loads than those found in unvaccinated individuals, suggesting that the vaccine might reduce infectiousness.

The molecular mechanisms of cellular insulin action have been the focus of much investigation since the discovery of the hormone 100 years ago. Insulin action is impaired in metabolic syndrome, a condition known as insulin resistance. The actions of the hormone are initiated by binding to its receptor on the surface of target cells. The receptor is an α2β2 heterodimer that binds to insulin with high affinity, resulting in the activation of its tyrosine kinase activity. Once activated, the receptor can phosphorylate a number of intracellular substrates that initiate discrete signaling pathways. The tyrosine phosphorylation of some substrates activates phosphatidylinositol-3-kinase (PI3K), which produces polyphosphoinositides that interact with protein kinases, leading to activation of the kinase Akt. Phosphorylation of Shc leads to activation of the Ras/MAP kinase pathway. Phosphorylation of SH2B2 and of Cbl initiates activation of G proteins such as TC10. Activation of Akt and other protein kinases produces phosphorylation of a variety of substrates, including transcription factors, GTPase-activating proteins, and other kinases that control key metabolic events. Among the cellular processes controlled by insulin are vesicle trafficking, activities of metabolic enzymes, transcriptional factors, and degradation of insulin itself. Together these complex processes are coordinated to ensure glucose homeostasis.

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Plasma phosphorylated tau181 (P-tau181) might be increased in Alzheimer’s disease (AD), but its usefulness for differential diagnosis and prognosis is unclear. We studied plasma P-tau181 in three cohorts, with a total of 589 individuals, including cognitively unimpaired participants and patients with mild cognitive impairment (MCI), AD dementia and non-AD neurodegenerative diseases. Plasma P-tau181 was increased in preclinical AD and further increased at the MCI and dementia stages. It correlated with CSF P-tau181 and predicted positive Tau positron emission tomography (PET) scans (area under the curve (AUC) = 0.87–0.91 for different brain regions). Plasma P-tau181 differentiated AD dementia from non-AD neurodegenerative diseases with an accuracy similar to that of Tau PET and CSF P-tau181 (AUC = 0.94–0.98), and detected AD neuropathology in an autopsy-confirmed cohort. High plasma P-tau181 was associated with subsequent development of AD dementia in cognitively unimpaired and MCI subjects. In conclusion, plasma P-tau181 is a noninvasive diagnostic and prognostic biomarker of AD, which may be useful in clinical practice and trials. Plasma P-tau18 level increased with progression of Alzheimer’s disease (AD) and differentiated AD dementia from other neurodegenerative diseases, supporting its further development as a blood-based biomarker for AD.

The discovery and generation of effective therapeutics to combat disease lies at the heart of biomedical research. Preclinical studies form the foundation of potential disease treatments, guiding their journey from scientific discovery to impactful patient outcomes. However, over the past two decades, preclinical research has been frequently plagued by the failure to replicate consistent results, costing an estimated $28 billion USD per year. Potential therapeutics from preclinical studies entering phase I trials only had a 10.4% approval rate between 2003 and 2014 and an even lower 6% to 7% rate between 2011 and 2017. The disappointing reality of promising preclinical findings that fail to translate into effective therapies has raised serious concerns within the scientific community. The cause of this failure is potentially elucidated in a 2015 retrospective analysis of four large biotech companies that showed the most common causes of termination in phase I and II clinical trials since 2003 are the lack of efficacy (60% of all trials) and toxicity (30%). Given these insights and the emergence of advanced technologies that enable large-cohort, in vitro human testing, a pressing need to reassess our approaches to studying human diseases exists. Such changes are vital to facilitating the development of lifesaving therapeutics that can extend both health span and life span by more efficiently.