Lyme disease associated neurological and musculoskeletal symptoms: A systematic review and meta-analysis.

Background And Objective: Lyme disease, caused by , presents major health challenges worldwide, leading to serious neurological and musculoskeletal issues that impact patients' lives and healthcare systems. This systematic review and meta-analysis aim to determine the prevalence and link between Lyme disease and these complications, aiming to enhance clinical and public health approaches.

Methods: We systematically searched PubMed, EMBASE, and Web of Science up until April 01, 2024, to find studies reporting the prevalence and severity of neurological and musculoskeletal complications associated with Lyme disease.

Genes and proteins expression profile of 2D vs 3D cancer models: a comparative analysis for better tumor insights.

When juxtaposed with 2D cell culture models, multicellular tumor spheroids demonstrate a capacity to faithfully replicate certain features inherent to solid tumors. These include spatial architecture, physiological responses, the release of soluble mediators, patterns of gene expression, and mechanisms of drug resistance. The morphological and behavioural similarities between 3D-cultured cells and cells within tumor masses highlight the potential of these models in studying cancer biology and drug responses.

Characteristics of children and adolescents with multidrug-resistant and rifampicin-resistant tuberculosis and their association with treatment outcomes: a systematic review and individual participant data meta-analysis.

Background: There are few data on the treatment of children and adolescents with multidrug-resistant (MDR) or rifampicin-resistant (RR) tuberculosis, especially with more recently available drugs and regimens. We aimed to describe the clinical and treatment characteristics and their associations with treatment outcomes in this susceptible population.

Methods: We conducted a systematic review and individual participant data meta-analysis.

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2.